Current Medical Imaging - Current Issue

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.

Primary cardiac tumors are rare. Most primary cardiac tumors are benign, with approximately 10.83% being malignant. We present a rare case of Primary Cardiac Angiosarcoma (PCA) with multiple metastases diagnosed using multimodality imaging, to enhance the understanding of PCA among clinicians and radiologists.

A 29-year-old woman presented to our hospital with a 2-day history of chest tightness, chest pain, palpitations, and dyspnea after physical activity. Ultrasonography and Computed Tomography (CT) of the heart revealed a mass in the right atrium. Cardiac magnetic resonance imaging suggested either a large cardiac lymphoma or angiosarcoma. The histopathological diagnosis confirmed a cardiac angiosarcoma. Positron Emission Tomography-Computed Tomography (PET/CT) revealed intense 18F-fluorodeoxyglucose (18F-FDG) uptake in the right side of the heart, with a maximum standardized uptake value of 10.9. Three months later, the patient was re-examined using abdominal CT, echocardiography, and PET/CT. PET/CT revealed increased 18F-FDG uptake which had become more extensive, with multifocal metastatic nodules in both the lungs and mediastinum. The patient was lost to follow-up after being discharged on May 1, 2022.

The combined evaluation using multimodality imaging plays a vital role in determining the precise size and localization of the PCA, detecting distant metastases, and assessing patient prognosis.

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.

Primary Squamous Cell Carcinoma of the Liver (PSCCL) is an exceptionally rare clinical entity characterized by diagnostic challenges, aggressive behavior, and poor prognosis. Globally, few studies have investigated PSCCL.

We report the case of a 76-year-old male patient with PSCCL, detailing his clinical presentation and imaging findings, to offer insights into the preoperative diagnosis of this disease. The patient presented with upper abdominal pain that had lasted for over two weeks without any specific triggers. Laboratory tests revealed abnormal liver function. Ultrasound examination showed a large, solid, hypoechoic mass in the right anterior lobe of the liver with heterogeneous internal echoes. Color Doppler imaging detected limited blood flow signals. Contrast-enhanced Computed Tomography (CT) of the whole abdomen revealed a low-density mass with indistinct margins in the right lobe of the liver, showing uneven and progressive peripheral enhancement. Comprehensive whole-body CT, gastroscopy, and liver biopsy were performed, excluding metastatic disease in other organs. Based on the pathological findings from a percutaneous ultrasound-guided liver biopsy, the patient was diagnosed with PSCCL.

PSCCL is a rare malignancy that presents significant diagnostic difficulties, often evading easy identification through clinical and imaging examinations. This case report aims to contribute to improving the preoperative diagnosis of PSCCL.

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.

Colon diseases are major global health issues that often require early detection and correct diagnosis to be effectively treated. Deep learning approaches and recent developments in medical imaging have demonstrated promise in increasing diagnostic accuracy.

This work suggests that a Convolutional Neural Network (CNN) model paired with other models can detect different gastrointestinal (GI) abnormalities or diseases from endoscopic images via the fusion of residual blocks, including alpha dropouts (αDO) and auxiliary fusing layers.

To automatically diagnose colon disorders from medical images, this work explores the use of a fused deeplearning model that incorporates the EfficientNetB0, MobileNetV2, and ResNet50V2 architectures. By integrating these features, the fused model aims to improve the classification accuracy and robustness for various colon diseases. The proposed model incorporates an auxiliary fusion layer and a fusion residual block. By combining diverse features through an auxiliary fusion layer, the network can create more comprehensive and richer representations, capturing intricate patterns that might be missed by single-source processing. The fusion residual block incorporates residual connections, which help mitigate the vanishing gradient problem. By adding the input of the block directly to its output, these connections facilitate better gradient flow during backpropagation, allowing for deeper and more stable training. A wide range of endoscopic images are used to assess the proposed model, offering an accurate depiction of various disease scenarios.

The proposed model, with an auxiliary fusion layer and residual blocks, exhibited an enormous reduction in overfitting and performance saturation. The proposed model achieved an impressive 98.03% training accuracy and 97.90% validation accuracy after evaluation, outperforming the majority of typically trained DCNNs in terms of efficiency and accuracy.

The proposed method developed a lightweight model that correctly identifies disorders of the gastrointestinal (GI) tract by combining advanced techniques, including feature fusion, residual learning, and self-normalization.

