UNASSIGNED: Segmental necrotizing granulomatous neuritis (SNGN) is a rare complication of leprosy involving peripheral nerves. It can appear alone in cases of pure neuritic leprosy or in combination with cutaneous lesions.
UNASSIGNED: A 15-year-old female diagnosed with borderline tuberculoid leprosy who received prior multidrug therapy presented 2 years later with occasional pain and tingling sensations along the inner aspect of her right arm and forearm. Imaging findings suggested SNGN, which was corroborated by cytopathological examination. She was considered relapsed from leprosy, and multi-drug therapy and steroids were started, following which she reported a decrease in the size of the swelling along with no further deterioration of the sensorineural deficit.
UNASSIGNED: SNGN, which is one of the rare complications of leprosy, can create diagnostic dilemmas as its differential diagnoses include reversal reactions, and peripheral nerve tumors (such as schwannoma and neurofibroma), which have been outlined in this article. SNGN is more likely when magnetic resonance imaging (MRI) shows a well-defined ovoid lesion with central necrosis and peripheral rim enhancement.
UNASSIGNED: The incidence of SNGN is on the rise due to multi-drug therapy. In our case, the patient developed SNGN, which was considered a relapse from leprosy, and multi-drug therapy and steroids were started, following which the patient reported a significant reduction in the size of the swelling with no further deterioration of the sensorineural deficit. Hence, an appropriate diagnosis of SNGN through ultrasonography and MRI will lead to favorable outcomes, ultimately benefiting the patient.

A serous cystic tumor is a rare entity that has a benign course. Its imaging characteristics, such as the presence of multiple cysts with or without nodular enhancement, can simulate other cystic or solid lesions of the pancreas. Identification of the enhancing scar with punctate calcifications on computed tomography (CT) or magnetic resonance imaging (MRI) may be a distinctive finding suggesting this diagnosis. Neuroendocrine tumors of the pancreas are a different and also rare entity. In images, they have early arterial enhancement. In MRI, they are hyperintense on T2 and hypointense on T1, with avid contrast enhancement. A case of a patient with two focal lesions in the pancreas is presented and the importance of integrating clinical findings, semiology in diagnostic images and, if applicable, the histopathological result for the optimal management of pancreatic tumors is illustrated, highlighting the crucial role of a radiologist in this process.

Glycogen plays essential roles in glucose metabolism. Imaging glycogen in the liver, the major glycogen reservoir in the body, may shed new light on many metabolic disorders. 13C magnetic resonance spectroscopy (MRS) has become the mainstream method for monitoring glycogen in the body. However, the equipment of special hardware to standard clinical magnetic resonance imaging (MRI) scanners limits its clinical applications. Herein, we utilized endogenous glycogen as a T 2-based relaxation contrast agent for imaging glycogen metabolism in the liver in vivo. The in vitro results demonstrated that the transverse relaxation rate of glycogen strongly correlates with the concentration, pH, and field strength. Based on the Swift-Connick theory, we characterized the exchange property of glycogen and measured the exchange rate of glycogen as 31,847 Hz at 37 °C. Besides, the viscosity and echo spacing showed no apparent effect on the transverse relaxation rate. This unique feature enables visualization of glycogen signaling in vivo through T 2-weighted MRI. Two hours-post intraperitoneal injection of glucagon, a clinical drug to promote glycogenolysis and gluconeogenesis, the signal intensity of the mice\'s liver increased by 1.8 times from the T 2-weighted imaging experiment due to the decomposition of glycogen. This study provides a convenient imaging strategy to non-invasively investigate glycogen metabolism in the liver, which may find clinical applications in metabolic diseases.

This study aimed to develop and validate a bone marrow edema model using a magnetic resonance imaging-based radiomics nomogram for the diagnosis of osteoarthritis. Clinical and magnetic resonance imaging (MRI) data of 302 patients with and without osteoarthritis were retrospectively collected from April 2022 to October 2023 at Longhua Hospital affiliated with the Shanghai University of Traditional Chinese Medicine. The participants were randomly divided into two groups (a training group, n = 211 and a testing group, n = 91). We used logistic regression to analyze clinical characteristics and established a clinical model. Radiomics signatures were developed by extracting radiomic features from the bone marrow edema area using MRI. A nomogram was developed based on the rad-score and clinical characteristics. The diagnostic performance of the three models was compared using the receiver operating characteristic curve and Delong\'s test. The accuracy and clinical application value of the nomogram were evaluated using calibration curve and decision curve analysis. Clinical characteristics such as age, radiographic grading, Western Ontario and McMaster Universities Arthritis Index score, and radiological features were significantly correlated with the diagnosis of osteoarthritis. The Rad score was constructed from 11 radiological features. A clinical model was developed to diagnose osteoarthritis (training group: area under the curve [AUC], 0.819; testing group: AUC, 0.815). Radiomics models were used to effectively diagnose osteoarthritis (training group,: AUC, 0.901; testing group: AUC, 0.841). The nomogram model composed of Rad score and clinical characteristics had better diagnostic performance than a simple clinical model (training group: AUC, 0.906; testing group: AUC, 0.845; p < 0.01). Based on DCA, the nomogram model can provide better diagnostic performance in most cases. In conclusion, the MRI-bone marrow edema-based radiomics-clinical nomogram model showed good performance in diagnosing early osteoarthritis.

