[Study of resting state functional connectivity of the red nucleus and substantia nigra were in normal adult].

OBJECTIVE: To detect the functional networks of the red nucleus and substantia nigra during the resting state in normal subjects with functional magnetic resonance imaging (fMRI). METHODS: Sixteen normal subjects were performed resting state fMRI scanning and susceptibility weighted imaging. The function connectivity networks base on seed regions of the red nucleus and substantia nigra were extracted from low frequency fluctuation signal in fMRI data by using a temporal correlation method. Individual functional maps were entered two-tailed one-sample t test to determine brain regions with significant positive correlation to the seeds. The statistic threshold was set at P < 0.001, cluster size>42 (336 mm(3)), cluster connectivity criterion 5 min with Alphasim correction. RESULTS: Brain regions involved in the functional connectivity network of the red nucleus include: dorsal anterior cingutate, supramarginal gyrus, the ventrolateral and the ventromedial nucleus of the thalamus, globus pallidus, dorsal thalamus, hippocampus, substantia nigra, red nucleus, pons, dentate nucleus, vermis; Brain regions involved in the functional connectivity network of the substantia nigra include: anterior cingutate, supramarginal gyrus, globus pallidus, dorsal thalamus, hippocampus, lobus insularis, substantia nigra, red nucleus, pons, dentate nucleus. The distribution of the networks of the red nucleus and substantia nigra presented symmetrical. Although the functional networks of the red nucleus and substantia nigra over lapped largely with each other, the rubral network was slightly different with the nigral network, witch showed strong correlations with more wide-spread striatum and thalamus areas. CONCLUSION: The functional networks of the red nucleus and substantia nigra reflected strong interplay within the extrapyramidal subcortical system, as well as correlations between some limited cerebral cortices; Functional magnetic resonance imaging is a potential powerful tool to explore the extrapyramidal system.

A Radiomics Nomogram for Classifying Hematoma Entities in Acute Spontaneous Intracerebral Hemorrhage on Non-contrast-Enhanced Computed Tomography.

Aim: To develop and validate a radiomics nomogram on non-contrast-enhanced computed tomography (NECT) for classifying hematoma entities in patients with acute spontaneous intracerebral hemorrhage (ICH). Materials and Methods: One hundred and thirty-five patients with acute intraparenchymal hematomas and baseline NECT scans were retrospectively analyzed, i.e., 52 patients with vascular malformation-related hemorrhage (VMH) and 83 patients with primary intracerebral hemorrhage (PICH). The patients were divided into training and validation cohorts in a 7:3 ratio with a random seed. After extracting the radiomics features of hematomas from baseline NECT, the least absolute shrinkage and selection operator (LASSO) regression was applied to select features and construct the radiomics signature. Multivariate logistic regression analysis was used to determine the independent clinical-radiological risk factors, and a clinical model was constructed. A predictive radiomics nomogram was generated by incorporating radiomics signature and clinical-radiological risk factors. Nomogram performance was assessed in the training cohort and tested in the validation cohort. The capability of models was compared by calibration, discrimination, and clinical benefit. Results: Six features were selected to establish radiomics signature via LASSO regression. The clinical model was constructed with the combination of age [odds ratio (OR): 6.731; 95% confidence interval (CI): 2.209-20.508] and hemorrhage location (OR: 0.089; 95% CI: 0.028-0.281). Radiomics nomogram [area under the curve (AUC), 0.912 and 0.919] that incorporated age, location, and radiomics signature outperformed the clinical model (AUC, 0.816 and 0.779) and signature (AUC, 0.857 and 0.810) in the training cohort and validation cohorts, respectively. Good calibration and clinical benefit of nomogram were achieved in the training and validation cohorts. Conclusion: Non-contrast-enhanced computed tomography-based radiomics nomogram can predict the individualized risk of VMH in patients with acute ICH.

A comparison between deep learning convolutional neural networks and radiologists in the differentiation of benign and malignant thyroid nodules on CT images.

INTRODUCTION: We designed 5 convolutional neural network (CNN) models and ensemble models to differentiate malignant and benign thyroid nodules on CT, and compared the diagnostic performance of CNN models with that of radiologists. MATERIAL AND METHODS: We retrospectively included CT images of 880 patients with 986 thyroid nodules confirmed by surgical pathology between July 2017 and December 2019. Two radiologists retrospectively diagnosed benign and malignant thyroid nodules on CT images in a test set. Five CNNs (ResNet50, DenseNet121, DenseNet169, SE-ResNeXt50, and Xception) were trained-validated and tested using 788 and 198 thyroid nodule CT images, respectively. Then, we selected the 3 models with the best diagnostic performance on the test set for the model ensemble. We then compared the diagnostic performance of 2 radiologists with 5 CNN models and the integrated model. RESULTS: Of the 986 thyroid nodules, 541 were malignant, and 445 were benign. The area under the curves (AUCs) for diagnosing thyroid malignancy was 0.587-0.754 for 2 radiologists. The AUCs for diagnosing thyroid malignancy for the 5 CNN models and ensemble model was 0.901-0.947. There were significant differences in AUC between the radiologists' models and the CNN models (p < 0.05). The ensemble model had the highest AUC value. CONCLUSIONS: Five CNN models and an ensemble model performed better than radiologists in distinguishing malignant thyroid nodules from benign nodules on CT. The diagnostic performance of the ensemble model improved and showed good potential.