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.

This study aimed to explore the diagnostic value of multiorgan (heart, lungs, blood vessels) point-of-care ultrasound (PoCUS) in patients with high-risk and medium-to-low-risk pulmonary embolism (PE).

Clinical data of 92 patients with suspected PE, admitted to Hangzhou TCM Hospital affiliated with Zhejiang Chinese Medical University from July 2021 to June 2023, were retrospectively analyzed. According to hemodynamic status, patients were divided into the high-risk (n=28) and the medium-to-low-risk groups (n=64). Using computed tomography (CT) and pulmonary angiography (CTPA) as the gold standard, all patients underwent multiorgan PoCUS examination. The sensitivity, specificity, and accuracy of different methods for diagnosing PE, as well as the time difference between multiorgan PoCUS examination and CTPA, were compared. Differences in measurement values of relevant indicators in all groups were analyzed.

In the high-risk group of patients, CTPA identified 15 cases of PE. In contrast, the PoCUS examination confirmed PE diagnosis in 14 cases (true positive), while 10 cases were diagnosed as true negative, one case as false negative, and three cases as false positive. In the medium-to-low-risk group, CTPA identified 50 patients with PE, while multiorgan PoCUS confirmed PE diagnosis in 33 cases (true positive), and identified 9 true negative, 17 false negative, and 5 false positive PE cases. Kappa test of the consistency between the results of multiorgan PoCUS and CTPA showed that multiorgan PoCUS had higher sensitivity, negative predictive value, and accuracy in the high-risk group compared to the medium-to-low-risk group (p<0.05). Cohen's Kappa value of the high-risk group was 0.710, indicating moderate consistency between PoCUS and CTPA results, while Cohen's Kappa value of 0.231 for the medium and low-risk group indicated poor consistency. There was a significant difference in ultrasound parameters between the high-risk and the medium-to-low-risk group (p<0.05). The time required for multiorgan PoCUS in both groups was significantly shorter than that for the CTPA. There was no significant difference in the time required for PoCUS between the two groups (p>0.05).

Multiorgan PoCUS has been found to have higher sensitivity and accuracy in diagnosing patients with high-risk PE compared to those with medium-to-low-risk PE, and a shorter imaging time compared to CTPA.

Pneumocephalus and pneumorrhachis are rare postoperative complications, commonly occurring within a few days to months after spinal surgery. They are very rarely reported after thoracic surgeries. This case highlights a unique presentation in the emergency department involving headache and vomiting caused by late complications following thoracic surgery with a titanium rib implant.

A 64-year-old male presented to the emergency department with headache and vomiting without fever since prior 1 week. He had a history of left lower lobectomy and thoracic wall reconstruction with a titanium rib implant 40 days earlier due to epidermoid lung cancer. Computed tomography imaging of head and thorax revealed bilateral pneumocephalus and extensive pneumorrhachis. After removal of the rib implant and dural repair, the patient fully recovered.

This case underscores the importance of early imaging and diagnosis in patients presenting with neurological symptoms following thoracic surgery and emphasizes the need for enhanced monitoring protocols for patients with titanium implants.

Primary thoracic lymphangioma is a rare disease. Most of the previous studies are comprised of individual case reports, with a very limited number of patients included.

This study aims to investigate the chest computed tomography (CT) imaging features and clinical manifestations of thoracic lymphangioma, thereby enhancing our understanding of the condition.

A retrospective analysis was conducted on 62 patients diagnosed with thoracic lymphangioma, comprising 32 males and 30 females. The study focused on analyzing the chest CT imaging features and the clinical manifestations observed in these patients.

The incidence rates of thoracic lymphangioma did not differ significantly between males and females; however, it was more frequently observed in children and adolescents. The most common clinical symptoms included cough, fever, chylothorax, chylous pericardium, and lymphedema. The mediastinum (82.3%) emerged as the most frequent location for thoracic lymphangioma, followed by the chest wall (62.9%), bone (40.3%), and pleura (32.3%). Pulmonary lymphangioma, the least prevalent subtype (19.4%), exhibited a propensity to induce respiratory symptoms, frequently manifesting as a generalized lymphatic anomaly (GLA). Furthermore, elevated levels of D-dimer were detected in 34 patients (54.8%) with thoracic lymphangioma.

