HIGHLIGHTS
SUMMARY
Endoscopic rectal ultrasound (ERUS) helps diagnose or guide therapy in the early stages of tumours, but it does not add value to locally advanced RC (LARC). In this setting, radiomics has been frequently coupled with AI, and in particular, machine_learning (ML) approaches for oncologic imaging, to establish models that may improve the accuracy of diagnosis, prognosis, and prediction by extracting and analysing imaging data. This review will summarise many critical clinical applications of MRI-based radiomics and AI in the field of RC, including staging, prediction of high-risk factors, genotyping, response to therapy . . .
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