Predictive modeling with linear machine learning can estimate glioblastoma survival in months based solely on...

Machine Learning (ML) has become an essential tool for analyzing biomedical data, facilitating the prediction of treatment outcomes and patient survival. However, the effectiveness of ML models heavily relies on both the choice of algorithms and the quality of the input data. In this study, we aimed to develop a novel predictive model to estimate individual survival for patients diagnosed with glioblastoma (GBM), focusing on key variables such as O6-Methylguanine-DNA Methyltransferase (MGMT) methylation status, age, and sex.

To identify the optimal approach, we utilized retrospective data from 218 patients treated at our brain tumor center. The performance of the ML models was evaluated within repeated tenfold regression. The pipeline comprised five regression estimators, including both linear and non-linear algorithms. Permutation feature importance highlighted the feature with the most significant impact on the model. Statistical significance was assessed using a permutation test procedure.

The best machine learning algorithm achieved a mean absolute error (MAE) of 12.65 (SD = ± 2.18) and an explained variance (EV) of 7% (SD = ± 1.8%) with p < 0.001. Linear algorithms led to more accurate predictions than non-linear estimators. Feature importance testing indicated that age and positive MGMT-methylation influenced the predictions the most.

In summary, here we provide a novel approach allowing to predict GBM patient's survival in months solely based on key parameters such as age, sex and MGMT-methylation status and underscores MGMT-methylation status as key prognostic factor for GBM patients survival.