Machine learning for radiation outcome modeling and prediction

Abstract

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.

Keywords: accuracy; interpretability; machine learning; radiation outcome modeling; structured and unstructured datasets.