Current Medical Imaging - Current Issue

The Myocardial Salvage Index (MSI) is a valuable indicator in ST-segment Elevation Myocardial Infarction (STEMI) treated with Percutaneous Coronary Intervention (PCI), yet challenges exist in its acquisition. This study aims to calculate MSI using Coronary Angiography (CAG) and myocardial perfusion imaging, and further investigate its correlation with long-term cardiac function.

In 203 STEMI, the myocardium at risk was measured through CAG using the Bypass Angioplasty Revascularization Investigation Myocardial Jeopardy Index (BARI) score. The infarcted myocardium was measured by the Total Perfusion Deficit (TPD) obtained in Myocardial Perfusion Imaging (MPI) after PCI. MSI was computed as (BARI score–TPD)/BARI score. Long-term cardiac function was assessed via echocardiography.

The MSI is notably associated with the long-term cardiac function [EF: Beta = 16 (13, 20), P < 0.00; LVD: Beta = -7.3 (-9.3, -5.3), P < 0.001]. TIMI flow grades 2-3 demonstrate a superior MSI compared to grades 0-1 [0.78 (0.32) vs. 0.61 (0.38), P = 0.002]. TIMI flow grades have an impact on MSI [Beta = 0.08 (0.04, 0.13), P < 0.001]. Compared to patients with a Killip grade of < 2, those with a grade ≥ 2 exhibit a lower MSI [0.69 (0.35) vs. 0.48 (0.42), p = 0.005]. The Killip classification has an impact on MSI [Beta = -0.12(-0.19, -0.04), P = 0.003].

The study indicates the pivotal role of MSI in predicting long-term cardiac function in STEMI, compares the advantages and limitations of SPECT, CMR, and hybrid SPECT/CAG methods, analyzes the impact of residual blood flow and acute heart failure on MSI, and highlights current technological challenges and future research directions.

CAG combining MPI after PCI can be used to obtain MSI. MSI is linked to long-term cardiac function. The amount of antegrade flow before PCI and the initial cardiac function upon admission significantly influence MSI.

This study aimed to assess the effectiveness and precision of a deep learning-based model in forecasting the early response of HCC patients to TACE.

A comprehensive review of HCC-TACE data involving 111 patients with HCC was carried out, encompassing both pre-TACE MR images (captured before the first TACE) and post-TACE imaging (acquired between 30 and 60 days following TACE). Based on the mRECIST criteria, patients were divided into two cohorts: a training dataset (91 subjects, 645 images) and a test dataset (20 subjects, 155 images). A deep learning-based model utilizing LeNet architecture with an attention mechanism was developed, targeting the prediction of HCC patients' response to TACE. The robustness and accuracy of the model were examined via ROC curves and confusion matrices.

Post-TACE treatment, 56 patients (50.5%) manifested an objective response (CR+PR), whereas 55 patients (49.5%) exhibited no response (SD+PD). Concerning the model's predictive ability for TACE response, the AUC was found to be 0.760 in the training dataset and 0.729 in the test dataset. The model's prediction accuracy was further corroborated by the confusion matrix, revealing an average accuracy of 70.7% in the training dataset and 72.3% in the test dataset.

Implementing a deep learning-based model using MRI data is potent for forecasting HCC patients’ response to TACE treatment. The novel LeNet model with the attention mechanism conceived in this study contributes valuable insights that can guide the formulation of effective treatment strategies.

Unilateral upper ureteral obstruction is one of the most common causes of acute kidney function impairment. Grading perirenal changes secondary to acute unilateral upper urinary tract obstruction (AUUTO) with multidetector spiral computed tomography (MDCT) and exploring its association with kidney function are useful for diagnosing and assessing damage to the ipsilateral kidney. However, the correlation between renal function impairment and the severity of perinephric changes secondary to AUUTO has not been reported.

This study aimed to investigate the association of perirenal changes secondary to AUUTO with hydronephrosis and serum creatinine levels, as well as white blood cell counts.

This retrospective study included 376 patients with acute unilateral upper ureteral obstruction, all of whom were subjected to MDCT scans. They were classified into four grades (0-III) according to their perirenal changes on MDCT images. The severity of hydronephrosis was classified into four grades based on MDCT scans. The serum creatinine level and leukocyte counts were compared among the MDCT grade groups, and logistic regression analysis was conducted.

Among 376 patients, 77 (20.5%), 103 (27.4%), 140 (37.2%), and 56 (14.9%) cases were graded into MDCT 0, I, II, and III, respectively. The proportions of patients who had normal kidneys in MDCT 0, I, II, and III were 20 (26.0%), 10 (9.7%), 11(7.9%), and 3 (5.4%), respectively. The proportions of patients who had mild hydronephrosis in MDCT 0, I, II, and III were 55 (71.4%), 83 (80.6%), 118 (84.2%), and 46 (82.1%), respectively. The proportions of patients who had moderate and severe hydronephrosis in MDCT 0, I, II, and III were 2(2.6%), 10 (9.7%), 11 (7.9%), 7 (12.5%), respectively. Serum creatinine levels and white blood cell counts were significantly different among the MDCT grade groups (P < 0.001). Univariate and multivariate logistic regression analyses indicated that the serum creatinine level and white blood cell counts were positively associated with the MDCT grades (P < 0.001).

Perinephric changes secondary to AUUTO on MDCT images were associated with the degree of obstruction. The severity of perinephric changes can reflect the functional impairment in the ipsilateral kidney. The MDCT grades may aid clinicians in assessing renal function impairment early in patients with AUUTO, which may help patients receive early intervention and avoid the potential risk of infection and deterioration of renal function.

The incidence of Alzheimer’s disease is rising with the increasing elderly population worldwide. While no cure exists, early diagnosis can significantly slow disease progression. Computer-aided diagnostic systems are becoming critical tools for assisting in the early detection of Alzheimer’s disease. In this systematic review, we aim to evaluate recent advancements in computer-aided decision support systems for Alzheimer’s disease diagnosis, focusing on data modalities, machine learning methods, and performance metrics.

We conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies published between 2021 and 2024 were retrieved from PubMed, IEEEXplore and Web of Science, using search terms related to Alzheimer’s disease classification, neuroimaging, machine learning, and diagnostic performance. A total of 39 studies met the inclusion criteria, focusing on the use of Magnetic Resonance Imaging, Positron Emission Tomography, and biomarkers for Alzheimer’s disease classification using machine learning models.

Multimodal approaches, combining Magnetic Resonance Imaging with Positron Emission Tomography and Cognitive assessments, outperformed single-modality studies in diagnostic accuracy reliability. Convolutional Neural Networks were the most commonly used machine learning models, followed by hybrid models and Random Forest. The highest accuracy reported for binary classification was 100%, while multi-class classification achieved up to 99.98%. Techniques like Synthetic Minority Over-sampling Technique and data augmentation were frequently employed to handle data imbalance, improving model generalizability.

Our review highlights the advantages of using multimodal data in computer-aided decision support systems for more accurate Alzheimer’s disease diagnosis. However, we also identified several limitations, including data imbalance, small sample sizes, and the lack of external validation in most studies. Future research should utilize larger, more diverse datasets, include longitudinal data, and validate models in real-world clinical trials. Additionally, explainability is needed in machine learning models to ensure they are interpretable and reliable in clinical settings.

While computer-aided decision support systems show significant promise in improving the early diagnosis of Alzheimer’s disease, further work is needed to enhance their robustness, generalizability, and clinical applicability. By addressing these challenges, computer-aided decision support systems could play a key role in the early detection of Alzheimer’s disease and potentially reduce health care costs.

Adrenal incidentalomas have increased over the past years. Although there are a lot of studies related to the frequency of adrenal masses and comorbidities, whether patients with functional or nonfunctional adrenal masses have higher risk is still a controversial issue.

A total of 464 patients (female/male: 309/155) with adrenal incidentalomas were evaluated and followed up for 8 years. The patients were divided into 5 subgroups, including Autonomous Cortisol Secretion (ACS), Cushing Syndrome (CS), Pheochromocytoma (Pheo), Non-functional Adrenal Incidentalomas (NFAI), and Primary Aldosteronism (PA).

While 336 (72.4%) of the patients had NFAI, the others suffered from ACS (10.8%), CS (4.3%), Pheo (4.1%), and PA (8.4%), respectively. When comparing biochemical and demographical data, BMI (p=0.77), Hba1c (p=0.495), FPG (p=0.28), LDL (p=0.66), and HDL (p=0.521) were similar among the patients with functional and nonfunctional adrenal masses. The most common comorbidities were hypertension (n=259, 55.8%), diabetes mellitus (n=158, 34.1%), and dyslipidemia (33.4%), respectively. While 84 (32.4%) patients with hypertension had functional adrenal masses, the others (n=175, 67.6%) had non-functional adrenal incidentalomas. In subgroup analyses, hypertension was more common in patients with PA (87.2% vs. 72%, p=0.001) and ACS. In multivariable regression analyses, hypertension (p<0.001), cortisol (p=0.003), and aldosterone (p=0.04) levels were significantly correlated with functionality.

Hypertension was the most common comorbidity in patients with adrenal adenomas, especially in functional adrenal adenomas related to serum cortisol and aldosterone levels.

This study aimed to demonstrate the relationship between hemodynamic changes in head blood flow and headache during the premenstrual period in patients experiencing premenstrual syndrome.

Thirty-two female patients experiencing premenstrual headaches were prospectively examined using carotid and vertebral artery duplex ultrasonography during headache episodes in the premenstrual periods and headache-free periods across two consecutive menstrual cycles. The diameters and areas of both the carotid and vertebral arteries, along with systolic and end-diastolic velocities, pulsatility and resistivity indices, and volumetric flow rates, were measured using grayscale imaging. Total head blood flow was determined as the sum of bilateral common carotid artery and vertebral artery flow volumes. Measurements were compared between participants’ premenstrual and menstrual periods.

A statistically significant difference in the diameter of the left external carotid artery was observed between periods with and without headache during the two consecutive menstrual cycles assessed (p = 0.030). Left external carotid artery (p = 0.019), total external carotid artery (p = 0.028), and total head blood volumes (p = 0.030) were significantly higher when headache was present during the premenstrual period than when headache was absent.

Towards the end of the luteal phase, the total head blood flow and external carotid artery flow were high due to a decrease in peripheral resistance caused by the decline in progesterone and hormonal fluctuations during this period.

Increased flow volume in the external carotid arteries and total head blood flow may be a contributing factor to premenstrual headaches.

Post-hepatic resection liver failure (PHLF) remains one of the most serious complications after hepatic resection, with an overall morbidity rate as high as 32% and an approximate 5% mortality. Previous studies demonstrate the potential of shear wave elastography (SWE) to predict PHLF. This meta-analysis aimed to evaluate the diagnostic accuracy of SWE in identifying liver failure after hepatectomy.

A comprehensive search was performed across PubMed/Medline, Embase, and Web of Science to identify studies assessing the diagnostic accuracy of SWE for predicting PHLF. The combined sensitivity, specificity, and the hierarchical summary receiver operating characteristic curve (HSROC) for SWE in detecting PHLF in liver resection patients. The Quality Assessment of Diagnostic Accuracy Studies tool was used to evaluate the quality of the studies included in the analysis. Heterogeneity was explored through sensitivity analysis, univariable meta-regression and subgroup analysis.

This meta-analysis included a total of 13 studies involving 2985 patients. For quantitative analysis. The combined sensitivities and specificities of SWE for detecting post-hepatectomy liver failure were 0.81 and 0.68, respectively. The HSROC value for SWE was 0.82. Significant heterogeneity (I² = 80.22) was observed in pooled specificity. Meta-regression and subgroup analyses suggest that differences in the proportion of patients with HCC and in the diagnostic criteria for PHLF may account for the observed heterogeneity. For the qualitative analysis, six predictive models based on SWE were included, and their AUCs were 0.80-0.915.

Both SWE alone and SWE-based prediction models appear to accurately detect PHLF and help to categorize patients into high- and low-risk groups. It may also assist surgeons in identifying the best candidates for liver resection and enhancing perioperative management.

An accurate and reliable prognostic model for Nasal Extranodal Natural Killer/T-cell Lymphoma (ENKTL) is critical for survival outcomes and personalized therapy. Currently, there is no Magnetic Resonance Imaging (MRI)- based radiomics analysis in the prognosis model for nasal ENKTL patients.

We aim to explore the value of MRI-based radiomics signature in the prognosis of patients with nasal ENKTL.

A total of 159 nasal ENKTL patients were enrolled and divided into a training cohort (n=81) and a validation cohort (n=78) randomly. Radiomics features from pretreatment MRI examination were extracted, respectively. Then two-sample t-test and Least Absolute Shrinkage and Selection Operator (LASSO) regression were used to select the radiomics signatures and establish the Rad-score. Univariate and multivariate Cox proportional hazards regression models were used to investigate the prognostic value of baseline clinical features and establish clinical models. A radiomics nomogram based on the Rad-score and clinical features was constructed to predict Overall Survival (OS). The predictive efficacy of the three models was evaluated in two cohorts.

A total of 1,345 features were extracted from T2-weighted (T2-w) and Contrast-enhanced T1-weighted (CET1-w) images, respectively, and 1,037 features with Intraclass Correlation Coefficient (ICC) >0.7 were selected. Ultimately, 20 features were chosen to construct the Rad-score, which showed a significant association with OS. The C-indexes of the Rad-score were 0.733 (95% confidence interval (CI): 0.645 to 0.816) and 0.824 (95% CI: 0.766-0.882), respectively, in training and validation cohorts. Through the univariate and multivariate analyses, three independent risk factors for OS were identified: Rad-score (HR: 10.962, 95% CI: 3.417-35.167, P <0.001), lactate dehydrogenase (LDH) level (HR: 3.009, 95% CI: 1.128-8.510, P = 0.028) and distant lymph-node involvement (HR: 2.966, 95% CI: 1.015-8.664, P = 0.047). Patients with distal lymph node involvement and LDH level before treatment were included in the clinical model, which achieved a C-index of 0.707 (95% CI: 0.600–0.814) in the training cohort and 0.635 (95% CI: 0.527–0.743) in the validation cohort.

We integrated the Rad-score and clinical variables to establish a radiomics nomogram, which exhibited a satisfactory prediction performance with the C-indexes of 0.849(95% CI: 0.781-0.917) and 0.931 (95% CI: 0.882-0.980) in two cohorts, respectively. The radiomics nomogram was more accurate in predicting OS in patients with nasal ENKTL than the other two models. Based on the radiomics nomogram, patients were categorized into low-risk and high-risk groups in two cohorts (P all < 0.05). The high-risk group defined by this nomogram exhibited a shorter OS.

The Rad-score was significantly correlated with OS for nasal ENKTL patients. Moreover, the MRI-based radiomics nomogram could be used for risk stratification and might guide individual treatment decisions.

Magnetic resonance imaging can differentiate Alzheimer-type dementia from dementia with Lewy bodies using voxel-based specific regional analysis systems for Alzheimer’s disease and arterial spin labeling, which reveal reduced blood flow from the posterior cingulate gyrus to the precuneus in Alzheimer-type dementia. However, the relationship between voxel-based specific regional analysis system scores and arterial spin labeling remains unclear. To investigate the relationship between brain atrophy scores and arterial spin labeling values in the posterior cingulate precuneus.

Participants with suspected dementia who underwent brain magnetic resonance imaging using a voxel-based regional analysis system were included. They were classified as follows: Group 1 (suspected Alzheimer-type dementia) had atrophy ≥2 in the volume of interest; Group 2 (suspected dementia with Lewy body) had atrophy <2 in the volume of interest and ≥0.2 in the gray and white matter of the dorsal brainstem; and Group 3 included those not meeting these criteria. Correlation values among atrophy within the volume of interest, percentage of atrophic areas, atrophy ratio, percentage of total brain atrophy, age, and maximum arterial spin labeling value at the posterior cingulate precuneus were evaluated.

Groups 1, 2, and 3 comprised 179, 143, and 197 patients, respectively. Arterial spin labeling values at the posterior cingulate precuneus were 77.0±24.4–77.3±25.2, 78.3±81.3–80.2±23.6, and 80.2±22.3–80.4±22.8 mL/min/100 g, respectively. Group 1 had a correlation coefficient between total brain atrophy and arterial spin labeling of –0.189 to–0.214 (P<0.01). Group 2 had a correlation coefficient between total brain atrophy and arterial spin labeling of –0.215 to –0.223 (P<0.01). Group 3 showed no significant correlations. No statistically significant difference was observed in ASL 1 and 2 values between the Alzheimer-type dementia and other groups (ASL 1: 74.5 mL/min/100 g vs. 78.8 mL/min/100 g, P=0.08; ASL 2: 74.8 mL/min/100 g vs. 79.2 mL/min/100 g, P=0.101). No statistically significant difference was observed in ASL 1 and 2 values between the Alzheimer-type dementia and DLB groups (ASL 1: 74.5 mL/min/100 g vs. 69.3. mL/min/100 g, P=0.093; ASL 2: 74.8 mL/min/100 g vs. 78.9 mL/min/100 g, P=0.258).