Imaging examinations play a crucial role in assisting clinicians in making more accurate early diagnoses of thoracic lymphangioma. They are also helpful for assessing the extent of systemic infiltration and enhancing diagnostic precision. With radiological assessment, clinicians could more readily select appropriate therapeutic treatments and monitor the progression of follow-up care.

This study aims to develop an accurate image registration framework for personalized respiratory motion modeling.

The proposed framework incorporates longitudinal data through a two-stage transfer learning approach. In the first stage, transfer learning is employed on longitudinal data collected from the same device. In the second stage, a personalized model is constructed using the transfer learning approach, reusing the model from the first stage. A novel cross-error function is introduced to guide the customized adaptation stage.

The experiments demonstrate the effectiveness of the proposed framework in respiratory motion modeling. Integrating longitudinal data allows for improved accuracy for personalized respiratory motion modeling.

The study presents a novel approach that incorporates longitudinal data into a two-stage transfer learning process for personalized respiratory motion modeling. The framework demonstrates improved accuracy. The results highlight the potential of leveraging longitudinal data to provide personalized image registration solutions.

The neuroanatomical basis of white matter fiber tracts in gait impairments in individuals suffering from Parkinson’s Disease (PD) is unclear.

Twenty-four individuals living with PD and 29 Healthy Controls (HCs) were included. For each participant, two-shell High Angular Resolution Diffusion Imaging (HARDI) and high-resolution 3D structural images were acquired using the 3T MRI. Diffusion-weighted data preprocessing was performed using the orientation distribution function to trace the main fiber tracts in PD individuals. Clinical characteristics between the two groups were compared, and the correlation between the FA value and behavioral data was analyzed. Quantitative gait and clinical parameters were recorded in PD at ON and OFF states, respectively.

The mean tract-specific FA values of the right Cingulum Cingulate (rCC) were statistically different between the PD group and the HC group (p =0.047). The FA value of 34-58 equidistant nodes in rCC was positively correlated with Mini-Mental State Examination (MMSE) (r=0.527, p=0.024), Berg Balance Scale (BBS)-OFF (r=0.480, p =0.040), and BBS-ON (r=0.528, p =0.024) scores, while it was negatively correlated with the MDS-UPDRS-III-ON score (r=-0.502, p =0.030). Regarding the gait analysis, the FA value was significantly correlated with velocity, cadence, and stride time of the pace and rhythm domains in both ‘ON’ and ‘OFF’ states, respectively (p<0.05).

This study served as an initial exploration to establish that HARDI sequences could be employed as a robust tool for analyzing microstructural alterations in white matter fiber bundles among PD patients, although the sample size was small. We confirmed microstructural integrity impairment of rCC to be significantly associated with both gait and cognitive deficits in patients with PD. Early detection of microstructural changes in rCC and targeted treatment can help improve behavioral disorders. In the future, we intend to further integrate multimodal data with assessments of patient behavior both prior to and following intervention. We will validate our findings within an independent cohort to monitor disease progression and evaluate the efficacy of therapeutic interventions.

The main objective of this work was to investigate the effect of cavum septi pellucidi et vergae (CSPV) on the pathogenesis of ventriculomegaly (VM) cases detected during the fetal period.

The fetuses of 515 mothers who applied to the Department of Radiology between October 2011 and December 2022 and who had undergone fetal magnetic resonance imaging (fMRI) were evaluated retrospectively. 152 fetuses with CSPV were included in the study. The fetuses were separated into the following groups: those with right VM (n = 20), those with left VM (n = 56), and those with bilateral VM (n = 44). Fetuses with CSPV, but without VM (n = 32), were included in the study as the control group. For the group with CSPV, lines were drawn to divide the fetal cranium into two symmetrical parts at the interhemispheric line in the axial and coronal planes. The distances from these lines to the lateral leaves of the CSPV were measured. In addition, measurements of the CSPV (anteroposterior, transverse, and high) were taken. An evaluation of whether that was associated with ventricular width or maternal age and gestational week was conducted.

The left ventricular width was significantly higher in cases where the CSPV deviated more to the right, and the right ventricular width was significantly higher in cases where the CSPV deviated more to the left. When the VM rates in the VM group without CSPV and the VM rates in the VM group with CSPV were compared, the VM rates were found to be significantly higher in those with CSPV.

