Prediction of Prognosis in Brain Metastasis with Artificial-intelligence-driven Methods for Whole Brain Radiotherapy

Background : Inferentially 24%–45% of cancer patients develop brain metastases in their course. Individual survival estimation for these patients is substantial to distinguish the subset of patients who may not benefit from whole brain irradiation (WBI) due to a short survival time. Aim : This study aimed to search on variables and evaluate an artificial intelligence algorithm to identify the subgroup of patients who will benefit from WBI. Methods: The data of 345 patients with brain metastasis who were treated with 30 Gy in 10 fractions of WBI were retrospectively analyzed. In this cohort totally 15 clinical / laboratory factors are evaluated with 15 models of machine learning algorithms using Python 2.3, Pycaret library. Results : Gradient Boosting Regressor was found to be the accurate modelling with a 0.68 R2 value and 12.90 mean absolute value (MAE). Prediction error for gradient Boosting Regressor was calculated as R2: 0.841. When the importance of features was investigated, time from diagnosis to metastasis was found to be the most important predictive variable for survival. Conclusion : The results of this study enables to identify patients who may have early death and provides a consequential decision guide in terms of whole brain radiotherapy or additional labor intense techniques.