Określanie strony ogniska padaczki za pomocą regularyzacji elastycznej biomarkerów tensorowych zobrazowania dyfuzji z użyciem uczenia maszynowego

Objective Accurate lateralization of the epileptogenic focus is essential for presurgical evaluation of patients with drug-resistant epilepsy. This study examined whether diffusion tensor imaging (DTI) measures of white matter microstructure, combined with clinical and demographic variables, could classify epilepsy lateralization using machine learning. Methods We present a retrospective analysis of 334 adult patients undergoing presurgical epilepsy evaluation at the University of Kansas Health System. Fractional anisotropy (FA) and mean diffusivity (MD) were extracted from 36 bilateral white matter tracts and combined with clinical and demographic variables. Several supervised machine learning approaches were evaluated, with elastic net logistic regression selected for detailed analysis. Models classified right-sided, left-sided, and bilateral or generalized epilepsy. Performance was evaluated with area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity as performance metrics. Results Elastic net models demonstrated good discrimination across classification tasks, with AUC values ranging from 0.76 to 0.88. The strongest performance distinguished left-sided epilepsy from bilateral or generalized epilepsy (AUC = 0.88; accuracy = 0.90; sensitivity = 0.90; specificity = 0.89). Discrimination between unilateral epilepsy and bilateral or generalized epilepsy was also good (AUC = 0.83). Classification of right-sided versus left-sided epilepsy demonstrated lower performance (AUC = 0.76). Significance DTI-derived white matter features analyzed using a machine learning approach may provide complementary information for epilepsy lateralization during presurgical evaluation. Further studies incorporating other pre-surgical evaluation data into the machine learning model are needed to establish the clinical utility of this approach. Key Points A machine learning model was tested using diffusion tensor imaging and clinical data to retrospectively classify epilepsy lateralization. Our model balanced sensitivity and specificity in epilepsy localization, suggesting potential as an interpretable surgical decision tool. Findingss: upport the hypothesis that white matter tracts contain useful information about seizure propagation to distal brain regions. Further model validation is needed before this can be used as a clinical tool.