Fetuses with CSPV should be followed up for the possibility of developing VM. However, it should be remembered that VM may be a variation due to CSPV. There is an inverse relationship between the side where CSPV deviates and the side where VM is observed.

We examined the effectiveness of integrating three-dimensional (3D) visualization reconstruction technology with Problem-Based Learning (PBL) in the clinical teaching of resident physicians focusing on pheochromocytoma.

Fifty resident physicians specializing in urology at Peking Union Medical College Hospital were randomly divided into two groups over the period spanning January 2022 to January 2024: an experimental group and a control group. The experimental group underwent instruction utilizing a pedagogical approach that integrated 3D visualization reconstruction technology with PBL, while the control group used a traditional teaching model. A comparative analysis of examination performance and teaching satisfaction between both groups of resident physicians was conducted to assess the efficacy of the integrated 3D visualization and PBL teaching methods in clinical instruction.

The experimental group demonstrated superior performance in both theoretical assessment and clinical skills evaluation, along with heightened levels of teaching satisfaction compared to the control group, with statistically significant differences (p < 0.05). Additionally, the experimental group exhibited markedly higher scores in both theoretical examinations and practical assessments compared to their counterparts in the control group (p < 0.05). The results of theoretical examinations for the experimental group and the control group were 92.15±3.22 and 81.09±4.46, respectively (< 0.0001). The results of practical examinations for the experimental group and the control group were 90.17±3.48 and 70.75±5.11, respectively (< 0.0001).

In the clinical teaching of training resident physicians specializing in urology for the management of pheochromocytoma, the integration of 3D visualization reconstruction technology with the PBL method significantly enhanced the teaching efficacy, improving both the quality of instruction and the level of satisfaction among participants.

The BraTS Generalizability Across Tumors (BraTS-GoAT) initiative addresses the critical need for robust and generalizable models in brain tumor segmentation. Despite advancements in automated segmentation techniques, the variability in tumor characteristics and imaging modalities across clinical settings presents a significant challenge.

This study aims to develop an advanced CNN-based model for brain tumor segmentation that enhances consistency and utility across diverse clinical environments. The objective is to improve the generalizability of CNN models by applying them to large-scale datasets and integrating robust preprocessing techniques.

The proposed approach involves the application of advanced CNN models to the BraTS 2024 challenge dataset, incorporating preprocessing techniques such as standardization, feature extraction, and segmentation. The model's performance was evaluated based on accuracy, mean Intersection over Union (IOU), average Dice coefficient, Hausdorff 95 score, precision, sensitivity, and specificity.

The model achieved an accuracy of 98.47%, a mean IOU of 0.8185, an average Dice coefficient of 0.7, an average Hausdorff 95 score of 1.66, a precision of 98.55%, a sensitivity of 98.40%, and a specificity of 99.52%. These results demonstrate a significant improvement over the current gold standard in brain tumor segmentation.

The findings of this study contribute to establishing benchmarks for generalizability in medical imaging, promoting the adoption of CNN-based brain tumor segmentation models in diverse clinical environments. This work has the potential to improve outcomes for patients with brain tumors by enhancing the reliability and effectiveness of automated segmentation techniques.

Multiple sclerosis (MS) is one of the most common disabling central nervous system diseases affecting young adults. Magnetic resonance imaging (MRI) is an essential tool for diagnosing and following up multiple sclerosis. Over the years, many MRI techniques have been developed to improve the sensitivity of MS disease detection. In recent years synthetic MRI (sMRI) and quantitative MRI (qMRI) have gained traction in neuroimaging applications, providing more detailed information than traditional acquisition methods. These techniques enable the detection of microstructural changes in the brain with high sensitivity and robustness to inter-scanner and inter-observer variability. This study aims to evaluate the feasibility of using these techniques to avoid administering intravenous gadolinium-based contrast agents (GBCAs) for assessing MS disease activity and monitoring.

Forty-two known MS patients, aged 20 to 45, were scanned as part of their routine follow-up. MAGnetic resonance image Compilation (MAGiC) sequence, an implementation of synthetic MRI, was added to our institute's routine MS protocol to automatically generate quantitative maps of T1, T2, and PD. T1, T2, PD, and apparent diffusion coefficient (ADC) data were collected from regions of interest (ROIs) representing normal-appearing white matter (NAWM), enhancing, and non-enhancing MS lesions. The extracted information was compared, and statistically analyzed, and the sensitivity and specificity were calculated.