Reduced blood flow in the posterior cingulate gyrus and precuneus shows only a weak correlation with brain atrophy in both Alzheimer-type dementia and dementia with Lewy bodies. Therefore, it is not a reliable marker for differentiating Alzheimer-type dementia from dementia with Lewy bodies and other groups.

It is necessary to avoid using cerebral blood flow assessment alone when diagnosing dementia.

The synchronous occurrence of primary pure squamous cell carcinoma (SCC) and invasive ductal carcinoma (IDC) of the breast is rare. Accurate identification of synchronous primary malignancies is crucial because their prognosis and treatment differ significantly from recurrent diseases. Herein, we present an unusual case highlighting the synchronous development of primary SCC and IDC in the ipsilateral breast.

A 48-year-old woman presented with a palpable mass in her right breast. Preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) demonstrated an irregularly shaped mass with internal rim enhancement. Surgical resection confirmed IDC of nuclear grade 3 with a high proliferation index (Ki-67: 70%), and the patient underwent adjuvant chemotherapy without radiation. Five months postoperation, a chest computed tomography (CT) revealed a new round-shaped lesion with rim enhancement and relatively circumscribed margins near the previous operation site. Breast ultrasound additionally identified a complex cystic and solid mass with an echogenic rind and increased vascularity. Following total resection, a pure squamous cell carcinoma with prominent keratinization was confirmed.

Accurate and early diagnosis of synchronous multiple primary malignancies from recurrence of the primary tumor is critical for improving prognosis by establishing an appropriate treatment and follow-up plan. Recognizing complex cystic and solid masses with relatively circumscribed margins on radiological imaging can assist clinicians in identifying and managing rare cases where IDC and SCC coexist or appear sequentially within a short period.

Dynamic changes in adenomyotic lesions in animal models have been difficult to observe and evaluate in vivo on a regular basis. Therefore, this study aims to investigate the feasibility of establishing a rat model of adenomyosis through autologous endometrial implantation and to assess the value of magnetic resonance imaging (MRI) for noninvasive evaluation of the model.

Forty rats were randomly divided into two groups (20 rats in the control group, 20 rats in the model group). A rat adenomyosis model was constructed through autologous endometrial implantation. Three months after the modeling surgery, the rats underwent MRI examination, including T2-weighted axial imaging and T1-weighted axial imaging. The thickness of the uterine myometrium and junctional zone was measured. Following the MRI, the rat uterus was sliced for hematoxylin-eosin (HE) staining.

In the model group, lesions of adenomyosis were successfully established in all surviving rats. The myometrium of the rat uterus showed uneven thickening accompanied by scattered spotty T2 hypersignal. The junctional zone appeared as a low-signal band between the endometrium with high signal and the myometrium. The average thicknesses of both the myometrium and the junctional zone were significantly greater in the model group compared to the control group, with the differences reaching statistical significance.

Ectopic endometrium can lead to hyperplasia of the peripheral muscle cells in the myometrium, which is manifested on T2-weighted images as localized thickening and hypo-intensity of the myometrium interspersed with punctiform hyperintensity. Histologically, regions of low signal intensity refer to hyperplasia of smooth muscle, while bright foci on T2-weighted images correspond to ectopic endometrial tissue and cystic dilation of glands. This study proved the noninvasive evaluation of a rat adenomyosis model and described the junctional zone in rats using MRI techniques. Histological examination using HE staining confirmed a higher nuclear-to-cytoplasmic ratio and a more compact cell arrangement in the junctional zone region of rats compared to the outer myometrium, which could explain its hypointensity.

MRI is a valuable method for evaluating the rat adenomyosis model non-invasively. Furthermore, the successful visualization of the junctional zone in the rat uterus using MRI may have potential applications in further evaluating the progression of adenomyosis.

The association between ABO and Rh blood groups and diseases is an intriguing topic that continues to be studied, but their potential influence on mandibular asymmetry has not been explored. Temporomandibular joint (TMJ) disorders are multifactorial, and subtle anatomical variations may be linked to genetic predispositions. Our study aims to investigate the relationship between ABO and Rh blood groups and mandibular condylar and ramal asymmetries in a healthy adult Turkish population.

This study included 149 adult patients (67 males, 82 females) who had no history of systemic diseases, craniofacial deformities, or TMJ-related complaints. Asymmetry was assessed in panoramic radiographic images using a formula developed in a previous study. The chi-square and Kruskal-Wallis tests were used to analyze differences among ABO groups while the Mann-Whitney U test was used for Rh groups.

No significant difference was found in terms of gender distribution, Rh factor or age between ABO or Rh groups. However, there was a significant difference in condylar asymmetry index (CAI) between ABO groups (p 0.05). Pairwise comparisons revealed that individuals with AB blood type exhibited significantly higher CAI values compared to those with B blood type. No statistically significant differences in asymmetry indices were observed between Rh groups.

The findings of our study indicate the existence of a significant relationship between blood groups and asymmetry indices in a healthy population. The significant differences in condylar asymmetry between AB and B blood groups indicate a possible association between blood type and mandibular anatomical variations, rather than a causal relationship. Further studies are needed to confirm these findings and to understand the underlying mechanisms of the relationship between blood groups and mandibular asymmetry.

The evaluation of the left atrial (LA) by imaging is becoming increasingly essential due to its significant role in numerous diseases. This study aimed to analyze and summarize research on LA imaging in the past 20 years through bibliometric analysis and offer insights into future research prospects.

The Web of Science (WOS) core collection database was retrieved for literature in LA imaging research from 2004 to 2023. Subsequently, the literature was processed and visualized by the VOSviewer and CiteSpace. VOSviewer was used to create cooperation networks for countries/regions and institutions. CiteSpace was used to analyze burst keywords in citation analysis.

A total of 3664 articles published in this field between January 2004 and December 2023 were analyzed. The number of published articles is increasing year by year. The USA contributed the most articles (1072). Hugh Calkins (44) was the most productive author with the highest publications.

Over the past 20 years, research on LA imaging has grown rapidly. The results of the present study provide insights into the field’s status and indicate the research hotspots. In recent years, research on left atrial appendage occlusion (LAAO) and LA strain has been notably focused, which is expected to remain a prominent topic in future research.

The sphenoid sinus is an important access point for trans-sphenoidal surgeries, but variations in its pneumatization may complicate surgical safety. Deep learning can be used to identify these anatomical variations.

We developed a convolutional neural network (CNN) model for the automated prediction of sphenoid sinus pneumatization patterns in computed tomography (CT) scans. This model was tested on mid-sagittal CT images. Two radiologists labeled all CT images into four pneumatization patterns: Conchal (type I), presellar (type II), sellar (type III), and postsellar (type IV). We then augmented the training set to address the limited size and imbalanced nature of the data.

The initial dataset included 249 CT images, divided into training (n = 174) and test (n = 75) datasets. The training dataset was augmented to 378 images. Following augmentation, the overall diagnostic accuracy of the model improved from 76.71% to 84%, with an area under the curve (AUC) of 0.84, indicating very good diagnostic performance. Subgroup analysis showed excellent results for type IV, with the highest AUC of 0.93, perfect sensitivity (100%), and an F1-score of 0.94. The model also performed robustly for type I, achieving an accuracy of 97.33% and high specificity (99%). These metrics highlight the model's potential for reliable clinical application.

The proposed CNN model demonstrates very good diagnostic accuracy in identifying various sphenoid sinus pneumatization patterns, particularly excelling in type IV, which is crucial for endoscopic sinus surgery due to its higher risk of surgical complications. By assisting radiologists and surgeons, this model enhances the safety of transsphenoidal surgery, highlighting its value, novelty, and applicability in clinical settings.

Rheumatoid arthritis (RA) is a systemic autoimmune disorder primarily characterized by joint degradation, with consequential cardiovascular ramifications significantly impacting patient mortality rates.

We systematically searched for full-text English-language journal articles from 1973 to 2025 in the PubMed and Web of Science databases. Utilizing keywords such as “Rheumatoid Arthritis,” “Autoimmune Diseases,” “Pathophysiology,” “Heart,” “Cardiac,” and “Echocardiography” to narrow the search results. Articles related to the evaluation of heart diseases in rheumatoid arthritis by echocardiography were included, while those with insufficient data or low data quality were excluded. Study quality was assessed using the CASP Quantitative Checklist (2018 version), and data were synthesized through thematic content analysis.

We included 52 studies in this review after the primary analysis. The results show that traditional echocardiography can identify organic changes in the heart and ventricular function impairment of patients with rheumatoid arthritis. New ultrasound techniques, such as speckle tracking and pressure-strain loops, can detect ventricular function impairment earlier than traditional echocardiography.

Echocardiography provides complementary diagnostic information for rheumatoid arthritis cardiac involvement through structural and functional assessment, yet limitations remain. Future work should establish multimodal ultrasound frameworks and develop AI-driven analytical platforms to enhance early detection and precision management.

The continuous progress of ultrasound technology has significantly improved the accuracy of assessing cardiac damage in patients with rheumatoid arthritis, and it has become an essential examination method for screening heart diseases in such patients, providing strong support for early diagnosis.

Unlike its well-established role in lung cancer staging, positron emission tomography /computed tomography (PET/CT)'s role in guiding lung biopsies remains unclear and underutilized, despite its potential to distinguish metabolically active regions from areas of necrosis or fibrosis within lesions.

This study aims to assess the diagnostic efficacy of PET/CT-aided versus conventional CT-guided lung biopsy by comparing the incidences of non-diagnostic results, false results, and complications.

Studies comparing PET/CT-aided and conventional CT-guided lung biopsy were identified through an intensive search of PubMed, Embase, and the Cochrane Library. Data on nondiagnostic results, false results, and complications were extracted. Risk ratios (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model.

Seven studies involving 1,661 procedures were included. PET/CT-aided lung biopsy significantly reduced nondiagnostic results compared to conventional CT-guided biopsy (2.8% vs. 9.1%; pooled RR: 0.38, 95% CI: 0.20–0.70, P = 0.002). False results were also significantly fewer in the PET/CT-aided group (6.5% vs. 17.0%; pooled RR: 0.48, 95% CI: 0.35–0.65, P < 0.001). There was no statistically significant difference in overall complication rates (28.1% vs. 32.5%; pooled RR: 0.92, 95% CI: 0.77–1.10, P = 0.352), while PET/CT-aided biopsy showed a slight tendency toward fewer major complications (0.9% vs. 1.7%; pooled RR: 0.67, 95% CI: 0.30–1.44, P = 0.303).

PET/CT-aided CT-guided lung biopsy offers advantages over conventional CT-guided lung biopsy by significantly reducing nondiagnostic and false results, without significant differences in the risk of complications.

This study evaluated the impact of Low-Intensity Pulsed Ultrasound (LIPUS) on bone radio density in patients undergoing orthodontic treatment with clear aligners, aiming to enhance bone remodeling and improve treatment stability.

This retrospective study included 68 participants divided into two groups: 34 treated with LIPUS and 34 in a control group. Bone radio density was measured using Hounsfield units from CBCT scans before and after treatment. Statistical analyses included Mann-Whitney U tests and paired t-tests.

The average age was 29.85 ± 14.85 years in the control group and 36.29 ± 12.78 years in the LIPUS group. Bone radio density in the upper arch of the LIPUS group significantly increased from 444.6 HU to 751.3 HU (p < 0.001), while the control group showed a slight decrease in the upper arch (657.4 HU to 650.5 HU, p = 0.86). In the lower arch, a similar trend was observed in the LIPUS group, with an increase from 767.7 HU to 823.4 HU (p = 0.17), though not statistically significant. There were no significant differences in post-treatment ABO DI scores between groups, suggesting equivalent effectiveness in achieving orthodontic outcomes.

LIPUS with clear aligners seems promising in enhancing bone radio density, indicating an improved bone remodeling effect. This highlights LIPUS's potential as a beneficial adjunct in orthodontic treatments.

Primary pituitary melanocytoma, an exceedingly rare tumor, may resemble pituitary adenoma with apoplexy owing to its heterogeneous melanin concentration and possible hemorrhagic events. An accurate diagnosis of melanocytoma is, therefore, essential.

We present a case of a 31-year-old female patient who exhibited a progressively worsening headache that commenced one month prior. MRI showed a significantly enlarged sella turcica with a gourd-shaped lesion that had a mixture of short T1 and T2 signals. In conjunction with the MRI findings, CT scans, both non-contrast and contrast-enhanced, revealed a circular, dense region in the sellar area, exhibiting heightened enhancement post-contrast administration. Subsequently, this patient was scheduled for endoscopic transnasal skull base tumor resection and skull base reconstruction. Later, histopathological assessment showed red-S-100 (+), red-melanin A (+), red-KI-67 (+5%), red-melanoma (+), P53 (+), red-P53 (+) and Ki-67 (+) and suggested an intermediate-grade melanocytoma, positioning this lesion between benign and malignant on the spectrum of melanocytic neoplasms.

This case report evaluated the presentation, key imaging findings, and histopathological features that help differentiate primary melanocytoma from other tumors and discussed key management and prognostic considerations following diagnosis.

Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries.

To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis.

By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture.

The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors’ diagnosis results, with an overall error rate of less than 2%.

The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

To address the low efficiency of diagnosing pulmonary nodules using computed tomography (CT) images and the difficulty in obtaining the key signs of malignant pulmonary nodules, a ghost convolution residual network incorporating hybrid normalization (GCHN-net) is proposed.

Firstly, a three-dimensional ghost convolution with a small kernel is embedded in the GCHN-net. Secondly, we designed a hybrid normalized-activation module (TMNAM) that can handle the rich and complex features of lung nodules in both the deep and shallow layers of the network, and incorporating two different normalization methods. This allows the network to comprehensively learn the intricate relationships underlying the intrinsic features of lung nodules and enhances its capacity to classify the properties of unknown nodules. Additionally, to enhance the accuracy and detail of the category activation map, GradCAM++ is integrated into the third layer of the GCHN-net. This integration enables the visualization of specific regions within three-dimensional lung nodules that the model focuses on during its predictions.

The accuracy of the GCHN-net on the Lung Nodule Analysis 16 (LUNA16) dataset was 90.22%, with an F1-score of 88.31% and a G-mean of 90.48%.

Compared with existing methods, the proposed method can greatly improve the classification of pulmonary nodules and can effectively assist doctors in diagnosing patients with pulmonary nodules.

Recently, deep learning (DL) algorithms use Arithmetic Units (AU) in CPU/GPU hardware for processing images/data. AU operates in fixed precision and limits the representation of weights and activations in DL. The problem leads to quantization errors, which reduce accuracy during cancer cell segmentation.

In this study, arithmetic multiplication in convolution layers is replaced with Vedic multiplication in the proposed DnCNN algorithm. Next, Vedic multiplication-based convolution layers in the DnCNN architecture are optimized using POA (Pelican Optimization Algorithm), and the resulting POA-DnCNN is implemented on an FPGA device for breast cancer detection, segmentation, and classification of benign and malignant breast lesions.

In the convolution layer of DnCNN, floating-point operations are performed through the Hybrid-Vedic (HV) multiplier called ‘CUTIN,’ which is the combination of Urdhva Tryambakam and Nikhilam Sutra with the upasutra ‘Anurupyena.’ Larger image sizes increase processor size and gate count.

The proposed HV-FPGA-based breast cancer detection system, employing Vedic multiplication in the convolution layers of DnCNN and hyperparameters optimized by POA, detects stages of breast cancer with an accuracy of 96.3%, precision of 94.54%, specificity of 92.37%, F-score of 93.56%, IoU of 94.78%, and DSC of 95.45%, outperforming existing methods.

The proposed CUTIN multiplier uses a CSA (carry save adder) with simplified sum-carry generation logic (CSCGL), achieving lower area-delay, high speed, and improved precision.

Despite the increasing prevalence of hyperuricemia and gout, there remains a relative paucity of research focused on the use of straightforward clinical and laboratory markers to predict urate crystal formation. The identification of such predictive markers is crucial, as they would greatly enhance the ability of clinicians to make timely and accurate diagnoses, leading to more effective and targeted therapeutic interventions.

The aim of this study was to evaluate the diagnostic value of various easily obtainable clinical and laboratory indicators and to establish a decision tree (DT) model to analyze their predictive significance for monosodium urate (MSU) deposition in the first metatarsophalangeal (MTP) joint.

