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

Area of Science:

• Neuroscience
• Oncology
• Medical Statistics

Background:

• Glioma patients frequently experience cognitive impairments before and after treatment.
• Accurate prediction of post-operative cognitive function is crucial for personalized treatment planning.
• Optimizing the onco-functional balance requires reliable prognostic tools.

Purpose of the Study:

• To predict cognitive functioning 3 months after surgery in 317 glioma patients.
• To evaluate machine learning models using pre-operative neuropsychological and clinical data.
• To assess the reliability of predictions for individual patient outcomes.

Main Methods:

• Employed nine multivariate Bayesian regression models with a machine-learning approach.
• Utilized pre-operative neuropsychological test scores and clinical predictors.
• Compared model performance using expected log pointwise predictive density (ELPD) and R-squared (R²).

Main Results:

• The best model achieved a median R² of 34.20%, indicating moderate predictive power.
• Pre-operative cognitive functioning was the most significant predictor.
• Models incorporating clinical predictors showed similar performance to those using only pre-operative functioning.

Conclusions:

• Post-operative cognitive functioning could not be reliably predicted using pre-operative data.
• Predictions were highly dependent on pre-operative cognitive status.
• Larger, multi-center, multimodal datasets are needed for more accurate individual predictions.