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

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

doi:10.1016/j.nicl.2020.102506

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

Neuroimage Clin. 2020;28:102506. doi: 10.1016/j.nicl.2020.102506. Epub 2020 Nov 19.

Authors

Affiliations

¹ Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland. Electronic address: kevin.akeret@usz.ch.
² Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Institute of Neurosurgery, Università Cattolica del Sacro Cuore, Rome, Italy.
³ Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Amsterdam Movement Sciences, Amsterdam, The Netherlands.
⁴ Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
⁵ Division of Epileptology, Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
⁶ Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Division of Pediatric Neurosurgery, University Children's Hospital, Zurich, Switzerland.

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.

Keywords: Epilepsy; Generalized additive model; Glioma; Metastases; Primary central nervous system lymphoma.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Brain Neoplasms* / diagnostic imaging
Epilepsy*
Humans
Machine Learning
Phylogeny
Seizures