Machine learning for radiation outcome modeling and prediction

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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.

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