A retrospective study was conducted on 317 patients who presented to the outpatient clinic with a gout flare between January 2023 and June 2024 (181 cases with MSU deposition in the first MTP joint and 136 cases without such deposition). Clinical and laboratory indicators included gender, age, disease course, serum uric acid (SUA), glomerular filtration rate (GFR), serum creatinine (SCR), C-reactive protein (CRP), and erythrocyte sedimentation rate (ESR). Statistical analysis methods, including T-test, logistic regression and decision tree, were used to analyze the predictors of MSU deposition in the first MTP joint. The performance of the DT model was evaluated using receiver operating characteristic (ROC) curves and a 5-fold cross-validation method was used to ensure the robustness of the study results.

Disease course, GFR, SUA, age, and SCR emerged as significant predictors of MSU deposition in the first MTP joint in both LR and DT analyses. The DT model exhibited superior diagnostic performance compared to the LR model, with a sensitivity of 83.4% (151/181), specificity of 56.6% (77/136), and overall accuracy of 71.9% (228/317). The importance of predictive variables in the DT model showed disease course, GFR, SUA, age, and SCR as 53.36%, 21.51%, 15.1%, 5.5% and 4.53%, respectively. The area under the ROC curve predicted by the DT model was 0.752 (95% CI: 0.700~0.800).

The DT model demonstrates strong predictive capability. Disease duration, GFR, SUA, age, and SCR are pivotal factors for predicting MSU deposition at the first MTP joint, with disease course being the most critical factor.

Reversible cerebral vasoconstriction syndrome (RCVS) is a condition characterized by thunderclap headaches, which are sudden and severe headaches that peak within a few seconds. These headaches present diagnostic difficulties due to their diversity and low specificity, often leading to misdiagnoses and patient dissatisfaction.

We present the case of a 52-year-old woman with a 10-day history of recurrent thunderclap headaches. Initial imaging revealed no abnormalities, but she experienced further episodes of thunderclap headaches during hospitalization. Subsequent neurovascular imaging revealed multiple intracranial stenoses with a “string of beads” appearance, confirming the diagnosis of reversible cerebral vasoconstriction syndrome. She was treated with nimodipine, and most symptoms had resolved upon discharge, with no recurrence of headache reported during a 3-month follow-up.

Prior reviews on reversible cerebral vasoconstriction syndrome predominantly emphasized isolated symptoms or advanced neuroimaging findings, offering limited applicability in primary care services. More attention should be given to identifying clinical manifestations warranting heightened reversible cerebral vasoconstriction syndrome suspicion.

Early recognition of reversible cerebral vasoconstriction syndrome counts in primary care services. We proposed a revised diagnostic routine that begins with clinical suspicion prompted by typical manifestations, like recurrent thunderclap headaches, female sex, and specific triggers, and recommends advanced neurovascular imaging when accessible. Extreme headache severity or deviation from prior migraine patterns should raise suspicion for reversible cerebral vasoconstriction syndrome, while diagnostic consideration should still remain in patients with transient neurological deficits, seizures, or cerebrovascular events.

Cor Triatriatum Sinister (CTS) is a rare congenital anomaly, accounting for 0.1%- 0.4% of congenital heart diseases. While often diagnosed and treated in infancy, some cases remain asymptomatic until adulthood due to large fenestrations. This report presents a unique case of CTS in an adult coexisting with chronic adhesive pericarditis, which may have contributed to chronic atrial dilatation, a condition not previously documented.

A 60-year-old asymptomatic Taiwanese male underwent a routine medical examination. Coronary computed tomography angiography revealed a fenestrated septum dividing the left atrium, consistent with CTS. Virtual endoscopy confirmed two wide fenestrations. Notably, chronic adhesive pericarditis, evidenced by curvilinear calcifications, was diagnosed. This condition likely exacerbated the hemodynamic impact of CTS, contributing to left atrial dilation and atrial fibrillation. Atrial fibrillation was identified, and the patient was treated with an anticoagulant for stroke prevention.

This is the first reported case of CTS coexisting with chronic adhesive pericarditis. Advanced imaging modalities, including cardiac computed tomography, angiography, and virtual endoscopy, are crucial for diagnosis and anatomical evaluation. Chronic adhesive pericarditis may amplify the effects of CTS, leading to complications, including atrial fibrillation. Anticoagulation is essential for stroke prevention in such cases.

Type 2 diabetes mellitus (T2DM) complicated with interstitial lung abnormalities (ILAs) is often overlooked and can progress to severe diabetes-induced pulmonary fibrosis (DiPF). Therefore, early diagnosis of T2DM complicated with ILAs is crucial. Chest computed tomography (CT) is an important method for diagnosing T2DM complicated with ILAs. Quantitative computed tomography (QCT) is more objective and accurate than visual assessment on CT. However, there are currently limited studies on T2DM complicated with ILAs based on quantitative CT.

This study aimed to explore the utility of quantitative computed tomography for early detection of lung injury in individuals with T2DM by examining CT-derived metrics in T2DM complicated with ILAs.

We collected data from 135 T2DM complicated with ILAs on chest CT scans retrospectively, alongside 135 non-diabetic controls with normal CT findings. Employing digital lung software, chest CT images were processed to extract quantitative parameters: total lung volume (TLV), emphysema index (LAA-950%, the percentage of lung area with attenuation < –950 Hu to total lung volume), pulmonary fibrosis index (LAA-700~-200%, the percentage of lung area with attenuation from –700Hu to –200 Hu to the total lung volume), and pulmonary peripheral vascular index (ratio TAV/TNV, the number of blood vessels TNV, the cross-sectional area of blood vessels TAV). Statistical comparisons between groups utilized Mann-Whitney U or t-tests. Correlations between Hemoglobin A1c (HbA1c) levels and CT parameters were assessed via Pearson or Spearman correlations. Parameters showing statistical significance were further examined through receiver operating characteristic (ROC) analysis.

The T2DM-ILAs cohort displayed a significantly higher LAA-700~-200% compared to controls (Z = -7.639, P< 0.001), indicative of increased fibrotic changes. Conversely, TLV (Z =-3.120, P=0.002), TAV/TNV (Z = -9.564, P< 0.001), and LAA-950% (Z = -4.926, P < 0.001) were reduced in T2DM-ILAs patients. The correlation between HbA1c and various CT quantitative indicators was not significant, HbA1c and TLV (r=-0.043, P=0.618), HbA1c and TAV (r=0.143, P=0.099), HbA1c and TNV (r=0.064, P=0.461), HbA1c and LAA-700~-200% (r=0.102, P=0.239), HbA1c and LAA-950% (r=-0.170, P=0.049), HbA1c and TAV/TNV (r=0.175, P=0.043). The peripheral vascular marker, TAV/TNV, excelled in distinguishing T2DM-related lung changes (AUC=0.84, P<0.001), outperforming LAA-700~-200% (AUC=0.77,P<0.001). A composite index incorporating multiple quantitative parameters achieved the highest diagnostic accuracy (AUC = 0.91, P< 0.001).

Quantitative CT parameters distinguish T2DM complicated with ILAs from non-diabetic individuals, suggesting a distinct pattern of lung injury. Our findings imply a particular susceptibility of small pulmonary blood vessels to injury in T2DM.

Non-gestational Ovarian Choriocarcinoma (NGOC) is an extremely rare and highly malignant ovarian germ cell tumor with nonspecific clinical manifestations, making early diagnosis challenging. At present, detailed reports on the clinical and imaging characteristics of NGOC are scarce. This case report discusses a rare instance of NGOC in a prepubertal adolescent, complemented by a literature review to enhance clinicians’ understanding of its presentation, diagnosis, and treatment.

A 10-year-old female with no history of menstruation or sexual activity presented with persistent lower abdominal pain and vaginal bleeding. Preoperative imaging revealed a large pelvic mass with heterogeneous echogenicity and vascularity. Serum Human Chorionic Gonadotropin (hCG) levels were markedly elevated (>297,000 IU/L).

Ultrasonography and CT demonstrated a large, heterogeneous, hypervascular adnexal mass with features of necrosis and cystic changes, suggesting malignancy.

The mass, originating from the right adnexa, was removed via laparotomy. Histopathology confirmed NGOC, supported by immunohistochemistry, showing strong positivity for markers like CD146, CK18, HCG, and HPL, along with a high Ki-67 index (>90%).

In young females with no sexual life, significantly elevated HCG levels and imaging findings of a large heterogeneous adnexal mass should raise suspicion for NGOC. Early recognition and multimodal diagnostic approaches, including imaging, biochemical, and pathological assessments, are essential for timely intervention, reducing metastatic risk and improving prognosis. This report contributes to the understanding of NGOC and emphasizes the importance of accurate diagnosis for better patient outcomes.

Pain with a persistent and recurrent onset is one of the most important symptoms of temporomandibular disorders (TMD). Recent evidence indicated the dysfunction of the central nervous system was more linked to TMD pain. This study aimed to explore the abnormal structural and perfusion alterations in patients with painful TMD (p-TMD) to understand the comprehension of neuro-pathophysiological mechanisms.

Forty-one p-TMD patients and 33 normal controls (NC) were recruited, and high-resolution structural brain and 3D PCASL data were obtained from a 3.0T MR scanner. The voxel-based analysis of the whole cerebral gray matter (GMV) was performed, and the GMV and cerebral blood flow (CBF) value of the altered positive areas were extracted to investigate the significant correlation with clinical variables.

The brain regions with significantly increased GMV in p-TMD group were listed as follows: right putamen, right superior frontal gyrus, left superior frontal gyrus medial segment, right supplementary motor cortex, left postcentral gyrus, right middle temporal gyrus, right postcentral gyrus medial segment, right temporal pole, right inferior temporal gyrus and right opercular part of the inferior frontal gyrus (Punc<0.001, cluster>39). However, there were no brain regions with significantly decreased GMV in the p-TMD group. Cerebral perfusion analysis identified that only the right postcentral gyrus medial segment presented significantly higher CBF value in the p-TMD group than in the NC group over all the brain regions with increased GMV. Within the p-TMD group, pain intensity, anxiety, depression, and jaw functional limitation scores were differentially associated with GMV and CBF value.

The voxel-based morphometric and perfusion findings collectively implicate maladaptive plasticity in both the sensory-discriminative and affective-motivational dimensions of pain processing in p-TMD pathophysiology.

Modic changes (MCs) are a common manifestation of lumbar degenerative disease, classified into three types. However, the relationship between Bone Mineral Density (BMD) and each type of MC at the vertebral lesion sites remains unclear.

This study included 144 patients who had both lumbar MR and CT images. The classification and grading of MCs were evaluated using MR images. On the CT images, BMD values, T-scores, and Z-scores were obtained from the normal T12 vertebrae, the corresponding lumbar Modic lesion sites, and the adjacent healthy regions at the same vertebra on the axial plane.

A total of 370 vertebrae (226 MCs and 144 normal T12 vertebrae) were assessed. No significant difference was found in the BMD of normal T12 vertebrae between males and females in the study. MCs were more commonly found in the lumbar 4 and 5 vertebrae. Of the MCs, 80 (36%) were classified as type I, 130 (57%) as type II, and 16 (7%) as type III. The BMD value, T-score, and Z-score of each Modic type lesion site were higher than those of adjacent healthy regions and normal T12 vertebrae. A strong correlation was found between the different Modic types, though no significant differences were observed between grades within the same Modic type.

The presence of any MCs was significantly associated with an increase in BMD in the corresponding lesion sites, with more severe MCs showing a stronger association with higher BMD. This is the first study to explore the relationship between all types of MCs and their BMD values.

Intrauterine growth restriction (IUGR) is associated with long-term metabolic disturbances, including obesity. Changes in hepatic lipid metabolism and adipose tissue function, mediated by lipin-1 and lipin-2, may contribute to these outcomes.

This study aimed to investigate the correlation between lipin-1 in visceral adipose tissues (VATs) and lipin-2 in the liver. It also examined hepatic T1 values using T1 mapping in IUGR rats.

The objective of this study was to explore the metabolic mechanisms linking IUGR and adult obesity by analyzing molecular and imaging markers.

Pregnant rats were fed either a low-protein diet (10%) to induce IUGR or a normal-protein diet (21%) as a control. Male offspring underwent conventional magnetic resonance imaging and native T1 mapping using a 3.0 T whole-body MR scanner at days 21, 56, and 84 post-birth. Liver tissues and VATs were collected for analysis. Lipin-1 and lipin-2 expression levels were measured using Western blot and real-time quantitative PCR.

The IUGR group exhibited significantly higher mRNA and protein expression levels of lipin-1 and lipin-2 compared to the control group at days 21, 56, and 84 after birth. Additionally, the IUGR group demonstrated significantly higher hepatic T1 values than the control group at the corresponding time points. Positive correlations were observed between the protein and mRNA expression levels of lipin-1 and hepatic T1 values. Similarly, the protein and mRNA expression levels of lipin-2 were positively correlated with hepatic T1 values. All results were statistically significant (P<0.05).

The upregulation of lipin-1 and lipin-2 expressions was found to be linked to elevated hepatic T1 values, potentially contributing to adult obesity in IUGR rats.

Semantic segmentation algorithms are essential for identifying and segmenting human organs and lesions in medical images. However, as U-Net variants enhance segmentation accuracy, they often increase in parameter count, demanding more sophisticated and costly hardware for training.

This study aims to introduce a lightweight U-Net that optimizes the trade-off between network parameters and segmentation accuracy, while fully leveraging the encoder's feature extraction capabilities.

We propose a lightweight full-encoder U-shaped network, termed LFE-UNet, which employs full-encoder skip connections, encompassing all encoder layers. This model is designed with a reduced number of basic channels—specifically, 8 instead of the typical 64 or 32—to achieve a more efficient architecture.

The LFE-UNet, when integrated with ResNet34, achieved a Dice score of 0.97385 on the ISBI LiTS 2017 liver dataset. For the BraTS 2018 brain tumor dataset, it obtained 0.87510, 0.93759, 0.87301, and 0.81469 on average, WT, TC, and ET, respectively. The paper also discusses the impact of varying basic channel numbers n and encoder layer counts N on the network's parameter efficiency, as well as the model's robustness to different levels of Gaussian noise in images and salt and pepper noise in labels. Additionally, the influence of different loss functions is explored.

The LFE-UNet proves that high segmentation accuracy can be attained with a markedly lower parameters, fully utilizing the full-scale encoder's feature extraction. It also highlights the significance of loss function selection and the effects of noise on segmentation accuracy.

The fundamental problem with the transmission and storage of medical images is their inherent redundancy and large size necessitating higher bandwidth and a significant amount of storage space.

The main objective is to enhance the compression efficiency through accurate segmentation followed by non-uniform compression through a cascade of encoders.

Due to a sharp growth in digital imaging data, it is highly desirable to reduce the size of medical images by a significant amount, without losing clinically important diagnostic information. The majority of the compression techniques reported in the literature use either manual or traditional segmentation techniques to extract the informative parts of the images. The methods based upon non-uniform compression require accurate extraction of the informative part of the image to achieve higher compression rate.

This research proposes unsupervised machine learning modified fuzzy c-means (FCM) clustering-based segmentation for accurate extraction of informative parts of MR images. The spatial constraints of the images are extracted using an automated region-growing algorithm and incorporated into the objective function of FCM clustering (RG-FCM) to enhance the performance of the segmentation process even in the presence of noise. Further, informative and background parts are subjected to two separate series of encoders, with higher bit rates for the informative part of the image.

Empirical analysis was done on the Magnetic Resonance Imaging (MRI)dataset, and experimental results indicate that the proposed technique outperforms similar existing techniques in terms of segmentation and compression metrics.

This integration of different segmentation techniques exhibits improvement in Jaccard and dice indexes, and cascade of different encoders endorse the superior performance of the proposed compression technique. The proposed technique can help in achieving higher compression of medical images without compromising clinically significant information.

Gastric schwannoma is a rare gastrointestinal mesenchymal tumor with Schwann cell differentiation. In the past, most of the published cases were single gastric schwannoma. Multiple gastric schwannoma is exceedingly rare. We herein report a case of multiple gastric schwannomas.

A 55-year-old male presented with postprandial vomiting of unclear etiology, accompanied by epigastric pain and bloating. Computed tomography revealed marked thickening of the gastric wall at the fundus-body junction along the greater curvature and gastric angle, with intraluminal nodular projections. Multiphase contrast-enhanced computed tomography demonstrated moderate progressive enhancement. The patient was misdiagnosed as having a gastric stromal tumor before the operation and subsequently underwent laparoscopic partial gastrectomy. However, pathological and immunohistochemical analysis confirmed multiple gastric schwannomas. The patient recovered uneventfully and was discharged without complications.