We report a case of a Morgagni hernia repaired by primary closure with an extra-abdominal suture. Moreover, we reviewed cases of laparoscopically repaired Morgagni hernia, in which the size of the hernia defect was known, to establish a size criterion for mesh utilization. An 87-year-old woman presented to our hospital with right upper abdominal pain and vomiting. She had no history of abdominal surgery or trauma. Chest radiography and computed tomography (CT) revealed a Morgagni hernia, with the stomach and transverse colon herniated into the right chest cavity. Initially, an endoscopic repair was performed for the herniated stomach due to her age, which was successful. However, she had a recurrence 2 days later, prompting us to perform a semi-emergent laparoscopic surgery. Laparoscopic examination revealed a Morgagni defect, with the omentum, transverse colon, and stomach herniated, with the stomach reduced by pneumoperitoneum. Fortunately, the herniated organs could be easily relocated into the abdomen with no adhesions. The hernia defect measured 6 x 3 cm. We performed primary closure with an extra-abdominal suture. No sac resection was performed. The operation lasted 98 min. Oral intake was initiated on postoperative day 1, and the patient was discharged on postoperative day 3 without complications. Chest radiography and CT scans at 1 month postoperatively showed no recurrence, and the patient remained asymptomatic at the 9-month follow-up examination. According to our review findings, primary closure is an efficient method for small hernia defects (rule of thumb: width, <4 cm; length, <7 cm).

UNASSIGNED: Acute Ischemic Stroke (AIS) remains a leading cause of mortality and disability worldwide. Rapid and precise prognostication of AIS is crucial for optimizing treatment strategies and improving patient outcomes. This study explores the integration of machine learning-derived radiomics signatures from multi-parametric MRI with clinical factors to forecast AIS prognosis.
UNASSIGNED: To develop and validate a nomogram that combines a multi-MRI radiomics signature with clinical factors for predicting the prognosis of AIS.
UNASSIGNED: This retrospective study involved 506 AIS patients from two centers, divided into training (n = 277) and validation (n = 229) cohorts. 4,682 radiomic features were extracted from T1-weighted, T2-weighted, and diffusion-weighted imaging. Logistic regression analysis identified significant clinical risk factors, which, alongside radiomics features, were used to construct a predictive clinical-radiomics nomogram. The model\'s predictive accuracy was evaluated using calibration and ROC curves, focusing on distinguishing between favorable (mRS ≤ 2) and unfavorable (mRS > 2) outcomes.
UNASSIGNED: Key findings highlight coronary heart disease, platelet-to-lymphocyte ratio, uric acid, glucose levels, homocysteine, and radiomics features as independent predictors of AIS outcomes. The clinical-radiomics model achieved a ROC-AUC of 0.940 (95% CI: 0.912-0.969) in the training set and 0.854 (95% CI: 0.781-0.926) in the validation set, underscoring its predictive reliability and clinical utility.
UNASSIGNED: The study underscores the efficacy of the clinical-radiomics model in forecasting AIS prognosis, showcasing the pivotal role of artificial intelligence in fostering personalized treatment plans and enhancing patient care. This innovative approach promises to revolutionize AIS management, offering a significant leap toward more individualized and effective healthcare solutions.

