Topographic brain tumor anatomy drives seizure risk and enables machine learning based prediction

• Main Authors: Kevin Akeret, Vittorio Stumpo, Victor E. Staartjes, Flavio Vasella, Julia Velz, Federica Marinoni, Jean-Philippe Dufour, Lukas L. Imbach, Luca Regli, Carlo Serra, Niklaus Krayenbühl
• Format: Article
• Language: English
• Published: Elsevier 2020-01-01
• Series: NeuroImage: Clinical
• Subjects: Epilepsy; Metastases; Glioma; Primary central nervous system lymphoma; Generalized additive model
• Online Access: http://www.sciencedirect.com/science/article/pii/S2213158220303430

Objective: The aim of this study was to identify relevant risk factors for epileptic seizures upon initial diagnosis of a brain tumor and to develop and validate a machine learning based prediction to allow for a tailored risk-based antiepileptic therapy. Methods: Clinical, electrophysiological and high-resolution imaging data was obtained from a consecutive cohort of 1051 patients with newly diagnosed brain tumors. Factor-associated seizure risk difference allowed to determine the relevance of specific topographic, demographic and histopathologic variables available at the time of diagnosis for seizure risk. The data was divided in a 70/30 ratio into a training and test set. Different machine learning based predictive models were evaluated before a generalized additive model (GAM) was selected considering its traceability while maintaining high performance. Based on a clinical stratification of the risk factors, three different GAM were trained and internally validated. Results: A total of 923 patients had full data and were included. Specific topographic anatomical patterns that drive seizure risk could be identified. The involvement of allopallial, mesopallial or primary motor/somatosensory neopallial structures by brain tumors results in a significant and clinically relevant increase in seizure risk. While topographic input was most relevant for the GAM, the best prediction was achieved by a combination of topographic, demographic and histopathologic information (Validation: AUC: 0.79, Accuracy: 0.72, Sensitivity: 0.81, Specificity: 0.66). Conclusions: This study identifies specific phylogenetic anatomical patterns as epileptic drivers. A GAM allowed the prediction of seizure risk using topographic, demographic and histopathologic data achieving fair performance while maintaining transparency.