Deep learning analysis of MRI to assess rectal cancer treatment.

Introduction: Traditional neoadjuvant therapy for locally advanced rectal cancer (LARC) results in pathologic complete response (pCR) in approximately 15% of patients, supporting non-operative strategies for those with clinical complete response (cCR). The subjectivity and variability in MRI-based cCR assessments highlight the need for objective, quantitative tools. Objective: To develop deep learning models for automated rectal tumor segmentation on pre- and post-treatment MRIs, and to identify radiomic features differentiating cCR from non-cCR patients. Materials and methods: We retrospectively analyzed pre- and post-treatment MRIs from 37 LARC patients enrolled in a Phase 2 TNT trial (NCT04380337). Rectal tumors were segmented on T2-weighted images by two data scientists, refined by a radiologist (reference standard), and independently segmented by a fellow. For pre-treatment segmentation, Model 1 (baseline; Results: For pre-treatment segmentation, radiologist-fellow inter-rater agreement was DSC Conclusion: This study demonstrates the feasibility of deep learning-based automated segmentation and radiomic profiling for differentiating treatment response in rectal cancer. Semi-supervised learning with pseudo-labeled data significantly improved segmentation performance, offering a practical approach to overcome limited annotations. Radiomic features warrant validation in larger multi-center studies for clinical translation.