Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy - Isaksson, Lars J.; Pepa, Matteo; Zaffaroni, Mattia; Marvaso, Giulia; Alterio, Daniela; Volpe, Stefania; Corrao, Giulia; Augugliaro, Matteo; Starzyńska, Anna; Leonardi, Maria C.; Orecchia, Roberto; Jereczek-Fossa, Barbara A. (Frontiers in Oncology, 2020)
In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction schemes are essential. In recent years, the growing interest towards artificial intelligence (AI) and machine learning (ML) in science have led to the implementation of such innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Here we present a review of ML-based models for predicting RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the classifiers used and the main results achieved. Our work, which considers one anatomical district at a time, aims to define the state-of-art for researchers and clinicians.