The mean R1 (the reciprocal of T1) value of the non-enhancing MS lesions was 0.694 s^-1 (T1=1440 ms), for enhancing lesions 1.015 s^-1 (T1=985ms), and for NAWM 1.514 s^-1 (T1=660ms). For R2 (the reciprocal of T2) values, the mean value was 6.816 s^-1 (T2=146ms) for non-enhancing lesions, 8.944 s⁻¹ (T2=112 ms) for enhancing lesions, and 1.916 s⁻¹ (T2=522 ms) for NAWM. PD values averaged 93.069% for non-enhancing lesions, 82.260% for enhancing lesions, and 67.191% for NAWM. For ADC, the mean value for non-enhancing lesions was 1216.60×10⁻⁶ mm²/s, for enhancing lesions 1016.66×10⁻⁶ mm²/s, and for NAWM 770.51×10⁻⁶ mm²/s.

Our results indicate that enhancing and non-enhancing MS lesions significantly decrease R1 and R2 values. Non-enhancing lesions have significantly lower R1 and R2 values compared to enhancing lesions.

Conversely, PD values are significantly higher in non-enhancing lesions than in enhancing lesions. For ADC, while NAWM has lower values, there was minimal difference between the mean ADC values of enhancing and non-enhancing lesions.

Leptomeningeal enhancement, visible on MRI, can indicate a variety of diseases, both neoplastic and non-neoplastic.

This comprehensive pictorial review aims to equip radiologists and trainees with a thorough understanding of the diverse imaging presentations of leptomeningeal disease.

Drawing from a retrospective analysis of MRI scans conducted between 1 January 2008 and 30 September 2022, at two tertiary teaching hospitals in Singapore, this review covers a wide range of conditions.

The main neoplastic conditions discussed include leptomeningeal carcinomatosis, myelomatosis, schwannoma, CNS lymphoma, and pineal region tumors. Additionally, the review addresses non-neoplastic enhancements such as meningoencephalitis, intracranial hypotension, cerebral ischemia/infarction, epidural lipomatosis, syringomyelia, Sturge-Weber syndrome, and subarachnoid hemorrhage.

By highlighting the imaging features and patterns associated with these conditions, the review underscores the critical role of accurate diagnosis and timely management in improving patient outcomes. Enhanced understanding of these conditions can significantly improve patient outcomes through timely and effective therapeutic interventions.

Studies specifically examining the sonographic features of juvenile fibroadenoma in the pediatric population have not been documented. We aimed to analyze sonograms of juvenile fibroadenoma in children.

Patients aged ≤ 18 years who underwent breast ultrasound examinations at our department and had pathologically proven juvenile fibroadenoma from September 2002 to January 2022 were included in this study. Demographic data, clinical findings, and sonograms were retrospectively analyzed. Patients were further divided into the puberty and post-puberty subgroups, and their results were compared.

A total of 24 girls aged 10-18 years with 27 masses diagnosed as juvenile fibroadenomas were identified. The diameter of the masses averaged 5.8 ± 3.3 cm, with a range of 1.5-13.6 cm. Twenty-one (87.5%) patients had a single mass and 3 had double lesions. Over 80% of the lesions were oval-shaped and encapsulated with circumscribed margins and parallel orientation. All masses showed internal hypoechogenicity, either uniform or heterogeneous. For masses that had a diameter > 5 cm, screening with high-frequency transducers revealed no posterior acoustic features or posterior shadowing. However, these features changed to posterior acoustic enhancement when the masses were re-evaluated using low-frequency transducers. Ultrasonic color Doppler showed blood flow in 24 (88.9%) masses. There were no significant differences in the incidence and sonographic features between the two subgroups.

Most juvenile fibroadenomas in children are oval, circumscribed, encapsulated masses with detectable blood flow. All juvenile fibroadenomas presented in this study exhibit internal hypoechogenicity with no posterior acoustic shadowing detected in any cases. Our findings suggest that screening with low-frequency transducers should be performed for a mass that has a diameter > 5 cm.

Early and timely detection of pulmonary nodules and initiation treatment can substantially improve the survival rate of lung carcinoma. However, current detection methods based on convolutional neural networks (CNNs) cannot easily detect pulmonary nodules owing to low detection accuracy and the difficulty in detecting small-sized pulmonary nodules; meanwhile, more accurate CNN-based models are slow and require high hardware specifications.

