Artificial Intelligence-Derived Intramuscular Adipose Tissue Assessment Predicts Perineal Wound Complications Following Abdominoperineal Resection.

Abstract

Perineal wound complications following abdominoperineal resection (APR) significantly impacts patient morbidity. Despite various closure techniques, no method has proven superior. Body composition is a key factor influencing postoperative outcomes. AI-assisted CT scan analysis is an accurate and efficient approach to assessing body composition. This study aimed to evaluate whether body composition characteristics can predict perineal wound complications following APR. A retrospective cohort study of APR patients from 2012 to 2024 was conducted, comparing primary closure and inferior gluteal artery myocutaneous (IGAM) flap closure outcomes. Preoperative CT scans were analyzed using a validated AI model to measure lumbosacral skeletal muscle (SM), intramuscular adipose tissue (IMAT), visceral adipose tissue, and subcutaneous adipose tissue. Greater IMAT volume correlated with increased wound dehiscence in males undergoing IGAM closure (40% vs. 4.8% and p = 0.027). Lower SM-to-IMAT volume ratio was associated with higher wound infection rates (60% vs. 19% and p = 0.04). Closure technique did not significantly impact wound infection or dehiscence rates. This study is the first to use AI derived 3D body composition analysis to assess perineal wound complications after APR. IMAT volume significantly influences wound healing in male patients having IGAM reconstruction.