MRI‐Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy from Epilepsy with Generalized Tonic–Clonic Seizures Alone

Abstract

BackgroundThe clinical presentation of juvenile myoclonic epilepsy (JME) and epilepsy with generalized tonic–clonic seizures alone (GTCA) is similar, and MRI scans are often perceptually normal in both conditions making them challenging to differentiate.PurposeTo develop and validate an MRI‐based radiomics model to accurately diagnose JME and GTCA, as well as to classify prognostic groups.Study TypeRetrospective.Population164 patients (127 with JME and 37 with GTCA) patients (age 24.0 ± 9.6; 50% male), divided into training (n = 114) and test (n = 50) sets in a 7:3 ratio with the same proportion of JME and GTCA patients kept in both sets.Field Strength/Sequence3T; 3D T1‐weighted spoiled gradient‐echo.AssessmentA total of 17 region‐of‐interest in the brain were identified as having clinical evidence of association with JME and GTCA, from where 1581 radiomics features were extracted for each subject. Forty‐eight machine‐learning combinations of oversampling, feature selection, and classification algorithms were explored to develop an optimal radiomics model. The performance of the best radiomics models for diagnosis and for classification of the favorable outcome group were evaluated in the test set.Statistical TestsModel performance measured using area under the curve (AUC) of receiver operating characteristic (ROC) curve. Shapley additive explanations (SHAP) analysis to estimate the contribution of each radiomics feature.ResultsThe AUC (95% confidence interval) of the best radiomics models for diagnosis and for classification of favorable outcome group were 0.767 (0.591–0.943) and 0.717 (0.563–0.871), respectively. SHAP analysis revealed that the first‐order and textural features of the caudate, cerebral white matter, thalamus proper, and putamen had the highest importance in the best radiomics model.ConclusionThe proposed MRI‐based radiomics model demonstrated the potential to diagnose JME and GTCA, as well as to classify prognostic groups. MRI regions associated with JME, such as the basal ganglia, thalamus, and cerebral white matter, appeared to be important for constructing radiomics models.Level of Evidence3Technical EfficacyStage 3

Editorial for “MRI‐Based Radiomics Approach for Differentiating Juvenile Myoclonic Epilepsy From Epilepsy With Generalized Tonic–Clonic Seizures Alone”

“…6 The study by Sim et al presents a radiomics-based method to differentiate JME and GTCA and to predict favorable clinical outcome defined as a seizure-free period of at least 2 years. 7 This study builds upon a similar, previously published method to differentiate JME from healthy controls. 8 This single-center retrospective study analyzed 127 JME and 37 GTCA patients with normal-appearing MRI scans obtained at 3 T. The authors determined a priori from which structures to extract radiomics features based on convergent findings from advanced imaging techniques, including voxel-based morphometry, functional and diffusion MRI, and MR spectroscopy.…”