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Predicting cognitive function 3 months after surgery in patients with a glioma
Boelders,Sander Martijn Nicenboim,Bruno Butterbrod,Elke De Baene,Wouter Postma,Eric Rutten,Geert-Jan Ong,Lee-Ling Gehring,Karin
Boelders,Sander Martijn
Nicenboim,Bruno
Butterbrod,Elke
De Baene,Wouter
Postma,Eric
Rutten,Geert-Jan
Ong,Lee-Ling
Gehring,Karin
Abstract
BACKGROUND: Patients with a glioma often suffer from cognitive impairments both before and after anti-tumor treatment. Ideally, clinicians can rely on predictions of post-operative cognitive functioning for individual patients based on information obtainable before surgery. Such predictions would facilitate selecting the optimal treatment considering patients' onco-functional balance. METHOD: Cognitive functioning 3 months after surgery was predicted for 317 patients with a glioma across 8 cognitive tests. Nine multivariate Bayesian regression models were used following a machine-learning approach while employing pre-operative neuropsychological test scores and a comprehensive set of clinical predictors obtainable before surgery. Model performances were compared using the expected log pointwise predictive density (ELPD), and pointwise predictions were assessed using the coefficient of determination ( R 2) and mean absolute error. Models were compared against models employing only pre-operative cognitive functioning, and the best-performing model was interpreted. Moreover, an example prediction including uncertainty for clinical use was provided. RESULTS: The best-performing model obtained a median R 2 of 34.20%. Individual predictions, however, were uncertain. Pre-operative cognitive functioning was the most influential predictor. Models including clinical predictors performed similarly to those using only pre-operative functioning (ΔELPD = 14.4 ± 10.0, Δ R 2 = -0.53%). CONCLUSION: Post-operative cognitive functioning could not reliably be predicted from pre-operative cognitive functioning and the included clinical predictors. Moreover, predictions relied strongly on pre-operative cognitive functioning. Consequently, clinicians should not rely on the included predictors to infer patients' cognitive functioning after treatment. Furthermore, our results stress the need to collect larger cross-center multimodal datasets to obtain more certain predictions for individual patients.
Description
© The Author(s) 2025. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.
Date
2025
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Volume Title
Publisher
Research Projects
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Keywords
Bayesian regression, Cognitive function after treatment, Glioma, Individual predictions, Machine learning
Citation
Boelders, S M, Nicenboim, B, Butterbrod, E, De Baene, W, Postma, E, Rutten, G-J, Ong, L-L & Gehring, K 2025, 'Predicting cognitive function 3 months after surgery in patients with a glioma', Neuro-Oncology Advances, vol. 7, no. 1, vdaf081. https://doi.org/10.1093/noajnl/vdaf081
License
info:eu-repo/semantics/openAccess