Gastric schwannoma is rare in clinical practice, especially gastric multiple schwannomas, which are easily confused with gastric stromal tumors, as illustrated in this case, where a preoperative misdiagnosis occurred. Clinicians should enhance their recognition of characteristic imaging features (including Computed tomography, Magnetic resonance imaging, and Positron emission tomography) and employ multimodal diagnostic approaches to optimize preoperative diagnosis.

Small cell neuroendocrine carcinoma (SCNEC) of the ureter is extremely rare, and tends to show a mixed histologic profile. Literature on its imaging features is limited.

We herein report the case of a 68-year-old woman who presented with two days of left flank pain. Ultrasound and CT scan revealed a lesion in the left distal ureter. The lesion exhibited intensive tracer activity on ¹⁸F-FDG PET/CT scan, corresponding to a malignant tumor, most likely a high-grade urothelial carcinoma, and no metastases were observed. Then, the patient underwent a radical left nephroureterectomy. Pathology revealed a carcinoma composed of SCNEC (approximately 83%) and urothelial carcinoma (approximately 17%). During one year of follow-up, the patient underwent six cycles of adjuvant chemotherapy (etoposide 100mg d1-3 + cisplatin 30mg d1-3, q3w), and no recurrence or metastases were found on the CT scan.

This case report has presented a case of ureteral SCNEC and explored the value of ¹⁸F-FDG PET/CT in the diagnosis and staging of the disease.

A transverse mesocolic internal hernia is a phenomenon in which a small intestinal loop protrudes through the natural orifice in the transverse colon mesentery. This type of internal hernia in adults, although rare, is one of the causes of closed-loop intestinal obstruction, which requires prompt diagnosis and treatment.

We report two cases of transverse mesocolic internal hernia that were examined and subsequently treated at Hospital 108, Hanoi, Vietnam. Both patients (53 and 66 years old) had atypical clinical symptoms, mainly dull epigastric pain. Upon admission, they were initially examined clinically, followed by blood testing and chest and abdominal X-ray radiography. Diagnostic imaging was mainly based on subsequent Multidetector-Row Computed Tomography (MDCT). Laparoscopic/surgical release of the hernia and closure of the natural orifice in the transverse colon mesentery were performed. The clinical symptoms and laboratory and radiographic findings did not suggest a causal diagnosis. However, MDCT provided several images suggestive of an internal hernia, including a closed intestinal loop passing through the transverse colon mesentery and located posteriorly in the left abdominal cavity near the Treitz angle, displacement of the mesenteric vascular bundle, and colon displacement. These displacements were the causes of intestinal inflammation/obstruction. Additionally, laparoscopic/surgical results confirmed the MDCT diagnosis.

Thin-slice thickness, high spatial resolution, multiplanar reconstruction MDCT was effective for diagnosing transverse mesocolic internal hernia. In our two cases, MDCT helped determine the cause and assess the state of intestinal ischemia.

The study investigates the creation and assessment of Machine Learning (ML) models using different classifiers such as Support Vector Machine (SVM), logistic regression, decision tree, k-nearest neighbour (kNN), and Artificial Neural Network (ANN) for the automated identification of tuberculosis (TB) from chest X-ray (CXR) images.

As a persistent worldwide health concern, TB requires early detection for effective treatment and control of the infection. The differential diagnosis of TB is a challenge, even for experienced radiologists. With the use of automated processing of CXR images which are reasonable and frequently used for TB diagnosis, employing Artificial Intelligence (AI) techniques provides novel possibilities.

The objective of the study was to identify respiratory disorders, radiologists devote a lot of time reviewing each of the CXR images. As such, they can identify the type of disease using automated methods based on AI algorithms. This work advances the diagnosis of TB via machine learning, which may result in early treatment options and enhanced outcomes for patients.

The disease was classified using distinct parameters like edge, shape, and Gray Level Difference Statistics (GLDS) on splitting of the dataset at 70:30 and 80:20.

It was observed that authors attained 93.5% accuracy using SVM with linear kernel for a 70:30 data split considering hybrid parameters. The comparison was made considering different feature extraction techniques, different dataset splitting, existing work, and another dataset.

The designed model using SVM, decision tree, kNN, ANN, and logistic regression was compared using other state-of-the-art techniques, other datasets, different feature extraction techniques, and different splitting of data. AI has great promise for enhancing tuberculosis detection, which will ultimately lead to an earlier diagnosis and improved disease management.

This study aimed to analyze the composition of renal staghorn calculi and their characteristics using spectral CT.

This study enrolled 111 cases of renal staghorn calculi from 94 patients (48 males and 46 females, aged 28–76 years; median age: 56 years). Using spectral CT, average Zeff and CT values were analyzed. The water/iodine-based images were generated by the material separation module. All stones were detected by FTIR spectroscopy.

111 cases of renal staghorn calculi included 53 cases of single composition (47.8%) and 58 cases of mixed composition (52.2%). In staghorn calculi of a single composition, urate (23 cases) and calcium oxalate monohydrate (16 cases) were more prevalent than struvite (5 cases) and brushite (5 cases). Mixed compositions included metabolic-metabolic (36 cases, 62.1%), metabolic-infectious (14 cases, 24.1%), and infectious-infectious (8 cases, 13.8%) cases, respectively. The average Zeff values showed some characteristics of carbapatite and urate. However, average Zeff and CT values had many overlappings among other compositions. All stones appeared homogeneous in water-based images. In iodine-based images, calcium oxalate monohydrate displayed homogeneous high density, but struvite and brushite showed heterogeneous high density. Single compositions of carbapatite, calcium oxalate monohydrate, and cystine exhibited homogeneous high density, similar to mixed compositions of carbapatite and calcium oxalate monohydrate. Furthermore, urate demonstrated homogeneous low density. Moreover, the mixture of struvite and brushite/urate showed heterogeneous high density.

In staghorn calculi of a single composition, the metabolic type was common, while metabolic-metabolic and metabolic-infectious types frequently occurred in staghorn calculi with mixed compositions. Except for average Zeff values, water-iodine material separation performed an important auxiliary function in differentiating stones’ compositions using spectral CT.

Optic nerve hemangioblastoma (ONH) is a rare benign tumor. It can be sporadic or associated with Von-Hippel Lindau (VHL) syndrome. Magnetic resonance imaging (MRI) is the most commonly used diagnostic technique for the tumor. However, an accurate diagnosis can be challenging due to the rarity of ONH and its similarity to glioma and meningioma.

A 49-year-old female experienced progressive vision loss for ten years in the right eye, accompanied by proptosis over two years. The ophthalmological examination found her visual acuity of the right eye to have no light perception. Optical coherence tomography showed decreased thickness of the right retinal ganglion cell layer. MRI revealed an oval solid mass within the right retrobulbar space, with isointensity on T1-weighted (T1WI) imaging and heterogeneous hyperintensity on T2-weighted imaging (T2WI). Heterogeneous enhancement was found on gadolinium-enhanced T1WI and dynamic contrast-enhanced MRI. At internal and marginal areas of the mass, multiple flow voids were observed on various sequences, especially on T2WI. Furthermore, the superior, inferior, medial, and lateral rectus muscles of the right eye distinctly atrophied, showing a lower signal intensity on T2WI and less apparent enhancement than the left normal ones. Postoperative pathological diagnosis was hemangioblastoma of the right optic nerve.

Hemangioblastoma should be considered as a differential diagnosis for the space-occupying mass of the optic nerve if there is the presence of flow voids, vivid enhancement, and absence of a dural attachment, regardless of VHL syndrome. Of note, this is the first reported case to consider altered extraocular muscles as a potential point to prompt the diagnosis on MRI.

The study aims to investigate the prognostic value of deep learning based pericoronary adipose tissue attenuation computed tomography (PCAT) and plaque volume beyond coronary computed tomography angiography (CTA) -derived fractional flow reserve (CT-FFR) in patients with percutaneous coronary intervention (PCI).

A total of 183 patients with PCI who underwent coronary CTA were included in this retrospective study. Imaging assessment included PCAT, plaque volume, and CT-FFR, which were performed using an artificial intelligence (AI) assisted workstation. Kaplan-Meier survival curves analysis and multivariate Cox regression were used to estimate major adverse cardiovascular events (MACE), including non-fatal myocardial infraction (MI), stroke, and mortality.

In total, 22 (12%) MACE occurred during a median follow-up period of 38.0 months (34.6-54.6 months). Kaplan-Meier analysis revealed that right coronary artery (RCA) PCAT (p = 0.007) and plaque volume (p = 0.008) were significantly associated with the increase in MACE. Multivariable Cox regression indicated that RCA PCAT (hazard ratios (HR): 7.05, 95%CI: 1.44-34.63, p = 0.016) and plaque volume (HR: 3.84, 95%CI: 1.44-10.27, p = 0.007) were independent predictors of MACE after adjustment by clinical risk factors. However, CT-FFR was not independently associated with MACE in multivariable Cox regression (p = 0.150).

Deep learning based RCA PCAT and plaque volume derived from coronary CTA were found to be more strongly associated with MACE than CT-FFR in patients with PCI.

Lung cancer is one of the main threats to global health, among lung diseases. Low-Dose Computed Tomography (LDCT) provides significant benefits for its screening but also brings new diagnostic challenges that require close attention.

By searching the Web of Science core collection, we selected articles and reviews published in English between 2005 and June 2024 on topics such as “Low-dose”, “CT image”, and “Lung”. These literatures were analyzed by bibliometric method, and CiteSpace software was used to explore the cooperation between countries, the cooperative relationship between authors, highly cited literature, and the distribution of keywords to reveal the research hotspots and trends in this field.

The number of LDCT research articles show a trend of continuous growth between 2019 and 2022. The United States is at the forefront of research in this field, with a centrality of 0.31; China has also rapidly conducted research with a centrality of 0.26. The authors' co-occurrence map shows that research teams in this field are highly cooperative, and their research questions are closely related. The analysis of highly cited literature and keywords confirmed the significant advantages of LDCT in lung cancer screening, which can help reduce the mortality of lung cancer patients and improve the prognosis. “Lung cancer” and “CT” have always been high-frequency keywords, while “image quality” and “low dose CT” have become new hot keywords, indicating that LDCT using deep learning techniques has become a hot topic in early lung cancer research.

The study revealed that advancements in CT technology have driven in-depth research from application challenges to image processing, with the research trajectory evolving from technical improvements to health risk assessments and subsequently to AI-assisted diagnosis. Currently, the research focus has shifted toward integrating deep learning with LDCT technology to address complex diagnostic challenges. The study also presents global research trends and geographical distributions of LDCT technology, along with the influence of key research institutions and authors. The comprehensive analysis aims to promote the development and application of LDCT technology in pulmonary disease diagnosis and enhance diagnostic accuracy and patient management efficiency.

The future will focus on LDCT reconstruction algorithms to balance image noise and radiation dose. AI-assisted multimodal imaging supports remote diagnosis and personalized health management by providing dynamic analysis, risk assessment, and follow-up recommendations to support early diagnosis.

Epithelioid Hemangioendothelioma (EHE) is a rare vascular neoplasm that typically occurs in the bone, soft tissue, liver, and lung but rarely in the mediastinum. Multimodal imaging of EHE is poorly understood, often leading to misdiagnosis as other mediastinal tumors.

Two female cases with incidental mediastinal masses were retrospectively analysed, focusing on multimodal presentations. For both cases, CT studies showed well-defined, low-density oval masses in the right anterior superior mediastinum with the Superior Vena Cava (SVC) invasion. Intralesional punctate calcifications were observed in Case 2. MRI revealed hypointense masses on T1WI and slightly hyperintense on T2WI, with partial diffusion restriction on DWI. Case 1 had mild enhancement, while Case 2 had significant enhancement. PET-CT showed significant FDG uptake with maximum standardized uptake values (SUVmax) of 9.2 and 5.1, respectively. Both patients underwent surgical resection, with pathology confirming mediastinal EHEs.

Mediastinal EHE presents as a well-defined soft-tissue mass with punctate calcifications and heterogeneous enhancement, typically located in the anterior mediastinum with invasion into medium or large veins. Moreover, it should be considered in the differential diagnosis of mediastinal tumors.

This study aimed to investigate the imaging features of primary fallopian tube carcinoma (PFTC).

Imaging findings of 12 PFTC patients were retrospectively studied. Multi-slice computed tomography (CT, MSCT) was performed to investigate tumor location, size, density, appearance (cystic/solid), enhancement pattern, and metastasis.

Twelve women aged 34–67 (mean=54.3) years were presented with pelvic pain (n=6), vaginal discharge (n=5), and incidental pelvic masses (n=3). The tumor diameters of PFTC varied from 3.3 to 6.8 cm (mean=4.7 cm). Ten cases were unilateral, and two were bilateral. The lesions were adnexal tubular-shaped cystic masses with mucosal papillary nodes in six cases, irregular cystic and solid masses in four cases, and sausage-shaped solid masses in two cases. The plain CT values ranged from 15 to 35 HU (mean, 28 HU). On enhanced CT, the enhancement of the solid composition was lower than that of the myometrium in all phases. CT values in arterial and venous phases were 55-62 and 60-63 HU, respectively, with average values of 58.6 and 61 HU. The metastasis sites included the ovary (n=2), omentum (n=3), retroperitoneal lymph nodes (n=5), pelvic lymph nodes (n=5), and inguinal lymph nodes (n=2). Seven cases exhibited pelvic fluid, and seven exhibited round ligament thickening on the lesioned side.

In patients presenting with vaginal discharge or genital bleeding and sausage-shaped or tubal-shaped cystic, solid, or solid-cystic complexes in the adnexal portion associated with hydrosalpinx and peritumoral ascites, PFTC should be considered in the diagnosis, especially in tumors associated with round ligament thickening.

Accurate determination of tumor boundaries is crucial for staging and treating central lung cancer (CLC).

This retrospective study aimed to evaluate the feasibility of contrast-free CT radiomics in discriminating CLC tumors from atelectasis.

A total of 58 patients with CLC and associated lung atelectasis, corresponding to 58 tumors and 58 atelectasis regions, were included. Radiomics features were extracted from tumor and atelectasis areas using contrast-free CT images. The least absolute shrinkage and selection operator (LASSO) identified the most differential radiomics features. A logistic regression model (LR) was established and evaluated using 5-fold cross-validation. Discrimination performance was assessed using the area under the ROC curve (AUC) and decision curve analysis (DCA). Additionally, the potential of visualizing and distinguishing tumors and atelectasis based on contrast-free CT was explored by comparing pixel-level radiomics features with contrast CT.

A total of 1561 radiomics features were extracted, with 356 showing significant statistical differences between tumor and atelectasis. LASSO identified the 10 most differential radiomics features. The LR model trained with these features achieved an AUC of 0.94 (95% CI: 0.89-0.99), sensitivity of 0.88, and specificity of 0.89 in the training group, and an AUC of 0.81 (95% CI: 0.67–0.95), sensitivity of 0.78, and specificity of 0.65 in the validation group. DCA confirmed the clinical utility, and the radiomics feature square_firstorder_10Percentile showed good performance in distinguishing tumors from atelectasis, with consistency to contrast CT.

Contrast-free CT radiomics can effectively discriminate CLC tumors from atelectasis.

This study aimed to characterize the magnetic resonance imaging (MRI) findings of juvenile suprasellar pilocytic astrocytoma (PA) in a sample of 11 children and help neuroradiologists preoperatively differentiate PAs from other suprasellar tumors.

Eleven consecutive children with pathologically confirmed suprasellar PAs in our hospital from May 2015 to November 2021 were enrolled in this study. The clinical data and preoperative MR images were retrospectively reviewed. MRI included T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), and postcontrast T1WI. Six patients underwent diffusion-weighted imaging (DWI). The location, signal features, enhancement pattern, and apparent diffusion coefficient (ADC) of the lesions on MRI were evaluated. The clinical status of the patients 3 years after surgery was noted.

The 11 suprasellar PAs were mainly located around the optic chiasma and hypothalamus and invaded adjacent structures. The lesions showed hyperintensity or slight hyperintensity on T2WI and hypointensity on T1WI. Among the 11 patients, 5 had solid tumors with homogeneous enhancement, one had a solid tumor with heterogeneous enhancement, and five had cystic and solid tumors with heterogeneous enhancement. Cerebrospinal fluid (CSF) dissemination foci were observed in 4 patients. The solid components of the lesions were hypointense or isointense on DWI, with high ADC values at a mean of 1.77±0.36 ×10^-3 mm²/s. Gross total resection was achieved in only one patient (9.1%), and 10 (90.9%) were subtotally resected. Five patients died during the follow-up period, and the 3-year survival rate was 54.5%.

Juvenile suprasellar PAs are characterized by a solid and intermixed cystic and solid appearance, hyperintensity on T2W images, obvious enhancement of the solid component, and relatively high ADC values.

Various methods have been employed to evaluate secondary limb lymphedema, each with its own set of limitations.

