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Abstract
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 root mean square errors less than 0.07. 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.

Affiliations

Yeseul Kim 1, Kyung Hwan Kim 2, Junyoung Park 3, Hong In Yoon 4, Wonmo Sung 5
1Department of Biomedical Engineering and of Biomedicine & Health Science, College of Medicine, The Catholic University of Korea, Seoul 137-70, South Korea.
2Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea.
3Department of Industrial and Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea.
4Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Seoul, South Korea. Electronic address: YHI0225@yuhs.ac.
5Department of Biomedical Engineering and of Biomedicine & Health Science, College of Medicine, The Catholic University of Korea, Seoul 137-70, South Korea. Electronic address: wsung@catholic.ac.kr.