In Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE : We aimed to develop a clinically applicable prognosis prediction model predicting overall survival (OS) and progression-free survival (PFS) for glioblastoma multiforme (GBM) patients.
MATERIALS AND METHODS : All 467 patients treated with concurrent chemoradiotherapy at Yonsei Cancer Center from 2016 to 2020 were included in this study. We developed a conventional linear regression, Cox proportional hazards (COX), and non-linear machine learning algorithms, random survival forest (RSF) and survival support vector machine (SVM) based on 16 clinical variables. After backward feature selection and hyperparameter tuning using grid search, we repeated 100 times of cross-validations to combat overfitting and enhance the model performance. Harrell's concordance index (C-index) and integrated brier score (IBS) were employed as quantitative performance metrics.
RESULTS : In both predictions, RSF performed much better than COX and SVM. (For OS prediction: RSF C-index=0.72 90%CI [0.71-0.72] and IBS=0.12 90%CI [0.10-0.13]; For PFS prediction: RSF C-index=0.70 90%CI [0.70-0.71] and IBS=0.12 90%CI [0.10-0.14]). Permutation feature importance confirmed that MGMT promoter methylation, extent of resection, age, cone down planning target volume, and subventricular zone involvement are significant prognostic factors for OS. The importance of the extent of resection and MGMT promoter methylation was much higher than other selected input factors in PFS. Our final models accurately stratified two risk groups with mean square errors less than 0.5%. The sensitivity analysis revealed that our final models are highly applicable to newly diagnosed GBM patients.
CONCLUSION : Our final models can provide a reliable outcome prediction for individual GBM. The final OS and PFS predicting models we developed accurately stratify high-risk groups up to 5-years, and the sensitivity analysis confirmed that both final models are clinically applicable.
Kim Yeseul, Hwan Kim Kyung, Park Junyoung, In Yoon Hong, Sung Wonmo
2023-Mar-13
Glioblastoma multiforme, cox proportional hazards, machine learning, prognosis prediction, random survival forest, survival support vector machine, web-based prediction tool
