A new model outperforming RPA and DS-GPA scores for individualized survival prediction of patients following whole brain irradiation for brain metastasis.

Abstract Purpose Survival after whole brain radiation therapy (WBRT) in patients with multiple brain metastases (BM) is currently predicted by group-based scoring systems with limited usability for decision. We aimed to develop a more relevant individualized predictive model than Radiation Therapy Oncology Group – Recursive Partitioning Analysis (RTOG-RPA) and Diagnosis – Specific Graded Prognostic Assessment (DS-GPA) for patients with limited life-expectancy. Methods Based on a Discovery cohort of patients undergoing WBRT, multivariable piecewise Cox regression models with time cut-offs at 1 and 3 months were developed to predict overall survival (OS). A final parsimonious model was defined, and an external validation cohort was used to assess its discrimination and calibration at one, six, and 12 months. Results In the 173-patient Discovery cohort, the majority of patients had primary lung cancer (56%), presence of extracranial disease (ECD) (75%), Eastern Cooperative Oncolgy Group - Performance Status (ECOG-PS) score 1 (41%) and no intracranial hypertension (ICH) (74%). Most patients were classified as the RPA class II (48%). The final piecewise Cox model was based on primary site, age, ECD, ECOG-PS and ICH. An external validation of the model was carried out using a cohort of 79 patients. Individualized survival estimates obtained with this model outperformed the RPA and DS-GPA scores for overall survival prediction at 1-month, 6-months and 12- months in both Discovery and Validation cohorts. A R/Shiny web application was developed to obtain individualized predictions for new patients, providing an easy-to-use tool for clinicians and researchers. Conclusion Our model provides individualized estimates of survival for poor prognosis patients undergoing WBRT, outperforming actual scoring systems.