To delve into a novel approach to lymphatic grading, specifically utilizing enhanced ultrasound for assessing lymphatic function, to compensate for the shortcomings of other methods to some extent.

The clinical and ultrasound data of 51 patients with secondary limb lymphedema from June 2022 to September 2023 were retrospectively analyzed. The characteristic ultrasound manifestations of all visualized lymphatic vessels were studied. A contrast-enhanced ultrasound grading of lymphatic vessels (Ceus-Clv) was formulated and applied to grade the 51 patients. The study also correlated Ceus-Clv with Campisi clinical stage, postoperative duration, and duration of edema.

Out of 51 patients, there were 19 cases of Ceus-Clv I, 10 cases of Ceus-Clv II, 19 cases of Ceus-Clv III, and 3 cases of Ceus-Clv IV. The correlation coefficient (rs) between Ceus-Clv and Campisi clinical stages was 0.958 (P < 0.001). Similarly, the correlation coefficient between Ceus-Clv and postoperative duration was 0.824 (P < 0.001), and between Ceus-Clv and duration of edema was 0.763 (P < 0.001).

Ceus-Clv grading is a safe, convenient, and effective method for assessing lymphatic vessel function in secondary limb edema. This method can accurately reflect the patient's lymphatic vessel function and the severity of edema, providing valuable guidance for the treatment of secondary limb edema.

Determination of LVSI is the recommended criterion for performing lymphatic drainage and is important for the preoperative clinical decision-making process; however, Intraoperative Frozen Section (IFS) has limitations for the analysis of LVSI, and there is an urgent need for other indirect methods to predict the presence of LVSI.

This study aimed to investigate the value of Magnetic Resonance Imaging (MRI) features in predicting Lymphovascular Space Invasion (LVSI) in endometrial cancer (EC).

The objective of this study was to analyze MRI features that may be associated with LVSI and to explore their association.

In this study, 179 patients who received treatment for EC confirmed by surgical pathology at two medical institutions from January 2017 to May 2024 were reviewed and grouped according to the presence or absence of vascular cancer embolism in the pathology. The MRI imaging features of the two groups were compared, including the maximum transverse diameter in the sagittal position, myometrial invasion, disruption of the uterine Junctional Zone (JZ), serosal surface, uterine appendages, cervical stromal invasion, lymph node enlargement, and its T2 value, and Diffusion-Weighted Imaging (DWI). The risk factors of the LVSI-positive group were determined by performing logistic regression analysis to analyze the correlation between Apparent Diffusion Coefficient (ADC) values and LVSI in EC.

There were 34 cases in the LVSI-positive group and 145 cases in the negative group. The maximum transverse diameter in sagittal position, myometrial invasion, interruption of the uterine JZ, serous surface, uterine appendages, cervical stromal invasion, lymph node enlargement, and their DWI and ADC values were statistically significant between the two groups (P < 0.05). In multivariate logistic regression analysis, lymph node enlargement (P = 0.001) and ADC value (P = 0.041) were identified as independent risk factors for positive LVSI.

Lymph node enlargement and reduced ADC values (<0.767*10-3mm²/s) in MR imaging are of high value in predicting the occurrence of LVSI in patients with EC and can be used as an important reference for preoperative clinical diagnostic and therapeutic decisions for patients.

Cryptococcosis is a severe but rare opportunistic fungal infection predominantly affecting immunocompromised individuals, such as post-transplant patients. The diagnosis is frequently delayed due to non-specific symptoms and lower incidence than other fungal infections.

A case of a 50-year-old male renal transplant recipient who developed disseminated cryptococcosis complicated by multi-organ failure is presented. Despite adherence to international treatment guidelines, the patient's condition rapidly deteriorated due to the extensive immunosuppression required for transplant rejection management. The patient developed pneumonia and was diagnosed with disseminated cryptococcosis on the 10th day of hospitalization, with Cryptococcus gattii identified in the pulmonary system and pleura. The patient underwent multiple interventions, including bronchoscopy, lobectomy, and pneumonectomy. Despite aggressive treatment, the infection progressed, leading to severe complications, such as neurological decline, gastrointestinal bleeding, and ultimately, multi-organ failure. The patient passed away after 53 days of hospitalization.

This report highlights the importance of early diagnosis and multidisciplinary management in post-transplant patients with suspected opportunistic infections. The high mortality associated with disseminated cryptococcosis, particularly in severely immunosuppressed patients, underscores the need for vigilance and prompt intervention to improve patient outcomes.

To examine the integration of artificial intelligence (AI) into Picture Archiving and Communication Systems (PACS) and assess its impact on medical imaging, diagnostic workflows, and patient outcomes. This review explores the technological evolution, key advancements, and challenges associated with AI-enhanced PACS in healthcare settings.

A comprehensive literature search was conducted in PubMed, Scopus, and Web of Science databases, covering articles from January 2000 to October 2024. Search terms included “artificial intelligence,” “machine learning,” “deep learning,” and “PACS,” combined with keywords related to diagnostic accuracy and workflow optimization. Articles were selected based on predefined inclusion and exclusion criteria, focusing on peer-reviewed studies that discussed AI applications in PACS, innovations in medical imaging, and workflow improvements. A total of 183 studies met the inclusion criteria, comprising original research, systematic reviews, and meta-analyses.

AI integration in PACS has significantly enhanced diagnostic accuracy, achieving improvements of up to 93.2% in some imaging modalities, such as early tumor detection and anomaly identification. Workflow efficiency has been transformed, with diagnostic times reduced by up to 90% for critical conditions like intracranial hemorrhages. Convolutional neural networks (CNNs) have demonstrated exceptional performance in image segmentation, achieving up to 94% accuracy, and in motion artifact correction, further enhancing diagnostic precision. Natural language processing (NLP) tools have expedited radiology workflows, reducing reporting times by 30–50% and improving consistency in report generation. Cloud-based solutions have also improved accessibility, enabling real-time collaboration and remote diagnostics. However, challenges in data privacy, regulatory compliance, and interoperability persist, emphasizing the need for standardized frameworks and robust security protocols.

The integration of AI into PACS represents a pivotal transformation in medical imaging, offering improved diagnostic workflows and potential for personalized patient care. Addressing existing challenges and enhancing interoperability will be essential for maximizing the benefits of AI-powered PACS in healthcare.

This study aimed to investigate the computed tomography (CT) and magnetic resonance imaging (MRI) characteristics of bone remodeling in chronic maxillary sinusitis and assess their clinical significance.

This retrospective study included patients with unilateral chronic maxillary sinusitis and bone remodeling who were admitted to our hospital from January, 2020 to December, 2022. A total of 31 patients were ultimately included. Imaging and clinical data analyses were conducted on the enrolled patients, including multislice spiral computed tomography (MSCT) examination and measurements, as well as plain and enhanced MRI scans. A comparative analysis was performed between the affected and healthy samples. The CT images were evaluated using the “LIAT” systematic assessment method, with a focus on lesion location, extrasinus wall invasion, density, and thickness. Furthermore, a comparative analysis between CT and MRI was carried out for various types of bone remodeling, emphasizing the imaging features of the surrounding soft tissues, including the mucosa and periosteum.

Among the 31 patients with chronic sinusitis, CT revealed 26 cases of cortical-like bone remodeling and 5 cases of cancellous-like bone remodeling. For cortical-like bone remodeling, the thickest part of the posterolateral wall of the maxillary sinus was used to differentiate between mild and moderate-to-severe cases using a 3 mm threshold. Specifically, 15 mild cases exhibited sinus mucosa thickening and a normal blood supply outside the sinus wall on MRI, whereas 11 moderate-to-severe cases exhibited sinus mucosa separation, submucosal edema, and significant vessel proliferation outside the sinus wall on MRI. In cases of cancellous-like bone remodeling, MRI revealed uneven sinus mucosa thickening and mild vessel proliferation outside the sinus wall. Specifically, 21 patients exhibited cross-suture signs, 13 patients exhibited vascular tunnel signs, and 6 patients exhibited nerve canal perineural infiltration.

Chronic maxillary sinusitis bone remodeling appeared in two forms on CT images: cortical-like bone remodeling and cancellous-like bone remodeling. MRI can detect morphological and signal alterations in the soft tissues around the remodeling site. Analyzing the imaging features of bone remodeling in chronic maxillary sinusitis patients can increase the understanding of disease progression and diagnostic accuracy.

This preliminary investigation aimed to assess the value of two elastography techniques, sound touch elastography (STE) and sound touch quantification (STQ), in measuring liver stiffness in children with non-fatty versus fatty livers.

This study used a case-control design. The STE and STQ were used to measure and compare liver stiffness in 121 children with fatty livers and 251 children with non-fatty livers, respectively.

In this study, we found that, compared to children with non-fatty liver disease, children with fatty liver disease had lower Young's modulus values in STE and STQ in the left lobe of the liver, and the difference was statistically significant (P < 0.05). However, after multifactorial analysis, no association was found between liver Young's modulus values measured by STE and STQ and the presence of fatty liver in children.

In the present study, significantly higher Young's modulus values were observed in the left lobe compared to the right lobe of the liver in children with non-fatty liver (P < 0.05). In contrast, no significant difference was found between the left and right lobes in children with fatty liver (P > 0.05). The optimal diagnostic threshold for detecting steatohepatitis in the left lobe was 5.890 kPa using STE and 8.050 kPa using STQ.

STE and STQ, as the latest ultrasound diagnostic techniques based on shear wave elastography, can quantitatively assess fatty liver in children. In this study, some liver elasticity measurements in the fatty liver group differed from those in the non-fatty liver group.

Phlebosclerotic colitis is a rare type of chronic ischemic colitis, with most documented cases occurring in Asians. Plain-film and computed tomography findings of pericolonic vascular calcifications are diagnostic. However, Magnetic Resonance Enterography (MRE) findings of phlebosclerotic colitis have not yet been reported, and its diagnosis might be overlooked without awareness of this disease.

A 70-year-old female patient without prior systemic disease presented with a 3-month history of nausea, vomiting, abdominal pain, and diarrhea. Personal history was unremarkable except for long-term use of herbal medicine. She was initially investigated at a regional hospital with a colonoscopy and biopsy. Due to the presence of stenosis at the transverse colon and biopsy results suggestive of Inflammatory Bowel Disease (IBD), she was referred to our hospital for further investigation and treatment. MRE was performed as part of the IBD workup, which showed a thickened ascending and transverse colonic wall that was fibrotic, non-edematous, and with triangular projections on the mesenteric aspect. Owing to findings that were inconsistent with IBD, subsequent abdominal plain-film radiography confirmed characteristic linear dendritic serpiginous radiopaque opacities alongside the ascending and transverse colon. Re-biopsy of the affected colon confirmed the diagnosis of phlebosclerotic colitis. The patient’s symptoms improved after conservative treatment.

MRE of phlebosclerotic colitis appears as symmetrical non-edematous bowel wall thickening with triangular signal voids indicative of venous calcifications.

Eagle’s Syndrome is a unilateral or bilateral elongation of the styloid process or calcified stylohyoid ligament, along with other symptoms, such as dysphagia, facial pain, globus sensation, and headache. Stylocarotid artery syndrome is a specific type of Eagle’s syndrome that causes various clinical symptoms due to pressure on adjacent anatomical structures.

This study presents a case of a 57-year-old female patient with a complaint of facial pain, head and neck discomfort, globus sensation, difficulty swallowing, and dizziness during head rotation. The patient was diagnosed with a bilateral elongated styloid process through panoramic radiography and cone beam computed tomography. Due to suspicion of stylocarotid artery syndrome, further evaluation was conducted using ultrasound imaging.

Bilateral elongated styloid processes can contribute to Stylocarotid Artery Syndrome (SAS). Ultrasound imaging, specifically B-mode and pulsed wave Doppler, proved to be valuable in detecting real-time vascular flow dynamics in extracranial vessels, highlighting its auxiliary role in the assessment of stylocarotid artery syndrome.

This research aims to develop and evaluate a novel health classification and severity detection system based on Vision Transformers (ViTs) for fetal ultrasound imagery. This contributes to improved precision in fetal health status detection and abnormalities with more accurate results than other traditional models.

Amidst the other imperatives of resource-deficient developing nations, mitigating neonatal mortality rates is a challenge that demands precision-based solutions in the era of artificial intelligence. Though the advent of machine learning models has added an optimal dimension to deal with emerging complexity in fetal ultrasound imagery, there is a call to address the huge gap in the demanded precision for prediction than the existing interpretation.

This research strives to formulate and access a novel health classification and severity detection system based on the implementation of the Vision Transformers frameworks. This pioneering investigation represents an unparalleled exploration into the efficacy of ViTs for discerning intricate patterns within fetal ultrasonographic imagery, facilitating precise categorization of fetal well-being and prognosticating the magnitude of potential anomalies.

A private and confidential dataset of 500 fetal ultrasound images has been collected from diverse hospitals. Each image has been annotated by radiologists according to two main labels: the health status of the fetus, which includes healthy, mild, moderate, or severe, and the severity of abnormalities as a continuous measure. At different levels, the dataset underwent pre-processing via distinct techniques. Then, the composite loss function Cross-Entropy has been deployed to train the optimized VIT model using the Adam algorithm.

The classification accuracy of the proposed model is 90% for detecting the severity with an F1-score of 0.87 and MAE of 0.30. The research ascertained that the model ViT evinced a superlative efficacy for the capturing of fine-grained spatial relations in ultrasound images to produce revolutionary predictions.

These results emphasize that ViTs have the potential to revolutionize fetal health monitoring and will contribute significantly to reducing neonatal mortality by supplying clinicians with accurate and reliable predictions for early interventions. This work stands as a yardstick for further diagnostic applications using AI in fetal health care.

Multiple spectral images can be extrapolated from Spectral Detector CT (SDCT), ED, and OED images. ED and OED images are highly sensitive to moisture-rich tissues. Moreover, they have the potential to detect pulmonary artery thrombi in non-enhanced chest CT images.

The objective of this study was to assess the sensitivity, specificity, and accuracy of ED and OED images obtained using SDCT for the detection of pulmonary embolism on non-enhanced images.

This study aimed to evaluate the utility of unenhanced spectral imaging, Electron Density (ED), and Overlay Electron Density (OED) images for assessing pulmonary embolisms in patients with suspected or confirmed Acute Pulmonary Embolism (APE).

Seventy-nine patients who underwent unenhanced and Computed Tomography Pulmonary Angiography (CTPA) using dual-layer spectral detector CT to evaluate APE between November, 2021 and April, 2022 were enrolled in this retrospective study. Based on unenhanced spectral and CTPA images, two radiologists identified areas of high density in the main, lobar, and segmental pulmonary arteries on ED and OED images and detected Pulmonary Embolism (PE) on enhanced images using a consultative approach. CTPA results were considered the gold standard. The diagnostic performance of ED and OED in detecting PE was analyzed.

PE was detected in 40 patients (40/79), and 17, 69, and 20 PEs were detected in the main, lobar, and segmental arteries, respectively. The PE detection sensitivity on ED images was 69.7–94.7%, and the specificity was 58.5–98.2% for the individual, main, lobe, and segmental pulmonary arteries. The sensitivity and specificity for OED images were 94.1–95.2% and 80.0–98.1%, respectively. The positive predictive value (PPV) and negative predictive value (NPV) were 53.6–87.7% and 69.7–95.9% for ED images and 48.5–88.9% and 94.1–98.9% for OED images, respectively. The accuracy was 76.0–98.9% and 87.3–96.2% when using ED and OED images, respectively. The research identified that whether it was main, lobar, or segmental pulmonary arteries with blood clots, EDW values ranged from 108.1–108.8%EDW, which were 3.9–4.2%EDW higher than those of arteries without emboli. Pulmonary arteries with emboli standardised ED values were 103.6-104.3%EDW.

ED and OED images using spectral CT without contrast media demonstrated high diagnostic performance and could improve the visualization of PE.

With the rapid development of computer technology, the application of digital technology to the display and processing of medical images has become a common concern. In recent years, oral digital imaging technology has received more and more attention.

This paper mainly aims at the ODIS-1 oral digital imaging system to analyze and study the image quality and image aims at the ODIS-1 oral digital imaging system to analyze and study the image quality and processing technology, of which X-ray imaging is indispensable.

In this paper, the ODIS-1 digital scanning technology is used to detect different types of dental tissues, and its application in diagnosing oral diseases is evaluated. This paper takes 320 inpatients as the research object and uses Kodak dental film to compare the image quality of different positions.

It is found that there is no significant difference in image quality between the maxillary anterior teeth and mandibular anterior teeth and the maxillary posterior teeth and mandibular posterior teeth (P>0.05); the image quality of maxillary anterior teeth, mandibular anterior teeth, and maxillary posterior teeth and mandibular teeth are significantly different (P<0.05); among the various positions of the ODIS-1 oral digital imaging system, the image quality of the anterior teeth area is the best, while the image quality of the maxillary posterior teeth area is the worst.

