Optimal computed tomography-based biomarkers for prediction of incisional hernia formation.

ABSTRACT:

Purpose

Unstructured data are an untapped source for surgical prediction. Modern image analysis and machine learning (ML) can harness unstructured data in medical imaging. Incisional hernia (IH) is a pervasive surgical disease, well-suited for prediction using image analysis. Our objective was to identify optimal biomarkers (OBMs) from preoperative abdominopelvic computed tomography (CT) imaging which are most predictive of IH development.

Methods

Two hundred and twelve rigorously matched colorectal surgery patients at our institution were included. Preoperative abdominopelvic CT scans were segmented to derive linear, volumetric, intensity-based, and textural features. These features were analyzed to find a small subset of OBMs, which are maximally predictive of IH. Three ML

SUBMITTER: Talwar AA 

PROVIDER: S-EPMC11235401 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature