Robust prostate cancer risk stratification from unregistered mpMRI via learned cross-modal correspondence

Our results demonstrate that learning a direct correspondence between unregistered mpMRI sequences significantly improves prostate cancer risk stratification. The proposed CMOT-Fusion framework offers a robust solution to the common clinical problem of inter-sequence misalignment, potentially enhancing diagnostic reliability and streamlining clinical workflows by removing the need for a separate image registration step. Given the single-center retrospective design and the small independent test cohort, these findings should be considered exploratory and warrant multi-center prospective validation.