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

NeuroImage: Clinical

Federica Marinoni¹  Vittorio Stumpo²  Flavio Vasella³  Jean-Philippe Dufour⁴  Carlo Serra⁴  Julia Velz⁴  Luca Regli⁴  Victor E. Staartjes⁴  Lukas L. Imbach⁴  Kevin Akeret⁴  Niklaus Krayenbühl⁵ 

[1] Amsterdam UMC, Vrije Universiteit Amsterdam, Neurosurgery, Amsterdam Movement Sciences, Amsterdam, The Netherlands;Corresponding author.;Institute of Neurosurgery, Università Cattolica del Sacro Cuore, Rome, Italy;Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland;Division of Epileptology, Department of Neurology, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland;

关键词: Epilepsy;    Metastases;    Glioma;    Primary central nervous system lymphoma;    Generalized additive model;   

DOI  :  

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown