Prediction of Pathological Complete Response after Chemoradiation for Locally Advanced Rectal Cancer Using Machine Learning with Clinical Features, the INTERCEPTOR Study Preliminary Model

Background: Organ-preserving strategies are increasingly used in rectal cancer for those achieving a pathological complete response (pCR) following chemoradiotherapy (CRT). In locally advanced rectal cancer (LARC), CRT may not significantly impact local recurrence or overall survival when an R0 resection is feasible. Accurate pretreatment prediction of pCR is critical to assist patients in deciding on CRT when it may not be oncologically required. The initial phase of the ongoing INTERCEPTOR study evaluated whether machine learning (ML) models using pretreatment clinical variables could improve pCR prediction beyond the baseline probability of 15-20%.