46. Optimal Computed Tomography-based Biomarkers for Prediction of Incisional Hernia Formation

Abstract

PURPOSE: Incisional hernia (IH) is a pervasive surgical disease. Computer vision and machine learning (ML) were used to derive computed tomography (CT)-based features predictive of IH. METHODS: Patients who underwent colorectal surgery between 2005-2017 were identified (n=14,345). Patients who developed IH were matched with those who did not (n=212). Preoperative abdominopelvic CT scans were segmented to derive linear, volumetric, intensity-based, and textural measurements. Optimal biomarkers (OBMs) were derived and used to test ML classifiers (SVMs, Random Forests, Ensemble Boosting) for IH prediction. RESULTS: 279 features were extracted from preoperative CTs. The most predictive combination of OBMs was: 1) abdominopelvic visceral adipose tissue volume (VAT); 2) abdominopelvic intra-abdominal musculature volume; and 3) pelvic VAT to outer abdominal musculature volume (OAM) ratio. ML models using these OBMs were tested, Ensemble Boosting outperformed other models across all metrics. CONCLUSION: These OBMs suggest intra-abdominal volume/pressure is the most salient pathophysiologic mechanism for IH formation. ML models using OBMs are highly predictive of IH. Image analysis is a powerful tool in surgical risk prediction.