The aim of this study is to develop a detection model that achieves both high accuracy and real-time performance, ensuring effective and timely results.

In this study, based on YOLOv5s, a concentrated-comprehensive convolution (C3_ODC) module with multidimensional attention is designed in the convolutional layer of the original backbone network for enhancing the feature-extraction capabilities of the model. Moreover, lightweight convolution is combined with weighted bidirectional feature pyramid networks (BiFPNs) to form a GS-BiFPN structure that enhances the fusion of multiscale features while reducing the number of model parameters. Finally, Focal Loss is combined with the normalized Wasserstein distance (NWD) to optimize the loss function. Focal loss focuses on carcinoma-positive samples to mitigate class imbalance, whereas the NWD enhances the detection performance of small lung nodules.

In comparison experiments against the YOLOv5s, the proposed model improved the average precision by 8.7% and reduced the number of parameters and floating-point operations by 5.4% and 8.2%, respectively, while achieving 116.7 frames per second.

The proposed model balances high detection accuracy against real-time requirements.

Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the medical imaging data and pose risks of data breaches and exploitation; however, federated learning is a decentralized architecture which significantly reduces data privacy concerns.

The federated learning works in a distributed architecture by sending a global model to clients rather than sending the data to the model. The proposed federated deep learning-based FedPneu computer-aided diagnosis model has been implemented in 2, 3, 4, and 5 clients architecture for early pneumonia detection using X-ray images. The key parameters configuration include batch size, learning rate, optimizer, decay, momentum, epochs, rounds, and random-split as 32, 0.0001, SGD, 0.000001, 0.9, 10, 100, and 42, respectively.

The results of the proposed federated deep learning-based FedPneu model have been provided in terms of round-wise accuracy, loss, and computational time. The highest accuracy of 85.632% has been achieved with 2-clients federated deep learning architecture, whereas, 3, 4, and 5 clients architecture achieved 85.536%, 76.112%, and 74.123% accuracies, respectively.

In the proposed privacy-protected federated deep learning-based FedPneu model, the two-client architecture has been resulted as the most optimal framework for pneumonia detection among 3-clients, 4-clients, and 5-clients architecture. The model works in a collaborative and privacy-protected framework with a multi-silo dataset which could be highly beneficial for healthcare departments to maintain patient’s data privacy with improved prediction outcomes.

Understanding brain changes in older patients with depression and their relationship with cognitive abilities may aid in the diagnosis of depression in this population. This study aimed to explore the association between brain lesions and cognitive performance in older patients with depression.

We utilized magnetic resonance imaging data from a previous study, which included older adults with and without depression. Smoothed Regional Homogeneity (ReHo) and local brain Amplitude of Low-frequency Fluctuation (ALFF) values were assessed to examine brain activity.

The analysis revealed decreased ReHo in the left middle temporal gyrus, left middle frontal gyrus, and left precuneus, as well as increased local ALFF in the right middle occipital gyrus, left postcentral gyrus, and right precentral gyrus in older patients with depression. These alterations may contribute to behavioral and cognitive changes. However, no significant relationship was found between ReHo values and Montreal Cognitive Assessment (MoCA) scores. In contrast, increased local ALFF in the left postcentral gyrus and right precentral gyrus was negatively correlated with MoCA scores.

This study demonstrated a significant association between regional brain alterations in patients with depression and cognitive behavior. Thus, this work identified functional brain regions and cognitive performance in older adults with depression, highlighting specific brain regions that play a crucial role in cognitive abilities in this population.

Mucinous Cystadenocarcinoma (MCA) of the breast remains a relatively rare condition, and to date, there is no systematic summary of its imaging manifestations. Therefore, this report presents a detailed account of the diagnosis and treatment of mucinous cystadenocarcinoma in a 40-year-old woman, with a particular focus on imaging findings. Additionally, we conducted a comprehensive literature review on this disease and summarized its key imaging features. This manuscript provides valuable insights and methodologies for the accurate diagnosis of mucinous cystadenocarcinoma.