UNASSIGNED: To investigate the prediction of pathologic complete response (pCR) in patients with non-small cell lung cancer (NSCLC) undergoing neoadjuvant immunochemotherapy (NAIC) using quantification of intratumoral heterogeneity from pre-treatment CT image.
UNASSIGNED: This retrospective study included 178 patients with NSCLC who underwent NAIC at 4 different centers. The training set comprised 108 patients from center A, while the external validation set consisted of 70 patients from center B, center C, and center D. The traditional radiomics model was contrasted using radiomics features. The radiomics features of each pixel within the tumor region of interest (ROI) were extracted. The optimal division of tumor subregions was determined using the K-means unsupervised clustering method. The internal tumor heterogeneity habitat model was developed using the habitats features from each tumor sub-region. The LR algorithm was employed in this study to construct a machine learning prediction model. The diagnostic performance of the model was evaluated using criteria such as area under the receiver operating characteristic curve (AUC), accuracy, specificity, sensitivity, positive predictive value (PPV), and negative predictive value (NPV).
UNASSIGNED: In the training cohort, the traditional radiomics model achieved an AUC of 0.778 [95% confidence interval (CI): 0.688-0.868], while the tumor internal heterogeneity habitat model achieved an AUC of 0.861 (95% CI: 0.789-0.932). The tumor internal heterogeneity habitat model exhibits a higher AUC value. It demonstrates an accuracy of 0.815, surpassing the accuracy of 0.685 achieved by traditional radiomics models. In the external validation cohort, the AUC values of the two models were 0.723 (CI: 0.591-0.855) and 0.781 (95% CI: 0.673-0.889), respectively. The habitat model continues to exhibit higher AUC values. In terms of accuracy evaluation, the tumor heterogeneity habitat model outperforms the traditional radiomics model, achieving a score of 0.743 compared to 0.686.
UNASSIGNED: The quantitative analysis of intratumoral heterogeneity using CT to predict pCR in NSCLC patients undergoing NAIC holds the potential to inform clinical decision-making for resectable NSCLC patients, prevent overtreatment, and enable personalized and precise cancer management.

UNASSIGNED: The aim of this study is to develop and verify a magnetic resonance imaging (MRI)-based radiomics model for predicting the microvascular invasion grade (MVI) before surgery in individuals diagnosed with nodular hepatocellular carcinoma (HCC).
UNASSIGNED: A total of 198 patients were included in the study and were randomly stratified into two groups: a training group consisting of 139 patients and a test group comprising 59 patients. The tumor lesion was manually segmented on the largest cross-sectional slice using ITK SNAP, with agreement reached between two radiologists. The selection of radiomics features was carried out using the LASSO (Least Absolute Shrinkage and Selection Operator) algorithm. Radiomics models were then developed through maximum correlation, minimum redundancy, and logistic regression analyses. The performance of the models in predicting MVI grade was assessed using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix.
UNASSIGNED: There were no notable statistical differences in sex, age, BMI (body mass index), tumor size, and location between the training and test groups. The AP and PP radiomic model constructed for predicting MVI grade demonstrated an AUC of 0.83 (0.75-0.88) and 0.73 (0.64-0.80) in the training group and an AUC of 0.74 (0.61-0.85) and 0.62 (0.48-0.74) in test group, respectively. The combined model consists of imaging data and clinical data (age and AFP), achieved an AUC of 0.85 (0.78-0.91) and 0.77 (0.64-0.87) in the training and test groups, respectively.
UNASSIGNED: A radiomics model utilizing-contrast-enhanced MRI demonstrates strong predictive capability for differentiating MVI grades in individuals with nodular HCC. This model could potentially function as a dependable and resilient tool to support hepatologists and radiologists in their preoperative decision-making processes.

We present the echocardiography images in a 6 year old girl who presented with a history of scalp swelling after trivial trauma which was subsequently diagnosed as metastases from primary intracardiac germ cell tumour.

UNASSIGNED: To investigate the central mechanism of lumbar disc herniation in patients with chronic low back pain (LDHCP) using resting-state functional magnetic resonance imaging (rs-fMRI) utilizing the Degree Centrality (DC) method.
UNASSIGNED: Twenty-five LDHCP and twenty-two healthy controls (HCs) were enrolled, and rs-fMRI data from their brains were collected. We compared whole-brain DC values between the LDHCP and HC groups, and examined correlations between DC values within the LDHCP group and the Visual Analogue Score (VAS), Oswestry Dysfunction Index (ODI), and disease duration. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curve analysis.
UNASSIGNED: LDHCP patients exhibited increased DC values in the bilateral cerebellum and brainstem, whereas decreased DC values were noted in the left middle temporal gyrus and right post-central gyrus when compared with HCs. The DC values of the left middle temporal gyrus were positively correlated with VAS (r = 0.416, p = 0.039) and ODI (r = 0.405, p = 0.045), whereas there was no correlation with disease duration (p > 0.05). Other brain regions showed no significant correlations with VAS, ODI, or disease duration (p > 0.05). Furthermore, the results obtained from ROC curve analysis demonstrated that the Area Under the Curve (AUC) for the left middle temporal gyrus was 0.929.
UNASSIGNED: The findings indicated local abnormalities in spontaneous neural activity and functional connectivity in the bilateral cerebellum, bilateral brainstem, left middle temporal gyrus, and right postcentral gyrus among LDHCP patients.