ArticleNeuro-oncology advances
Predicting cognitive function 3 months after surgery in patients with a glioma.
Article in Neuro-oncology advances. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Article
- Association of multidisciplinary collaborative nursing with cognitive function and quality of life after craniotomy for glioma resection: a retrospective comparative study.Frontiers in medicine · 2026Article
- A scoping review: Understanding brain tumor patients' decisional needs and preferences.Neuro-oncology practice · 2025Review
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Authors and funding
8 authors.
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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 ( Results: The best-performing model obtained a median 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.
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