However, the system has a variety of image post-processing functions, which can adjust the brightness and contrast of the image arbitrarily, select the area of interest in the image according to the detection requirements, and perform local amplification, edge enhancement, and other technologies to make the image achieve the best effect. In the case of poor image quality, the clarity of the image can be further improved through image post-processing and analysis.

Recurrent spontaneous abortion (RSA) demonstrates a complex pathogenesis. The uterine artery (UtA) Doppler ultrasound monitoring is clinically valuable for predicting RSA risk.

This study aimed to assess the type of blood flow velocity waveform (FVW) and the hemodynamic characteristics of the UtA between the RSA and control groups.

This retrospective study included 203 patients with RSA and 121 without RSA. All participants underwent transvaginal Doppler ultrasonography during the mid-luteal phase to assess the type of FVW and the hemodynamic parameters of the UtA.

The C type was the most prevalent in both the control and RSA groups, with incidences of 80.16% and 63.04%, respectively. The single type was more predominant in the control group than in the RSA group (83.47% vs. 73.89%). Notably, the compound type was more frequent in the RSA group than in the control group (26.11% vs. 15.26%). The compound type exhibited significantly higher circulatory resistance than the single type, with significant statistical differences observed in the mean pulsatility index (mPI) and mean resistance index (mRI) between the two types (P < 0.001). Further, mPI and mRI values of the UtA were higher in the RSA group than in the control group, with significant statistical differences between the two groups (P < 0.001). If abnormal UtA hemodynamic parameters and FVW are detected, early clinical intervention should be implemented to improve adverse pregnancy outcomes.

UtA FVW varies, indicating differences in blood resistance. Prepregnancy monitoring of high-resistance FVW and hemodynamic parameters effectively assessed uterine perfusion status and may provide a foundation for early clinical intervention and potential personalized treatment strategies.

Pain is a significant indicator of temporomandibular disorders (TMDs), which are impacted by a complex process. Recently, the evolution and chronification of painful TMD (p-TMD) have been facilitated by central nervous system mechanisms. Therefore, the purpose of this study was to investigate the aberrant brain structure in p-TMD patients using surface-based morphometry (SBM) analysis.

This study recruited forty-one p-TMD patients and 33 normal controls (NC) who underwent high-resolution brain structural imaging on a 3.0T MR scanner. SBM analysis was applied to the brain structural images, and the surface parameters, including the cortical thickness, fractal dimension, sulcus depth, and gyrification index, were measured. The independent two-sample t-test by SPM12, with age and gender as covariates, was used to investigate the differences in p-TMD patients compared with the NC.

The p-TMD group had significantly decreased cortical thickness in the left lateral occipital cortex and significantly decreased fractal dimension in the left paracentral, right pars opercularis, right rostral middle frontal, left lingual, and right inferior temporal cortices when compared with NCs. However, there were no significant differences in sulcal depth and gyrification index between the two groups.

This study demonstrated decreased cortical thickness and fractal dimension in p-TMD patients, which may be associated with abnormal neural mechanisms underlying the brain's processing of emotions and pain. The SBM technology may offer additional independent morphological characteristics for investigating the structure of the brain.

Sparse angular projection is an important way to reduce CT dose. It consists of two processes, sparse sampling, and image reconstruction based on sparse projection. Under the traditional reconstruction framework, the sparseness of the projection angle may cause a degradation effect in the reconstructed image. A series of machine learning methods for sparse angle CT reconstruction developed in recent years, especially deep learning methods, can effectively improve the reconstruction quality, however, these methods can only reconstruct CT images based on a certain sparse sampling scheme.

On the other words, they cannot search for an efficient sparse sampling scheme under a certain dose constraint automatically, which became the motivation to develop an end-to-end sparse angular CT reconstruction method.

In this work, we propose a sampling encoding layer for searching sparse sampling schemes and integrate it into a sparse reconstruction neural network model based on projection data. Meanwhile, a joint reconstruction strategy based on both the radon domain and image domain painting is also developed.

Experiments based on public CT datasets demonstrate the effectiveness of the method.

The results show that the joint reconstruction network based on a sparse sampling coding layer has great application potential.

Osteochondritis Dissecans (OCD) is an idiopathic process and can progress from stable to cartilage fragmentation with the formation of loose bodies in the affected joint capsule. Loose bodies in the knee may wear out the articular cartilage, tendons, and ligaments, leading to a series of problems, such as joint locking, bouncing, joint effusion, and meniscus tear; therefore, early recognition and treatment of intraarticular loose bodies are important to achieve favorable long-term outcomes.

A 49-year-old male presented with a 1-month history of right knee discomfort. The patient underwent a knee MRI scan and was diagnosed with OCD. A short-term MRI follow-up with a thin-layer PDWI sequence without fat suppression detected the cartilage fragments in the knee capsule. Loose body removal, cartilage repair, and microfracture surgery were performed under arthroscopic surgery, and loose bodies of cartilage fragments were removed.

Short-term MRI follow-up and thin-layer PDWI sequence without fat suppression are necessary for detecting cartilage loose bodies.

The basal ganglia area is a frequent site of stroke, which commonly causes intricate functional impairments. This study aims to uncover disparities in static and dynamic functional connectivity (FC) of the brain in patients afflicted with left-sided basal ganglia stroke (L-BGS) and right-sided basal ganglia region stroke (R-BGS), furthermore scrutinising the mechanism behind the lateralisation of the stroke.

A total of 23 patients with L-BGS and 20 patients with R-BGS were recruited, alongside 20 healthy control subjects. Resting-state functional magnetic resonance imaging and sliding window techniques were employed to conduct static and dynamic FC analyses on both patient groups and controls, which can enable a more refined evaluation of the variations in neural signals.

The inter-network connectivity analysis showed significant changes only in the L-BGS patient group (p < 0.05). The R-BGS group showed increased connectivity in the auditory and posterior visual networks, while the L-BGS group showed reduced connectivity. In dynamic connectivity analyses, the L-BGS group exhibited greater positive network connectivity reorganization.

Within one month of stroke onset, the L-BGS group showed a more pronounced impairment of inter-network connectivity, alongside enhanced FC compensatory changes of a positive nature. Differential changes in the two patient groups may provide useful information for individualized rehabilitation strategies.

This study aims to investigate the effect of full- and half-rotation angles on patient radiation dose and quantitative image quality in CBCT imaging of the head and neck region.

A total of 67 TLDs were used for the dosimetry of 16 different regions in the head and neck slices of the anthropometric phantom. The Hyperion X9 Pro (MyRay, Cefla, Imola, Italy) CBCT device was used with a 90 kV pulsed beam and a 13x16e FOV size. Two separate imaging modes (Regular 360 ⁰ and Quick 180 ⁰) were tested, and the mA was determined by the software. Effective doses (EDs) were calculated using the coefficients recommended by ICRP 103 (2007). For the quantitative image quality tests, three VOIs were manually selected for three separate densities in image slices selected from the mandible, maxilla, and paranasal sinus regions of both volumes separately. Pixel values were averaged, and (SNR), contrast-to-noise ratio (CNR), and uniformity tests were conducted.

In 360 ⁰, ED was calculated as 1.903 mSv and the highest absorbed dose was found in the oral mucosa (1.566 mSv). In 180 ⁰, ED was calculated as 1.123 mSv and the highest absorbed dose was found in the right temporal squamous region (0.984 mSv). The reduction in ED was found to be 41% for full- and half-rotation angles. Quick/Regular ratios for SNR and CNR were changed between 0.83-0.91.

The magnitude of reduction in ED was found to be higher than the quantitative image quality; however, the impact of this change on diagnosis should be analyzed according to the imaging purpose.

Brain tumor is characterized by its aggressive nature and low survival rate and therefore, it is regarded as one of the deadliest diseases. Thus, misdiagnosis or miss-classification of brain tumors can lead to miss-treatment or incorrect treatment and reduce survival chances. Therefore, there is a need to develop a technique that can identify and detect brain tumors at early stages.

Here, we proposed a framework titled I-Brainer which is an Artificial Intelligence/Internet of Things (AI/IoT)-powered classification of MRI into 4 classes. We employed a Br35H+SARTAJ brain MRI dataset which contains 7023 total images including no tumor, pituitary, meningioma, and glioma. To accurately classify MRI into 4 classes, we developed the LeNet model from scratch, and implemented 2 pre-trained models which include EfficientNet and ResNet-50 as well as feature extraction of these models coupled with 2 Machine Learning (ML) classifiers namely; k-Nearest Neighbours (KNN) and Support Vector Machine (SVM).

Evaluation and comparison of the performance of the 3 models have shown that ResNet-50 achieved the best result in terms of AUC (99%) and ResNet-50-KNN ranked higher in terms of accuracy (94%) on the testing set.

This framework can be harnessed by patients residing in remote areas and as a confirmatory approach for medical experts.

The study aimed to investigate the association of CT-relevant skeletal muscle parameters, such as sarcopenia and myosteatosis, with survival outcomes in patients receiving chemotherapy for unresectable pancreatic ductal adenocarcinoma (PDAC).

In this retrospective analysis, patients who began chemotherapy for unresectable PDAC were included. Sarcopenia and myosteatosis were assessed on pretreatment CT at the L3 level by skeletal muscle index and mean muscle attenuation with predefined cutoff values. The Cox proportional hazards model was used to analyze the factors associated with progression-free survival (PFS) and overall survival (OS).

A total of 150 patients were enrolled. Compared to patients without sarcopenia, patients with sarcopenia had significantly worse PFS (p=0.003) and OS (p<0.001). Patients with myosteatosis had significantly worse PFS (p=0.01) and OS (p=0.002) compared to those without myosteatosis. In multivariate analysis, after adjusting for age, sex, tumor size, location, treatment modality, smoking, drinking, underlying diseases, and partial laboratory tests, sarcopenia remained an independent predictor of PFS (p=0.006) and OS (p<0.001). Myosteatosis remained an independent predictor of OS (p=0.008), but not of PFS.

Sarcopenia and myosteatosis are independent prognostic factors for patients with unresectable pancreatic ductal adenocarcinoma after chemotherapy.

The objective of this study was to comprehensively review the literature on Shear Wave Elastography (SWE), a non-invasive imaging technique prevalent in medical ultrasound. SWE is instrumental in assessing superficial glandular tissues, abdominal organs, tendons, joints, carotid vessels, and peripheral nerve tissues, among others. By employing bibliometric analysis, we aimed to encapsulate the scholarly contributions over the past two decades, identifying key research areas and tracing the evolutionary trajectory of SWE.

For this study, we selected research articles related to SWE published between 2004 and March 2024 from the Web of Science Core Collection (WOSCC). We utilized sophisticated bibliometric tools, such as CiteSpace, VOSviewer, and SCImago Graphica, to analyze the trends in annual publications, contributing countries and institutions, journals, authors, co-cited authors, co-cited references, and keywords.

Our analysis yielded a total of 3606 papers. China emerged as the leading country in terms of publication output, with a strong collaborative relationship with the United States. Sun Yat-Sen University was identified as the institution with the highest number of publications. The keyword “transient elastography” was the most prevalent, with “acoustic radiation force” being a focal point in the initial stages of SWE research. Recently, Contrast-enhanced Ultrasound (CEUS) has emerged as a new research focus, signaling a potential direction for future research and development.

The global research landscape for SWE is projected to expand continuously. Future research is likely to concentrate on the integrated application of SWE and CEUS for diagnostic purposes, along with exploring the clinical utility of multimodal ultrasound that synergistically combines SWE with other ultrasound technologies. This bibliometric research offers a comprehensive overview of the SWE literature, guiding researchers in their pursuit of further exploration and discovery.

Bronchogenic cysts are congenital cystic anomalies of the bronchus that originate from abnormal development of the bronchial tree during the embryonic period. Their common manifestation is a space-occupying lesion in the lungs or mediastinum. Common imaging modalities for detecting bronchogenic cysts include chest X-ray and chest computed tomography (CT) scans.

A 24-year-old female presented with an abnormal space-occupying lesion in the mediastinum detected through imaging examinations. Echocardiography revealed a cystic mass located between the descending aorta and the right pulmonary artery. A CT scan identified a low-density mass with a distinct density relative to adjacent tissues, situated near the left main bronchus. The final diagnosis of a bronchogenic cyst was established following surgical intervention and pathological examination.

Bronchogenic cysts are rare congenital anomalies. Common clinical symptoms include chest pain, cough, and dyspnea. On standard chest radiographs and CT scans, most cysts present as homogenous water-density shadows, with the mediastinum being the most frequently affected location. The diagnosis is confirmed through pathological examination. Surgical intervention remains the most effective treatment method, typically resulting in a favorable prognosis.

The aim of this study is to evaluate the potential of black tea as a negative oral contrast agent in Magnetic Resonance Cholangiopancreatography (MRCP) to improve image quality by reducing gastrointestinal fluid signals.

Retained gastrointestinal fluids can interfere with ductal imaging during MRCP, and suitable oral negative contrast agents are not widely available.

Two types of black tea (Lapsang Souchong and Yinghong NO9) were tested in vitro at different concentrations (3g, 6g, and 9g) to assess their T2 signal suppression. The tea with the best signal suppression was selected for a prospective clinical study involving 51 patients undergoing MRCP. Signal intensity, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured before and after black tea administration.

In vitro experiments showed that the 9g concentration of Lapsang Souchong tea provided the most effective T2 signal suppression, with manganese and iron ion concentrations of 4.705 mg/L and 0.040 mg/L, respectively. In the clinical study, paired T-tests revealed a significant decrease in gastrointestinal fluid signals after black tea administration, with a mean signal intensity reduction in the stomach and duodenum. The SNR in the duodenal bulb increased significantly, while no significant differences were observed in SNR and CNR in other gastrointestinal segments.

Black tea, rich in iron and manganese, effectively reduces gastrointestinal fluid signals, potentially enhancing MRCP image quality. Further research is warranted to explore its clinical application.

The global incidence of lung cancer highlights the need for improved assessment of nodule characteristics to enhance early detection of lung adenocarcinoma presenting as ground-glass nodules (GGNs). This study investigated the applicability of radiomics features of vascular structures within GGNs for predicting invasiveness of GGNs.

In total, 165 pathologically confirmed pulmonary GGNs were retrospectively analyzed. The nodules were classified into preinvasive and invasive groups and randomly categorized into training and validation sets in a 7:3 ratio. Four models were constructed and evaluated: radiomics-GGN, radiomics-vascular, clinical-radiomics-GGN, and clinical-radiomics-vascular. The predictive performance of these models was assessed using receiver operating characteristic curves, decision curve analysis, calibration curves, and DeLong’s test.

Significant differences and density were observed between the preinvasive and invasive groups in terms of age, nodule length, average diameter, morphology, lobulation sign (P = 0.006, 0.038, 0.046, 0.049, 0.002 and0.008 respectively). In the radiomics-GGN model, the support vector machine (SVM) approach outperformed logistic regression (LR), achieving an area under the curve (AUC) of 0.958 in the training set and 0.763 in the validation set. Similarly, in the radiomics-vascular model, the SVM approach outperformed LR. Furthermore, the clinical-radiomics-vascular model demonstrated superior predictive performance compared with the clinical-radiomics-GGN model, with an AUC of 0.918 in the training set and 0.864 in the validation set. DeLong’s test indicated significant differences in predicting the invasiveness of pulmonary nodules between the clinical-radiomics-vascular model and the clinical-radiomics-GGN model, both in the training and validation sets (P < 0.01).

The radiomics models based on internal vascular structures of GGNs outperformed those based on GGNs alone, suggesting that incorporating vascular radiomics analysis can improve the noninvasive assessment of GGN invasiveness, thereby aiding in clinical decision-making and guiding biopsy selection and treatment planning.

Complications of Post-Reversible Encephalopathy Syndrome (PRES) following interventional embolization of aneurysms are rarely reported, and PRES disease can be reduced or resolved through prompt and aggressive treatment, resulting in minimal or no residual neurological deficits.

A 51-year-old female patient with an aneurysm in the pericallosal segment of the left anterior cerebral artery experienced prolonged status epilepticus following aneurysm embolization, attributed to PRES. The diagnosis of PRES was confirmed by symptom improvement and resolution of lesions on imaging studies after one month of treatment involving blood pressure management and prevention of cerebral vasospasm. At the 7-month post-discharge follow-up, the patient's examination indexes were normal without any residual neurological deficits.

This case underscores the importance of promptly identifying and diagnosing PRES, as timely intervention can prevent permanent neurological deficits and mitigate the risk of more severe outcomes.

This study aimed to develop and assess the performance of a YOLOv8 algorithm-aided detection model for identifying rib fractures on multiplane reconstruction (MPR) images, addressing the limitations of current AI models and the labor-intensive nature of manual diagnosis.