We report a 40-year-old premenopausal woman who discovered multiple cysts in her left breast five years ago. Over the past two years, the size of these tumors has increased. Ultrasound examination indicated that the cysts had grown to 27 x 17mm. Following a puncture, the cysts were confirmed to be benign and were not monitored regularly. A year later, the patient's mass in the left breast increased, and an ultrasound exam indicated a suspicious mixed echo area in the upper outer quadrant, suggestive of a malignant lesion. Mammography showed amorphous suspicious calcifications in the lesion area, distributed in segments. Contrast-enhanced magnetic resonance imaging displayed non-mass-type enhancement of the lesion, with a dynamic enhanced imaging time-signal intensity curve (TIC) showing a rapidly rising plateau pattern. Postoperative pathology confirmed invasive carcinoma of the left breast along with mucinous cystadenocarcinoma. Four months after surgery, the patient developed multiple abnormal lymph nodes in the left axilla, which were confirmed to be metastasis upon pathology examination. Following radiotherapy, the patient's condition remained stable during the follow-up period.

Most MCA lesions typically exhibit clear borders and irregular edges, with some displaying expansive growth and compression of surrounding tissues. Mammography can reveal calcified components in lesions. Ultrasound often reveals an isoechoic or hypoechoic mass with well-defined borders but irregular edges. Magnetic resonance imaging (MRI) can show clear boundaries and uneven enhancement of the lesions, and the time-intensity curve (TIC) of the mass area often shows an inflow enhancement pattern.

Cervical cancer is a prevalent malignancy among women, often asymptomatic in early stages, complicating detection.

This study aims to investigate innovative techniques for early cervical cancer detection using a novel U-RCNNS model.

Cervical epithelial cell images stained with hematoxylin and eosin (HE) were analyzed using the U-RCNNS model, which integrates U-Net for segmentation and R-CNN for object detection, incorporating dilated convolution techniques.

The U-RCNNS model significantly improved the accuracy of detecting and segmenting cervical cancer cells, with the enhanced Mask R-CNN showing notable advancements over the baseline model.

The U-RCNNS model presents a promising solution for early cervical cancer detection, offering improved accuracy compared to traditional methods and highlighting its potential for clinical application in early diagnosis.

The aim of this study was to evaluate renal artery embolization in patients with spontaneous renal artery bleeding based on detailed angiographic findings and a comprehensive analysis of its efficacy and clinical outcomes.

This retrospective study evaluated the outcomes of renal artery embolization in 18 cases among 15 patients (11 men and 4 women; mean age: 57.9 years) treated for spontaneous renal bleeding at our institution between March 2017 and October 2023. Data derived from abdominal computed tomography (CT) and arteriography were analyzed to assess the effectiveness of embolization.

Most patients had end-stage renal disease or renal atrophy, with common findings on CT scans, including signs of active bleeding in 66.7% (10/15) and hematoma extending to the retroperitoneal space in 53.3% (8/15). Microcoils were commonly used for embolization (n = 10), with a technical success rate of 100% and primary and final clinical success rates of 80% and 100%, respectively. No major complications were reported during the follow-up, and clinical improvement was observed in all patients who underwent total embolization, with few instances of reduced hematoma size and renal atrophy.

Transarterial embolization is safe and effective for controlling spontaneous renal hemorrhage.

Cytomegalovirus (CMV) infection is common in the digestive and central nervous systems and can infect the entire digestive tract from the mouth to the rectum. In immunocompromised patients, CMV infection is prone to develop into CMV disease, especially in Acquired Immune Deficiency Syndrome (AIDS) patients. Severe cases may accelerate the progression of AIDS patients and form systemic CMV infection. Timely diagnosis and treatment are very important for the prognosis of patients.

In this paper, we report a 36-year-old man with a Human Immunodeficiency Virus (HIV) infection complicated with CMV colitis. Three weeks ago, he developed abdominal pain with fresh blood in the stool, accompanied by anal pain. He was found to be HIV positive 8 years ago. An enhanced CT scan showed edema and irregular thickening of the rectal wall, obvious enhancement of the mucosa, and multiple enlarged lymph nodes around. ¹⁸F-FDG PET/CT imaging displayed diffuse rectum wall thickening and increased glucose metabolism, and the SUV max was 12.7. There were multiple enlarged lymph nodes around the rectum, glucose metabolism was increased, and the SUVmax was 4.6.

¹⁸F-FDG-PET imaging technology has potential value in the diagnosis of CMV colitis, especially in immunocompromised patients. Detection of FDG concentrations in the colon wall can help diagnose CMV infection and understand the extent of the lesion, which is essential for the timely initiation of antiviral therapy.

