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Purpose. To investigate whether the radiomics analysis of MR imaging in the hepatobiliary phase (HBP) can be used to predict microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC). Method. A total of 130 patients with HCC, including 80 MVI-positive patients and 50 MVI-negative patients, who underwent MR imaging with Gd-EOB-DTPA were enrolled. Least absolute shrinkage and selection operator (LASSO) regression was applied to select radiomics parameters derived from MR images obtained in the HBP 5 min, 10 min, and 15 min images. The selected features at each phase were adopted into support vector machine (SVM) classifiers to establish models. Multiple comparisons of the AUCs at each phase were performed by the Delong test. The decision curve analysis (DCA) was used to analyze the classification of MVI-positive and MVI-negative patients. Results. The most predictive features between MVI-positive and MVI-negative patients included 9, 8, and 14 radiomics parameters on HBP 5 min, 10 min, and 15 min images, respectively. A model incorporating the selected features produced an AUC of 0.685, 0.718, and 0.795 on HBP 5 min, 10 min, and 15 min images, respectively. The predictive model for HBP 5 min, 10 min and 15 min showed no significant difference by the Delong test. DCA indicated that the predictive model for HBP 15 min outperformed the models for HBP 5 min and 10 min. Conclusions. Radiomics parameters in the HBP can be used to predict MVI, with the HBP 15 min model having the best differential diagnosis ability.

Background. The prognosis of IDH1-mutant glioma is significantly better than that of wild-type glioma, and the preoperative identification of IDH mutations in glioma is essential for the formulation of surgical procedures and prognostic assessment. Purpose. To explore the value of a radiomic model based on preoperative-enhanced MR images in the assessment of the IDH1 genotype in high-grade glioma. Materials and Methods. A retrospective analysis was performed on 182 patients with high-grade glioma confirmed by surgical pathology between December 2012 and January 2019 in our hospital with complete preoperative brain-enhanced MR images, including 79 patients with an IDH1 mutation (45 patients with WHO grade III and 34 patients with WHO grade IV) and 103 patients with wild-type IDH1 (33 patients with WHO grade III and 70 patients with WHO grade IV). Patients were divided into a primary dataset and a validation dataset at a ratio of 7 : 3 using a stratified random sampling; radiomic features were extracted using A.K. (Analysis Kit, GE Healthcare) software and were initially reduced using the Kruskal-Wallis and Spearman analyses. Lasso was finally conducted to obtain the optimized subset of the feature to build the radiomic model, and the model was then tested with cross-validation. ROC (receiver operating characteristic curve) analysis was performed to evaluate the performance of the model. Results. The radiomic model showed good discrimination in both the primary dataset ( AUC = 0.87 , 95% CI: 0.754 to 0.855, ACC = 0.798 , sensitivity = 85.5 % , specificity = 75.4 % , positive   predictive   value = 0.734 , and negative   predictive   value = 0.867 ) and the validation dataset ( AUC = 0.86 , 95% CI: 0.690 to 0.913, ACC = 0.789 , sensitivity = 91.3 % , specificity = 69.0 % , positive   predictive   value = 0.700 , and negative   predictive   value = 0.909 ). Conclusion. The radiomic model, based on the preoperative-enhanced MR, can effectively predict the IDH1 genotype in high-grade glioma.

Purpose: To investigate the clinical features, imaging features, and prognosis of mild encephalitis/encephalopathy with a reversible splenial lesion (MERS) in children Methods: The clinical and imaging data of a cohort of 28 children diagnosed as MERS from January 2019 to October 2020 were retrospectively analyzed Results: Of the 28 patients, 17 were males and 11 were females. The onset age ranged from 8 months to 12 years old, with an average age of 4 years and 2 months. All children developed normally before onset, and three of them had a history of febrile convulsion. More than half of the patients (62.9%) had preceding infections of gastrointestinal tract. All the cases developed seizures, and most (71.4%) had more than one time. Other neurological symptoms included dizziness/headache, consciousness disorder, limb weakness, blurred vision, and dysarthria. Cranial magnetic resonance imaging (MRI) showed lesions in the splenium of the corpus callosum in all, extending to other areas of the corpus callosum, bilateral semi-ovoid center, and adjacent periventricular in two cases. The clinical symptoms were relieved after steroids, intravenous immunogloblin, and symptomatic treatment, without abnormal neurodevelopment during the followed-up (2 months-2 years). Complete resolution of the lesions was observed 8-60 days after the initial MRI examinations Conclusion: MERS in children is related to prodromal infection mostly, with a wide spectrum of neurologic symptoms, characteristic MRI manifestations, and good prognosis. K E Y W O R D Schild, magnetic resonance imaging, mild encephalitis/encephalopathy with a reversible splenial lesion (MRES) INTRODUCTIONMild encephalitis/encephalopathy with a reversible splenial lesion (MERS) is a clinico-radiological syndrome first described by Tada et al.

Objectives: The objective of this study was to develop a radiomics nomogram for predicting the meningioma grade based on enhanced T1WI images. Methods: 188 patients with meningioma were analyzed retrospectively. There were 94 high grade meningioma to form high-grade group and 94 low-grade meningioma were selected randomly to form low grade group. Clinical data and MRI features were analyzed and compared. The clinical model was built by using the significant variables. The least absolute shrinkage and selection operator (LASSO) regression was used to select the most valuable radiomics feature. The radiomics signature was built and the Rad-score was calculated. The radiomics nomogram was developed by the significant variables of the clinical factors and Rad-score. The calibration curve and the Hosmer–Lemeshow test were used to evaluate the radiomics nomogram. Different models were compared by Delong test and DCA curve. Results: The sex, size and surrounding invasion were used to build clinical model. The AUC of clinical model was 0.870 (95% CI: 0.782–0.959). Nine features were used to construct the radiomics signature. The AUC of the radiomics signature was 0.885 (95% CI: 0.802–0.968). The AUC of radiomics nomogram was 0.952 (95% CI: 0.904–1). The AUC of radiomics nomogram was higher than that of clinical model and radiomics signature with a significant difference (p＜0.05). The DCA curve showed that the radiomics nomogram had a larger net benefit than the clinical model and radiomics signature. Conclusions: The radiomics nomogram based on enhanced T1WI images for predicting the meningioma grade showed high predictive value and might contribute to the diagnosis and treatment of meningioma. Advances in knowledge: 1. We first constructed a radiomic nomogram to predict the meningioma grade. 2. We compared the results of the clinical model, radiomics signature and radiomics nomogram.