Machine Learning-Enhanced Prognostic Modeling in Elderly Glioblastoma Isocitrate Dehydrogenase-Wildtype: A Multidimensional Single-Center Cohort Study.

BACKGROUND

Glioblastoma isocitrate dehydrogenase IDH-wildtype (GBM IDHwt) in elderly patients presents challenges due to biological heterogeneity and under-representation in clinical trials. Despite rising incidence, prognostication remains inadequate, with treatment decisions based on subjective criteria.

OBJECTIVE

To determine clinical, radiological, surgical, and molecular determinants of survival in elderly GBM IDHwt patients and explore prognostic utility of machine learning (ML) models using clinical and pretreatment data.

METHODS

We analyzed 155 patients aged ≥70 years with confirmed GBM IDHwt who underwent neurosurgery at a tertiary care institution. We examined variables related to clinical presentation, imaging, surgery, and molecular markers using multivariate regression and Histogram Gradient Boosting Regression ML models. Two ML models were developed: one incorporating full dataset variables, and another focusing on preoperative features.

RESULTS

Median overall survival (OS) was 11.3 months for patients undergoing resection and 3.7 months for biopsy. Independent predictors of prolonged OS included gross total resection (GTR), O6-methylguanine-DNA methyltransferase promoter methylation, nonacute symptom onset, and concomitant radiotherapy with temozolomide (RT + TMZ). ML models confirmed RT + TMZ and GTR as strongest predictors, while Karnofsky Performance Status showed negative importance. Body mass index (BMI) emerged as impactful; and the pretreatment model emphasized BMI and cognitive decline.

CONCLUSIONS

This study confirmed prognostic relevance of GTR, O6-methylguanine-DNA methyltransferase methylation, and RT + TMZ combination. Baseline Karnofsky Performance Status and age did not demonstrate independent prognostic value, while BMI and cognitive decline were potential preoperative predictors. Our findings advocate a multidimensional, data-driven approach to preoperative risk stratification in elderly GBM patients, which may facilitate individualized treatment strategies.