Machine learning for radiation outcome modeling and prediction

摘要

Aims This review paper intends to summarize the application of machine learning to radiotherapy outcome modeling based on structured and un‐structured radiation oncology datasets. Materials and methods The most appropriate machine learning approaches for structured datasets in terms of accuracy and interpretability are identified. For un‐structured datasets, deep learning algorithms are explored and a critical view of the use of these approaches in radiation oncology is also provided. Conclusions We discuss the challenges in radiotherapy outcome prediction, and suggest to improve radiation outcome modeling by developing appropriate machine learning approaches where both accuracy and interpretability are taken into account.