Image-based deep learning model to predict stoma-site incisional hernia in patients with temporary ileostomy: A retrospective study

Abstract

Summary: The prophylactic implantation of biological mesh can effectively prevent the occurrence of stoma-site incisional hernia (SSIH) in patients undergoing stoma retraction. Therefore, our study prospectively established and validated a mixed model, which combined radiomics, stepwise regression, and deep learning for the prediction of SSIH in patients with temporary ileostomy. The mixed model showed good discrimination of the SSIH patients on all cohorts, which outperformed deep learning, radiomics, and clinical models alone (overall area under the curve [AUC]: 0.947 in the primary cohort, 0.876 in the external validation cohort 1, and 0.776 in the external validation cohort 2). Moreover, the sensitivity, specificity, and precision for predicting SSIH were improved in the mixed model. Thus, the mixed model can provide more information for SSIH precaution and clinical decision-making.

Keywords

• Computing methodology
• Health sciences
• Medical imaging