P1035 CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn’s disease resistant to infliximab

Abstract

Abstract Background Data describing the effect of visceral adipose tissue (VAT) on infliximab treatment response in patients with Crohn’s disease (CD) remains scarce. We aimed to develop and validate a VAT-based radiomics model (RM) using computed tomography (CT) images to identify CD patients at high risk of primary nonresponse to infliximab, and explored whether it can improve the prediction efficacy of an established bowel lesions-based RM. Methods This retrospective study included 231 CD patients (training cohort, n=112; internal validation cohort, n=48; external validation cohort, n=71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI). Results VAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal [AUC: VAT model vs bowel model, 0.737(95% CI, 0.590-0.854) vs. 0.832 (95% CI, 0.750 - 0.896)] and external validation cohort [AUC: VAT model vs. bowel model, 0.714(95% CI, 0.595-0.815) vs. 0.799(95% CI, 0.687-0.885)], exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal [AUC,0.840(95%CI, 0.706-0.930)] and external validation cohort [AUC,0.833(95%CI, 0.726-0.911)], significantly better than bowel alone (IDI=4.2% and 3.7% in internal and external validation cohorts, both P<0.05). Conclusion VAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM.