Predicting cognitive function 3 months after surgery in patients with a glioma

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.