Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy
Lars Johannes Isaksson, Matteo Pepa, Mattia Zaffaroni, Giulia Marvaso, Daniela Alterio, Stefania Volpe, Giulia Corrao, Matteo Augugliaro, Anna Starzyńska, Maria Cristina Leonardi, Roberto Orecchia, Barbara Alicja Jereczek‐Fossa
Abstract
In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of 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. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.