Combining MRI radiomics and clinical features for early identification of drug-resistant epilepsy in people with newly diagnosed epilepsy

• Published in: Epilepsy & behavior 2025-01, Vol.162, p.110165, Article 110165
• Main Authors: Yang, Shijun, Chen, Siying, Huang, Yaling, Lu, Yang, Chen, Yi, Ye, Liyun, Liu, Qunhui
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Child; Deep Learning; Drug Resistant Epilepsy - diagnostic imaging; Drug-resistant epilepsy; Female; Humans; Machine Learning; Machine learning classifiers; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Middle Aged; Radiomics; ResNet-18 model; Sensitivity and Specificity; Support Vector Machine; Young Adult
• ISSN: 1525-5050; 1525-5069; 1525-5069
• DOI: 10.1016/j.yebeh.2024.110165

•We identify the radiomic features to predict the residual tumor status in patients with advanced epithelial ovarian cancer.•A total of 10 ultrasonic radiomic variables and 4 clinical features were incorporated into the nomogram model with good performance.•This model had excellent accuracy and clinical practicability in identifying predict the residual tumor status after external validation. To identify newly diagnosed patients with drug-resistant epilepsy (DRE) based on radiomics and clinical features. A radiomics approach was used to combine clinical features with magnetic resonance imaging (MRI) features extracted by the ResNet-18 deep learning model to predict DRE. Three machine learning classifiers were built, and k-fold cross-validation was used to assess the classifier outcomes, and other evaluation metrics of accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) were used to evaluate the performance of these models. One hundred and thirty-four newly diagnosed epilepsy patients with 13 available clinical features and 1394 MRI features extracted by the ResNet-18 model were included in our study. Then three machine learning classifiers were built based on5 clinical features and 8 MRI features, including Support Vector Machine (SVM), Gradient-Boosted Decision Tree (GBDT) and Random Forest. After internally validation, the GBDT model performed the best, with an average accuracy of 0.85 [95% confidence interval (CI) 0.77–0.91], sensitivity of 0.97 [95% CI 0.85–1.00], specificity of 0.96 [95% CI 0.83–1.00], F1 score of 0.81 [95% CI 0.77–0.89], AUC of 0.95 [95% CI 0.82–0.99], and ten-fold cross validation avg score of 0.96 [95% CI 0.89–0.99] in test set. This study offers a novel approach for early diagnosis of DRE. Radiomics can provide potential diagnostic and predictive information to support personalized treatment decisions.