Type 2 diabetes Mellitus (T2DM) increases vulnerability to metabolic dysfunction-associated steatotic liver disease (MASLD). Therefore, this study aims to determine the prevalence and coexistence of MASLD in patients with T2DM using ultrasound.

This cross-sectional retrospective study included 168 patients with T2DM from multiple diabetes clinics in Abha City, Asir region, recruited between August 2023 and December 2023. Adult patients aged 18 and over with T2DM were included, and data was extracted from patient files. All patients were examined by ultrasound to determine the prevalence and coexistence of MASLD in patients with T2DM. Hepatic steatosis on B-mode ultrasound is qualitatively classified on a four-point scale: normal (0), mild (1), moderate (2), and severe (3).

Out of 168 patients, 68.4% were identified with MASLD, mostly with diffuse liver (97.4%) diagnosed through ultrasound. MASLD was significantly higher in individuals with uncontrolled diabetes (72.5%) than those with controlled diabetes (46.2%), with a significant difference (p=0.015) and an odds ratio (OR) of 3.081, indicating uncontrolled diabetics are over three times more likely to develop MASLD. The uncontrolled group had a statistically significant larger liver size than the control group (13.6cm ±1.43 vs. 13.0cm ±1.20, respectively: [p=0.032, 95%CI 0.053-1.12]). Furthermore, a notable association was observed between increased BMI and the prevalence of MASLD in individuals with T2DM. Furthermore, no significant association was found between the duration of diabetes and the severity of MASLD, nor between the grading of MASLD and gender.

This study highlights a crucial association between uncontrolled diabetes and increased MASLD prevalence, emphasizing the importance of diabetes management in reducing MASLD risk.

Primary intracranial Ewing Sarcoma/peripheral Primitive Neuroectodermal Tumor (EWS/pPNET) is exceedingly rare and easy to misdiagnose.

We present a case involving a 23-year-old male who presented with headaches and vomiting. The preoperative brain imaging revealed an irregular mass in the left parietal lobe, initially misdiagnosed as meningioma. However, the surgical specimen was ultimately diagnosed as primary intracranial EWS/pPNET. The patient underwent a total tumor resection, followed by adjuvant chemotherapy and radiotherapy. No recurrence or distant metastasis was observed 18 months after the surgery.

When the imaging features of young patients’ lesions are solid, aggressive, and unevenly enhanced masses, physicians should be aware of the possibility of primary intracranial EWS/pPNET, and if possible, Gross Total Resection (GTR) and intensive chemotherapy and radiotherapy are recommended.

The purpose of this study was to establish the efficacy of ultrasound (US)-guided hydrodilatation of the glenohumeral joint, in conjunction with corticosteroid injection, in alleviating pain and improving shoulder joint adhesion among patients with primary frozen shoulder (FS).

FS, also known as adhesive capsulitis, is a pathological condition characterized by pain and potential functional impairment. The natural progression of FS involves three distinct stages: freezing, frozen, and thawing. Chronic pain in FS patients can lead to aseptic inflammation, thickening of fibroblasts, and an abundance of type I and III collagen fibers in the vicinity of the glenohumeral joint, ligaments, and tendons. This condition significantly impacts patients' quality of life.

A total of 200 FS patients were enrolled in this study. All participants underwent US-guided hydrodilatation of the glenohumeral joint, combined with corticosteroid injection, at our department. Pre- and post-treatment (1 year) ultrasound measurements were recorded for the thickness of the axillary recess capsule (ARC), coracohumeral ligament (CHL), and subacromial bursa. Additionally, the numerical rating scale (NRS) and Constant-Murley score (CMS) were assessed to evaluate pain intensity and shoulder function, respectively.

Prior to the commencement of treatment, the measurements indicated a thickness of 4.8±2.3 mm for the ARC, 4.2±1.7 mm for the CHL, and 3.9±1.4 mm for the subacromial bursa. Additionally, the preoperative assessment using the NRS scale for pain yielded a score of 6.4±2.0, while the CMS score for the joint function was 35.8±8.5. Following one year of treatment, a notable decrease was observed in the thickness of ARC, CHL, and subacromial bursa. Furthermore, significant improvements were recorded in both the pain NRS score and the CMS score.

US-guided hydrodilatation of the glenohumeral joint, in combination with corticosteroid injection, may help improve the symptom and function of FS.