Ethical approval was obtained, and a dataset comprising 624 MPR images, confirmed by CT, was collected from three regions of Tongji Hospital between May 2020 and May 2023. The images were categorized into training, validation, and external test sets. A musculoskeletal radiologist labeled the images, and a YOLOV8n model was trained and validated using these datasets. The performance metrics, including sensitivity, specificity, accuracy, precision, recall, and F1 score, were calculated.

The refined YOLO model demonstrated high diagnostic accuracy, with sensitivity, specificity, and accuracy rates of 96%, 97%, and 97%, respectively. The AI model significantly outperformed the radiologist in terms of diagnostic speed, with an average interpretation time of 2.02 seconds for 144 images compared to 288 seconds required by the radiologist.

The YOLOv8 algorithm shows promise in expediting the diagnosis of rib fractures on MPR images with high accuracy, potentially improving clinical efficiency and reducing the workload for radiologists. Future work will focus on enhancing the model with more feature learning capabilities and integrating it into the PACS system for human-computer interaction.

Recurrent pleomorphic adenoma (PA) in the submandibular gland is a rare tumor that may be misdiagnosed as an inflammatory lesion. The imaging manifestations of the submandibular gland recurrent PA are unclear, with only three case reports reporting CT and MRI imaging, respectively. Our report is the first case report that comprehensively describes the imaging manifestations of recurrent PA in the submandibular gland.

A 28-year-old woman had a right submandibular gland pleomorphic adenoma that recurred 5 years after resection and gradually grew larger. She had no special discomfort and was diagnosed with a recurrence of pleomorphic adenoma. The patient underwent CT and MRI examinations and tumor resection, and postoperative pathology showed tumor recurrence.

This case report provides substantial and comprehensive CT and MRI data, which is conducive to the diagnosis of the recurrence of submandibular gland pleomorphic adenoma and the avoidance of misdiagnosis to the greatest extent possible.

The study aimed to evaluate the clinical efficacy of CT-guided radiofrequency ablation in conjunction with targeted therapy in lung cancer patients.

We retrospectively analyzed 80 lung cancer patients. They were stratified into the Observation Group (OG; n=40, treated with CT-guided radiofrequency ablation in conjunction with targeted therapy) and the Control Group (CG; n=40, treated solely with targeted therapy).

The Overall Response Rate (ORR) and Disease Control Rate (DCR) in the OG group (70.00%, 95.00%) were significantly higher than those in the CG group (57.50%, 87.50%). After 6 weeks of treatment, the OG group had significantly lower levels of SCC, CEA, and CA125, higher CD4+ levels, and lower CD8+ levels, compared to the CG group. The 24-month follow-up survival rate of the OG group (47.50%) was significantly higher than that of the CG group (27.50%).

CT-guided radiofrequency ablation and targeted therapy have been proven effective in treating lung cancer.

There remains a lack of methods to accurately assess the efficacy of neoadjuvant chemoradiotherapy for locally advanced rectal cancer.

This study aimed to investigate the value of shear-wave elastography in evaluating the treatment response to neoadjuvant chemoradiotherapy for locally advanced rectal cancer.

This prospective observational study enrolled 275 patients with locally advanced rectal cancer who received neoadjuvant chemoradiotherapy during September 2021–March 2023. All patients underwent endorectal ultrasound and shear-wave elastography examination before total mesorectal excision. Clinical baseline data, endorectal ultrasound, and shear-wave elastography examination data were collected from all patients. The independent predictors of complete response were analyzed and screened, followed by the establishment of a logistic regression model. The diagnostic efficacy of the model was compared with that of radiologists.

The results of binary multivariate logistic regression suggested that the mean elastography value of the tumor lesion acted as an independent predictor for the diagnosis of complete response [OR: 0.894 (0.816, 0.981)]. The optimal cutoff value was 14.6 kPa. The area under the receiver operating characteristic curve of the model for predicting complete response in the training and test cohorts was 0.850 and 0.824, respectively. The diagnostic accuracy of the model was significantly higher than that of radiologists (P < 0.001).

Shear-wave elastography can be used as a feasible method to evaluate the complete response of locally advanced rectal cancer after neoadjuvant chemoradiotherapy.

Glaucoma is a leading cause of irreversible visual impairment and blindness worldwide, primarily linked to increased intraocular pressure (IOP). Early detection is essential to prevent further visual impairment, yet the manual diagnosis of retinal fundus images (RFIs) is both time-consuming and inefficient. Although automated methods for glaucoma detection (GD) exist, they often rely on individual models with manually optimized hyperparameters. This study aims to address these limitations by proposing an ensemble-based approach that integrates multiple deep learning (DL) models with automated hyperparameter optimization, with the goal of improving diagnostic accuracy and enhancing model generalization for practical clinical applications.

The RFIs used in this study were sourced from two publicly available datasets (ACRIMA and ORIGA), consisting of a total of 1,355 images for GD. Our method combines a custom Multi-branch convolutional neural network (CNN), pretrained MobileNet, and DenseNet201 to extract complementary features from RFIs. Moreover, to optimize model performance, we apply Bayesian Optimization (BO) for automated hyperparameter tuning, eliminating the need for manual adjustments. The predictions from these models are then combined using a Dirichlet-based Weighted Average Ensemble (Dirichlet-WAE), which adaptively adjusts the weight of each model based on its performance.

The proposed ensemble model demonstrated state-of-the-art performance, achieving an accuracy (ACC) of 95.09%, precision (PREC) of 95.51%, sensitivity (SEN) of 94.55%, an F1-score (F1) of 94.94%, and an area under the curve (AUC) of 0.9854. The innovative Dirichlet-based WAE substantially reduced the false positive rate, enhancing diagnostic reliability for GD. When compared to individual models, the ensemble method consistently outperformed across all evaluation metrics, underscoring its robustness and potential scalability for clinical applications.

The integration of ensemble learning (EL) and advanced optimization techniques significantly improved the ACC of GD in RFIs. The enhanced WAE method proved to be a critical factor in achieving well-balanced and highly accurate diagnostic performance, underscoring the importance of EL in medical diagnosis.

Lipomas represent a rare benign etiology of intussusception in adults, affecting both the small intestine and the colon. Diagnosing intussusception in adults can be challenging, and there are no reports on the use of positron emission tomography/CT (PET/CT) in the diagnosis of lipoma-induced intussusception. This study aimed to preliminarily explore the potential diagnostic utility of ¹⁸F-FDG PET/CT in the diagnosis of intussusception caused by lipomas.

We conducted a retrospective review of the clinical characteristics and imaging findings of three patients diagnosed with lipoma-induced intussusception using ¹⁸F-FDG PET/CT from 2019 to 2023 at our hospital.

The three cases presented with diverse clinical presentations and were diagnosed based on PET/CT imaging. Surgical confirmation was obtained in two cases. Lipomas were identified in both the small intestine and the colon, with one case displaying increased metabolic activity on FDG uptake, suggesting a possible link between FDG uptake and clinical severity.

Lipoma is a benign cause of intussusception that can occur in both the small intestine and the colon. The symptoms of adult intussusception are often atypical and variable. Imaging modalities, particularly PET/CT, are instrumental in diagnosing intussusception due to lipomas, differentiating between benign and malignant causes, and assessing the severity to inform treatment strategies.

MRI could be considered as a non-destructive disease diagnosis procedure, this procedure does not allow directly molecular types of cancer. Herein, we aimed to evaluate the correlation of breast MRI background parenchymal enhancement (BPE) and fibroglandular tissue (FGT) with the molecular subtypes and immunohistochemical markers of breast cancer.

This was a single-cross-sectional retrospective study.Fifty-six patients diagnosed with unilateral breast cancer who underwent breast MRI scans before needle biopsy or surgery were selected. The relationship between qualitative and quantitative BPE/FGT ratios and the expression of breast cancer molecular subtypes and immunohistochemical markers were evaluated in patients with breast cancer.

Quantitative BPE (BPE%) of luminal A and luminal B was significantly lower than that of triple-negative breast cancer. There was no significant difference in the qualitative BPE/FGT between the different breast cancer subtypes. The quantitative BPE (BPE%) of estrogen receptor (ER)-negative tumors was higher than that of the ER-positive tumors, and the expression of FGT%, BPE%, and other immunohistochemical markers (human epidermal growth factor receptor-2(HER-2), progesterone receptor (PR), and Ki-67) were not significantly different. The proportion of high BPE distribution in HER-2 positive tumors was higher than that in the HER-2 negative group; however, there was no significant difference in the expression of qualitative BPE/FGT and other immunohistochemical markers (ER, PR, and Ki-67).

There were significant differences in the levels of BPE among the different molecular subtypes. Therefore, BPE may be a potential imaging biomarker for the diagnosis of the molecular subtypes of breast cancer.

Although the “conventional” autopsy is still considered the “gold standard” for the definition of the cause of death, an emerging interest in non-invasive cadaveric investigations is spreading. Among all these, the application of post-mortem magnetic resonance imaging of the heart is increasingly gaining ground in the study of sudden cardiac death.

Using the diffusion tensor imaging sequence, the present study aimed to demonstrate how through the fractional anisotropy value it is possible to qualitatively and quantitatively define sudden cardiac death, particularly in cases of sudden arrhythmic death syndrome. Four hearts were collected for the present pilot study: the first from a subject who died from a brain injury caused by a gunshot, and the other three hearts from subjects who died of sudden cardiac death. In all cases examined, the extracted hearts were hung inside a container containing 10% formalin solution and placed inside a 1.5T scanner with a 16-channel chest coil. Then, the cardiac diffusion tensor imaging sequence was performed and the quantitative maps of fractional anisotropy and apparent diffusion coefficient were obtained. After imaging analysis, the samples were processed, paraffin-embedded, and stained with hematoxylin and eosin and trichrome staining. Cases B, C, and D showed lower fractional anisotropy values than non-pathological one.

Histological investigation revealed extensive areas of fibrosis and foci of contraction band necrosis in heart B, myofiber disarray and interstitial fibrosis in heart C, and findings consistent with atonic death in heart D.

The study aimed to demonstrate that in cases of sudden cardiac death, lower fractional anisotropy values, as already observed in clinical trials, are associated with arrhythmic heart disease or myocardial infarction. Quantitative, appreciable, and reproducible data could support such diagnoses.

In this case series, we present two male cases with renal parenchymal perforation without perirenal hematoma after D-J ureteral stent insertion, one with nutcracker renal vein syndrome. Our study provides new and important contributions to the field of science regarding what to consider during D-J stent insertion in similar cases and in patients with obstruction in the urinary collecting system for more than 2 months.

Two patients, 30 and 37 years old, who were inserted a D-J catheter after endoscopic ureteral stone treatment, suffered from severe ipsilateral flank pain and hematuria after the operation. The Kidney Urine Bladder (KUB) radiography showed that the proximal part of the D-J stent was protruding from the upper calyx and parenchyma of the kidney in both patients. One of the patients had an ipsilateral nutcracker renal vein syndrome, and the clinical progression was more severe. In both cases, conventional follow-up with bed rest, nonsteroidal anti-inflammatory (NSAI) therapy, intravenous (IV) fluid infusion, and anti-biotherapy after the D-J stent reposition was sufficient. The patients had no clinical problems during the next outpatient clinic visits.

Double-j (D-J) ureteral stent insertion procedure may cause many life-threatening complications, from subcapsular hematoma to pulmonary embolism. In this case series, conventional follow-up was sufficient for the treatment of patients with renal parenchymal damage without perirenal hematoma due to D-J stent insertion, including nutcracker renal vein syndrome cases. More care should be taken when placing D-J stents, especially in patients with obstruction in the urinary collecting system for more than 2 months and with nutcracker renal vein syndrome. In these patients, the soft proximal end of the guidewire should not be pushed and forced too hard to the upper part of the kidney and upper collecting system. Additionally, the D-J stent placement procedure should be performed under fluoroscopy as much as possible to avoid complications.

The purpose of this study was to investigate the morphology and distribution characteristics of fat globules in osteomyelitis on magnetic resonance imaging (MRI).

Patients with pathologically-confirmed osteomyelitis and MRI scans were retrospectively enrolled, and fat globules on the MRI images were analyzed.

Among 103 patients with non-traumatic osteomyelitis, 75 were fat globule negative and 28 were positive. There was no statistically significant difference in age and gender between patients with and without fat globules (p>0.05). The inflammatory indicators (CRP, ESR, WBC, and NEUT) in the fat globule positive group were significantly higher (p<0.05) than those in the negative group. The lesions were mainly located in the long bones of the limbs in patients with positive fat globules. Twenty-eight patients (27.2% or 28/103) were detected to have fat globules on MRI images, including 20 males (71%) and 8 females (29%) aged 5-64 years (mean 16 years). The time from onset to MRI examination was 8 days to 4 months. The location of fat globules was in the tibia in 10 patients (35.7%), femur in 8 (28.6%), humerus in 4 (14.3%), radius in 2 (7.1%), ulna in 1 (3.6%), calcaneus in 1 (3.6%), sacrum in 1 (3.6%), and fibula in 1 patient (3.6%). On MRI imaging, 28 cases (100%) showed widely distributed patches or tortuous and sinuous abnormal signals in the bone marrow. In 25 cases (89.2%), a grid-like abnormal signal was found in the subcutaneous soft tissue. In 21 patients (75%), pus was found in the adjacent extraosseous soft tissues. Among 28 patients with fat globules, 17 patients (60.7%) had fat globules only in the adjacent extraosseous soft tissue, 6 patients (21.4%) had only intraosseous fat globules (including 5 cases with halo signs around the fat globules and 1 case (3.6%) with fat globules located at the edge of the pus cavity inside the bone without a halo sign), and 5 patients (17.8%) had both intraosseous and extraosseous fat globules. Of 6 patients (21.4% or 6/28) with liquid levels, the liquid level appeared outside the bone.

The appearance of fat globules on MRI in patients with osteomyelitis indicates severe infection. Fat globules of osteomyelitis may present with diverse shapes inside and outside the bone marrow as one of the MRI signs of osteomyelitis, with a probability of approximately 27.2%. They have high specificity in diagnosing osteomyelitis and can be used for diagnosis and differential diagnosis.

Delphian lymph node (DLN) metastasis is common in papillary thyroid cancer (PTC). However, few studies have specifically investigated the clinicopathologic characteristics of DLN metastasis in PTC. This study aimed to examine the incidence, risk factors, and predictive value of DLN in papillary thyroid carcinoma.

In the present study, the clinicopathologic features and metastatic risks were statistically analyzed by reviewing 1837 patients with papillary thyroid carcinoma who underwent initial surgery in our department between January, 2022 and July, 2024.

Among the 1837 patients included in the study, DLN was detected in 925 patients (50.3%), of which 409 patients (22.3%) had confirmed DLN metastasis. In univariate analysis, DLN metastasis was correlated with age (≥55 years), bilateral cancer, multifocality, tumor location (isthmus cancer), central lymph node metastasis (CLNM), and lateral lymph node metastasis (LLNM). However, it was not correlated with gender distribution, tumor size, thyroiditis, thyroid-stimulating hormone (TSH) level, and BRAF mutation. Multivariate analysis showed that CLNM (p=0.03), LLNM (p=0.025), bilateral cancer, and tumor location (p=0.012) were independent risk factors for DLN metastasis. DLN involvement was mildly predictive of CLNM (sensitivity=29.76%, specificity=87.06%, positive predictive values=74.08%, negative predictive values=49. 93%, positive likelihood ratio=2.30, negative likelihood ratio=0.81) and moderately predictive of LLNM (sensitivity=49.36%, specificity=85.01%, positive predictive values=46.94%, negative predictive values=86.20%, positive likelihood ratio=3.29, negative likelihood ratio=0.60).

Bilateral cancer, CLNM, LLNM, and isthmus cancer were independent risk factors for DLN metastasis. DLN metastasis could be used as a predictor for central and lateral lymph node metastasis. Positive DLN should be a warning signal to carefully evaluate central and lateral lymph nodes during thyroidectomy.

This study aimed to determine the relationship between Radiation-Induced Lung Injury (RILI) and the clinical outcome of Non-Small Cell Lung Cancer (NSCLC) following Stereotactic Ablative Radiotherapy (SABR).

Clinical data and follow-up CT scanning of 101 patients with early NSCLC who received SABR treatment from January 2012 to December 2018 were retrospectively collected, and the Progression Free Survival (PFS) was calculated. CT features of peritumoral RILI were observed by 3 radiologists, each with 10 to 15 years of experience, based on consensus among 3 radiologists and divided into 3 types. Type 1: Diffuse consolidation surrounding the tumor, including the tumor boundary. Type 2: Ground Glass Opacities (GGOs) covering more than 180 degrees around the tumor. Type 3: GGOs surrounding the tumor but covering less than 180 degrees. Log-rank test was used to analyze the correlation between the classification of radiation-induced lung injury CT findings and PFS. Independent predictors of PFS rate were analyzed by COX multivariate regression.

