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Kevin Akeret

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2 papers

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YNICL Journal 2020 Journal Article

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

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.

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YNICL Journal 2019 Journal Article

Anatomical features of primary brain tumors affect seizure risk and semiology

Kevin Akeret
Carlo Serra
Omar Rafi
Victor E. Staartjes
Jorn Fierstra
David Bellut
Nicolai Maldaner
Lukas L. Imbach

OBJECTIVE: An epileptic seizure is the most common clinical manifestation of a primary brain tumor. Due to modern neuroimaging, detailed anatomical information on a brain tumor is available early in the diagnostic process and therefore carries considerable potential in clinical decision making. The goal of this study was to gain a better understanding of the relevance of anatomical tumor characteristics on seizure prevalence and semiology. METHODS: We reviewed prospectively collected clinical and imaging data of all patients operated on a supratentorial intraparenchymal primary brain tumor at our department between January 2009 and December 2016. The effect of tumor histology, anatomical location and white matter infiltration on seizure prevalence and semiology were assessed using uni- and multivariate analyses. RESULTS: Of 678 included patients, 311 (45.9%) presented with epileptic seizures. Tumor location within the central lobe was associated with higher seizure prevalence (OR 4.67, 95% CI: 1.90-13.3, p = .002), especially within the precentral gyrus or paracentral lobule (100%). Bilateral extension, location within subcortical structures and invasion of deeper white matter sectors were associated with a lower risk (OR 0.45, 95% CI: 0.25-0.78; OR 0.10, 95% CI: 0.04-0.21 and OR 0.39, 95% CI: 0.14-0.96, respectively). Multivariate analysis revealed the impact of a location within the central lobe on seizure risk to be highly significant and more relevant than histopathology (OR: 4.79, 95% CI: 1.82-14.52, p = .003). Seizures due to tumors within the central lobe differed from those of other locations by lower risk of secondary generalization (p < .001). CONCLUSIONS: Topographical lobar and gyral location, as well as extent of white matter infiltration impact seizure risk and semiology. This finding may have a high therapeutic potential, for example regarding the use of prophylactic antiepileptic therapy.

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