Deep learning in MRI‐guided radiation therapy: A systematic review
Abstract
Abstract Recent advances in MRI‐guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real‐time MRI monitoring, and the MRI‐only treatment planning workflow. Given the rapid growth and emergence of new state‐of‐the‐art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x‐ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi‐modal, visual transformer, and diffusion models.
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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