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

SUBMITTER: Akeret K 

PROVIDER: S-EPMC7711280 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature