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

Abstract

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.

• 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
• Journal: NeuroImage. Clinical (Neuroimage Clin) Vol. 28 Pg. 102506 ( 2020) ISSN: 2213-1582 [Electronic] Netherlands
• PMID: 33395995 (Publication Type: Journal Article, Research Support, Non-U.S. Gov't)
• Copyright: Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

Topics

• Brain Neoplasms (diagnostic imaging)
• Epilepsy
• Humans
• Machine Learning
• Phylogeny
• Seizures