https://orcid.org/0000-0002-6540-1273
Abstract
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.
Cognitive functioning three months after surgery was predicted for 317 patients with a glioma across eight 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 (R²) 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.
The best-performing model obtained a median R² 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, ΔR² -0.53%).
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.
Accepted manuscripts
