Joeky Senders

88 Chapter 5 Abstract Introduction Although survival statistics in patients with glioblastoma are well-defined at the group level, predicting individual-patient survival remains challenging due to significant variation within strata. The aim of this study was to compare statistical and machine learning algorithms in their ability to predict survival in glioblastoma patients and deploy the best performing model as an online survival calculator. Methods Patients undergoing an operation for a histopathologically confirmed glioblastoma were extracted from the Surveillance Epidemiology and End Results (SEER) database (2005-2015) and split into a training and hold-out test set in an 80/20 ratio. Fifteen statistical and machine learning algorithms were trained based on 13 demographic, socio-economic, clinical, and radiographic features to predict overall survival, one-year survival status, and compute personalized survival curves. Results In total, 20,821 patients met our inclusion criteria. The accelerated failure time model demonstrated superior performance in terms of discrimination (concordance- index=0.70), calibration, interpretability, predictive applicability, and computational efficiency compared to Cox proportional hazards regression and other machine learning algorithms. This model was deployed through a free, publicly available software interface (https://cnoc-bwh.shinyapps.io/gbmsurvivalpredictor/) . Conclusion The development and deployment of survival prediction tools require a multimodal assessment rather than a single metric comparison. This study provides a framework for the development of prediction tools in cancer patients, as well as an online survival calculator for patients with glioblastoma. Future efforts should improve the interpretability, predictive applicability, and computational efficiency of existing machine learning algorithms, increase the granularity of population-based registries, and externally validate the proposed prediction tool.