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

Objective: To identify newly diagnosed patients with drug-resistant epilepsy (DRE) based on radiomics and clinical features.
Methods: 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.
Results: 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.
Significance: This study offers a novel approach for early diagnosis of DRE. Radiomics can provide potential diagnostic and predictive information to support personalized treatment decisions.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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