The 5-year PFS rates based on RILI types observed at 6-8 months post-SABR were: Type 1 = 69.5%, Type 2 = 50.9%, and Type 3 = 36.1%. A statistically significant difference was observed among the three RILI types (p=0.025). COX multivariate regression analysis showed that RILI were independent factors influencing PFS (at 6-8 months follow-up after radiotherapy (p=0.041).

Patients with more extensive and denser RILI tend to have a longer PFS. Data from our cohort study indicate that the 6- 8 months post-SABR period represents the optimal follow-up window, as evidenced by significant progression-free survival rate dynamics during this interval (HR = 1.5, 95% CI 1.0-2.2, p < 0.05).

This study seeks to assess vasculogenic mimicry (VM) occurrence in lung adenocarcinoma (LUAD) by delineating intratumoral and peritumoral characteristics using preoperative CT-based radiomics and a nomogram for enhanced precision.

Our retrospective analysis enrolled 150 LUAD patients, ascertained their VM status, and stratified them randomly into development (n=105) and validation cohorts. We extracted radiomics features from intra- and peritumoral zones, delineating 3, 5, and 7mm expansions on thin-section chest CT images. We formulated logistic models encompassing a clinical model (CM), intratumoral radiomics model (TRM), peritumoral radiomics models at 3, 5, and 7 mm (PRMs), and a composite model integrating both intra- and peritumoral zones (CRM). A radiomics nomogram model (RNM) was devised, amalgamating the Rad-scores from intra- and peritumoral regions with clinical-radiological traits to forecast VM. The models' efficacy was gauged via the receiver operating characteristic (ROC) curve analysis, calibration assessment, and decision curve analysis (DCA).

The CRM outperformed its counterparts, the TRM, PRM_3mm, PRM_5mm, and PRM_7mm models, with AUCs reaching 0.859 and 0.860 in the development and validation cohorts. Within the CM, tumor size and spiculation emerged as significant predictive covariates. The RNM, integrating independent predictors with the CRM-Rad-score, demonstrated clinical utility, achieving AUCs of 0.903 and 0.931 in the respective cohorts.

Our findings underscore the potential of CT-based radiomics characteristics derived from intratumoral and peritumoral regions to assess VM presence in LUAD patients. Combining radiomics signatures with clinicoradiological parameters within a nomogram framework significantly enhances predictive accuracy.

This study aimed to assess the hemodynamic changes in the vena cava and predict the likelihood of Cardiac Remodeling (CR) and Myocardial Fibrosis (MF) in athletes utilizing four-dimensional (4D) parameters.

A total of 108 athletes and 29 healthy sedentary controls were prospectively recruited and underwent Cardiac Magnetic Resonance (CMR) scanning. The 4D flow parameters, including both general and advanced parameters of four planes for the Superior Vena Cava (SVC) and Inferior Vena Cava (IVC) (sheets 1-4), were measured and compared between the different groups. Four machine learning models were employed to predict the occurrence of CR and/or MF.

Most general 4D flow parameters related to VC were increased in athletes and positive athletes compared to controls (p < 0.05). Gradient Boosting Machine (GBM) was the most effective model in sheet 2 of SVC, with the area under the curve values of 0.891, accuracy of 85.2%, sensitivity of 84.6%, and specificity of 85.4%. The top five predictors in descending order were as follows: net positive volume, forward volume, waist circumference, body weight, and body surface area.

Physical activity can induce a high flow state in the vena cava. CR and/or MF may elevate the peak velocity and maximum pressure gradient of the IVC. This study successfully constructed a GBM model with high efficacy for predicting CR and/or MF. This model may provide guidance on the frequency of follow-up and the development of appropriate exercise plans for athletes.

Fibrosing mediastinitis (FM) is a rare and benign disease affecting the mediastinum and often causes pulmonary hypertension (PH). Timely diagnosis of PH caused by FM is clinically important to mitigate complications such as right heart failure in affected individuals. This retrospective study aimed to analyze the CT imaging characteristics of tuberculosis (TB) related FM in patients with (TB). Additionally, the study investigates the underlying reasons contributing to the manifestation of symptoms.

From April 2007 to October 2020, high-resolution CT (HRCT) and dual-phase CT pulmonary angiography images of 64 patients with suspected FM diagnosed with PH at a tertiary hospital were examined. The imaging characteristics of these CT scans were analyzed, with a specific focus on the TB-FM involvement of the pulmonary veins, pulmonary arteries, and bronchi (down to the segment level).

HRCT imaging revealed that fibrous tissue inside the mediastinum exhibited minimal or negligible reinforcement in TB-FM and diffuse fibrous infiltration in the mediastinum and hilar areas. Notably, segmental bronchial and pulmonary artery stenosis are more pronounced and frequently co-occurring than lobe-level stenosis. Pulmonary venous stenosis developed outside the pericardium, whereas pulmonary artery stenosis occurred outside the mediastinal pleura. Furthermore, no isolated FM involvement in pulmonary veins was noticed in this cohort.

HRCT imaging of TB-related FM presents unique features in certain regions of the bronchi, pulmonary veins, and pulmonary arteries. Thus, it is imperative to accurately identify fibrous tissue involvement in mediastinal lesions for proper diagnosis and management of TB-FM.

The aim of this study was to develop and validate predictive models for perineural invasion (PNI) in gastric cancer (GC) using clinical factors and radiomics features derived from contrast-enhanced computed tomography (CE-CT) scans and to compare the performance of these models.

This study included 205 GC patients, who were randomly divided into a training set (n=143) and a validation set (n=62) in a 7:3 ratio. Optimal radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. A radiomics model was constructed utilizing the optimal among five machine learning filters, and a radiomics score (rad-score) was computed for each participant. A clinical model was built based on clinical factors identified through multivariate logistic regression. Independent clinical factors were combined with the rad-score to create a combined radiomics nomogram. The discrimination ability of the models was evaluated by receiver operating characteristic (ROC) curves and the DeLong test.

Independent predictive factors of the clinical model included tumor T stage, N stage, and tumor differentiation, with AUC values of 0.777 and 0.809 in the training and validation sets. The radiomics model was constructed using the support vector machine (SVM) classifier with the best AUC (0.875 in the training set and 0.826 in the validation set). The combined radiomics nomogram, which combines independent clinical predictors and the rad-score, demonstrated better predictive performance (AUC=0.889 in the training set; AUC=0.885 in the validation set).

The nomogram integrating independent clinical predictors and CE-CT radiomics was constructed to predict PNI in GC. This model demonstrated favorable performance and could potentially assist in prognosis evaluation and clinical decision-making for GC patients.

Muscle infiltration of bladder cancer has become the most important index to evaluate its prognosis. Machine learning is expected to accurately identify its muscle infiltration status on images.

This study aimed to establish and validate a machine learning prediction model based on multi-phase contrast-enhanced CT (MCECT) for preoperatively evaluating the muscle-invasive status of bladder cancer.

A retrospective study was conducted on bladder cancer patients who underwent surgical resection and pathological confirmation by MCECT scans. They were randomly divided into training and testing groups at a ratio of 8:2; we used an independent external testing set for verification. The radiomics features of lesions were extracted from MCECT images and radiomics signatures were established by dual sample T-test and least absolute shrinkage selection operator (LASSO) regression. Afterward, four machine learning classifier models were established. The receiver operating characteristic (ROC) curve, calibration, and decision curve analysis were employed to evaluate the efficiency of the model and analyze diagnostic performance using accuracy, precision, sensitivity, specificity, and F1-score.

The best predictive model was found to have logic regression as the classifier. The AUC value was 0.89 (5-fold cross-validation range 0.83-0.96) in the training group, 0.80 in the test group, and 0.87 in the external testing group. In the testing and external testing group, the diagnostic accuracy, precision, sensitivity, specificity, and F1-score were 0.759, 0.826, 0.863, 0.729, 0.785, and 0.794, 0.755, 0.953, 0.720, and 0.809, respectively.

The machine learning model showed good accuracy in predicting the muscle infiltration status of bladder cancer before surgery.

Pneumoconiosis is a preventable occupational lung disease that is caused by the inhalation of inorganic occupational dust. The disease can progress and result in functional impairment. Profusion scores are crucial for the assessment of disease severity.

This study aimed to determine the prevalence of pneumoconiosis cases with a profusion score of 0/1 and explore the correlation between pneumoconiosis and smoking behavior and sectors.

A retrospective cross-sectional study was carried out in this work. Pneumoconiosis was diagnosed with occupational exposure histories and thoracic computed tomography (CT) findings. The study included patients admitted to the occupational diseases outpatient clinic at Eskişehir City Hospital for occupational or pulmonary conditions from January 2021 to July 2023. The collected data included age, sex, smoking status, pack-years, industry of employment, specific departments, occupations, exposure to occupational and non-occupational environmental factors, duration of exposure, laboratory results, pulmonary function test outcomes, thoracic CT findings, and International Classification of Radiographs of Pneumoconiosis score.

Among the 361 patients, 99.4% were male and 62.3% were current smokers. We observed a profusion score of 0/1 in 15% (n = 54) of the cases. Patients with a 0/1 profusion score had better lung function than those with higher scores, with the FEV1/FVC ratio declining as the profusion score increased. Non-smokers with progressive massive fibrosis had significantly lower FEV1/FVC ratios compared to other non-smokers.

In order to avert the progression of early-stage cases, it is significant that we reevaluate occupational health policies and measures, regardless of compensation.

The malignant risk of cystic-solid thyroid nodules may be underestimated in the ultrasound assessment.

This systematic review and meta-analysis aimed to evaluate the value of multimodal ultrasound characteristics in the malignant risk assessment of cystic-solid thyroid nodules.

We conducted a comprehensive search of PubMed, Web of Science, and Cochrane Library databases for studies depicting the ultrasound characteristics of cystic-solid thyroid nodules published prior to October 2023. The Review Manager 5.4 software was utilized to evaluate the ultrasound features suggestive of malignancy and to determine their sensitivity and specificity. Additionally, the Sata 12.0 software was utilized to construct summary receiver operating characteristic curves (SROC), estimate the area under the curve (AUC), and evaluate any potential publication bias.

This review included 16 studies comprising 5,655 cystic-solid thyroid nodules. Nine ultrasound features were identified as risk factors for tumor malignancy. Among the ultrasound features, microcalcification in the solid portion, heterogeneous hypoenhancement on Contrast-Enhanced Ultrasound (CEUS), and sharp angles in the solid portion exhibited higher malignant predictive value in cystic-solid thyroid nodules, with AUC values of 0.91, 0.84, and 0.81, respectively.

Our findings indicate that microcalcification and sharp angles in the solid part of the nodule, along with heterogeneous hypoenhancement on contrast-enhanced ultrasound (CEUS), can better predict malignant cystic-solid thyroid nodules.

The systematic review and meta-analysis was registered prospectively in the International Prospective Register of Systematic Reviews (No. CRD42024602893).

The objective of this research is to enhance pneumonia detection in chest X-rays by leveraging a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) with modified Swin Transformer blocks. This study aims to significantly improve diagnostic accuracy, reduce misclassifications, and provide a robust, deployable solution for underdeveloped regions where access to conventional diagnostics and treatment is limited.

The study developed a hybrid model architecture integrating CNNs with modified Swin Transformer blocks to work seamlessly within the same model. The CNN layers perform initial feature extraction, capturing local patterns within the images. At the same time, the modified Swin Transformer blocks handle long-range dependencies and global context through window-based self-attention mechanisms. Preprocessing steps included resizing images to 224x224 pixels and applying Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance image features. Data augmentation techniques, such as horizontal flipping, rotation, and zooming, were utilized to prevent overfitting and ensure model robustness. Hyperparameter optimization was conducted using Optuna, employing Bayesian optimization (Tree-structured Parzen Estimator) to fine-tune key parameters of both the CNN and Swin Transformer components, ensuring optimal model performance.

The proposed hybrid model was trained and validated on a dataset provided by the Guangzhou Women and Children’s Medical Center. The model achieved an overall accuracy of 98.72% and a loss of 0.064 on an unseen dataset, significantly outperforming a baseline CNN model. Detailed performance metrics indicated a precision of 0.9738 for the normal class and 1.0000 for the pneumonia class, with an overall F1-score of 0.9872. The hybrid model consistently outperformed the CNN model across all performance metrics, demonstrating higher accuracy, precision, recall, and F1-score. Confusion matrices revealed high sensitivity and specificity with minimal misclassifications.

The proposed hybrid CNN-ViT model, which integrates modified Swin Transformer blocks within the CNN architecture, provides a significant advancement in pneumonia detection by effectively capturing both local and global features within chest X-ray images. The modifications to the Swin Transformer blocks enable them to work seamlessly with the CNN layers, enhancing the model’s ability to understand complex visual patterns and dependencies. This results in superior classification performance. The lightweight design of the model eliminates the need for extensive hardware, facilitating easy deployment in resource-constrained settings. This innovative approach not only improves pneumonia diagnosis but also has the potential to enhance patient outcomes and support healthcare providers in underdeveloped regions. Future research will focus on further refining the model architecture, incorporating more advanced image processing techniques, and exploring explainable AI methods to provide deeper insights into the model's decision-making process.

In the dynamic landscape of healthcare, integrating Artificial Intelligence paradigms has become essential for sophisticated brain image analysis, especially in tumor detection. This research addresses the need for heightened learning precision in handling sensitive medical images by introducing the Fragmented Segment Detection Technique.

The ever-evolving healthcare landscape demands advanced methods for brain image analysis, particularly in detecting tumors. This study responds to this need by introducing the Feature Segmentation and Detection Technique (FSDT), a novel approach designed to identify brain tumors precisely using MRI images. The focus is on enhancing detection accuracy, even for diminutive tumors.

The primary objective of this study is to introduce and evaluate the efficacy of FSDT in identifying and sizing brain tumors through advanced medical image analysis. The proposed technique utilizes cross-section segmentation and pixel distribution analysis to improve detection accuracy, particularly in size-based tumor detection scenarios.

The proposed technique commences by fragmenting the input through cross-section segmentation, enabling meticulous separation of pixel distribution in various sections. A Convolutional Neural Network then independently operates sequentially on the minimum and maximum representations. The segmented cross-section feature, exhibiting maximum accuracy, is employed in the neural network training process. Fine-tuning of the neural network optimizes feature distribution and pixel arrangements, specifically in consecutive size-based tumor detection scenarios.

The FSDT employs cross-sectional segmentation and pixel distribution analysis to enhance detection accuracy by leveraging a diverse dataset encompassing central nervous system CNS tumors. Comparative evaluations against existing methods, including ERV-Net, MRCNN, and ENet-B0, reveal FSDT's superiority in accuracy, training rate, analysis ratio, precision, recall, F1-score, and computational efficiency. The proposed technique demonstrates a remarkable 10.45% increase in accuracy, 14.12% in training rate, and a 10.78% reduction in analysis time.

The proposed FSDT emerges as a promising solution for advancing the accurate identification and sizing of brain tumors through cutting-edge medical image analysis. The demonstrated improvements in accuracy, training rate, and analysis time showcase its potential for effective real-world healthcare applications.

The earlier detection of cervical cancer in women patients can save human life. This article proposes a novel methodology for detecting abnormal cervigram images from healthy cervigram images and segments the cancer regions in the abnormal cervigram images using the deep learning method. The conventional deep learning architecture has been modified into the proposed CervixNet architecture to improve the cervical cancer detection rate.

This methodology is constituted of a training and testing process, where the training process generates the training sequences individually for healthy cervigram images and the cancer case cervigram images. The testing process tests the cervigram images into either a healthy or cancer cases using the training sequences generated through the training process. During the testing process of the proposed system, the cancer segmentation algorithm was applied on the abnormal cervigram image to detect and segment the pixels belonging to cancer. Finally, the performance has been carried out on the segmented cancer cervical images for the ground truth images. This proposed methodology has been evaluated on the cervigrams on IMODT and Guanacaste databases. Its performance has been analyzed concerning cancer pixel sensitivity, cancer pixel specificity and cancer pixel accuracy.

This research work obtains 98.69% Cancer Pixel Sensitivity (CPS), 98.76% Cancer Pixel Specificity (CPSP), and 99.27% Cancer Pixel Accuracy (CPA) for the set of cervigram images in the IMODT database. This research work obtains 99.22% CPS, 99.03% CPSP, and 99.01% CPA for the set of cervigram images in Guanacaste database.

These experimental results of the proposed work have been significantly compared with the state-of-the-art methods and show the significance and novelty of the proposed works.