Current Medical Imaging - Current Issue

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.