Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy.

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 stand

SUBMITTER: Isaksson LJ 

PROVIDER: S-EPMC7289968 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature