Boelders Sander Martijn, Nicenboim Bruno, Butterbrod Elke, De Baene Wouter, Postma Eric, Rutten Geert-Jan, Ong Lee-Ling, Gehring Karin
Department of Cognitive Sciences and AI, Tilburg University, Tilburg, The Netherlands.
Department of Neurosurgery, Elisabeth-TweeSteden Hospital, Tilburg, The Netherlands.
Neurooncol Adv. 2025 May 2;7(1):vdaf081. doi: 10.1093/noajnl/vdaf081. eCollection 2025 Jan-Dec.
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 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 ( ) 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 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 = -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.
胶质瘤患者在抗肿瘤治疗前后常出现认知障碍。理想情况下，临床医生可以根据术前可获取的信息对个体患者术后的认知功能进行预测。这种预测将有助于在考虑患者肿瘤功能平衡的情况下选择最佳治疗方案。
对317例胶质瘤患者进行了8项认知测试，以预测术后3个月的认知功能。采用机器学习方法，使用9个多元贝叶斯回归模型，同时采用术前神经心理学测试分数和术前可获取的一组全面的临床预测指标。使用预期对数逐点预测密度（ELPD）比较模型性能，并使用决定系数（ ）和平均绝对误差评估逐点预测。将模型与仅采用术前认知功能的模型进行比较，并对表现最佳的模型进行解释。此外，还提供了一个包括临床使用不确定性的示例预测。
表现最佳的模型中位数 为34.20%。然而，个体预测并不确定。术前认知功能是最有影响力的预测指标。包括临床预测指标的模型与仅使用术前功能的模型表现相似（ΔELPD = 14.4±10.0，Δ = -0.53%）。
无法根据术前认知功能和纳入的临床预测指标可靠地预测术后认知功能。此外，预测强烈依赖于术前认知功能。因此，临床医生不应依赖纳入的预测指标来推断患者治疗后的认知功能。此外，我们的结果强调需要收集更大的跨中心多模态数据集，以获得对个体患者更确定的预测。
