Computed tomography enterography-based radiomics nomograms to predict inflammatory activity for ileocolonic Crohn’s disease: a preliminary single-center retrospective study - Yuping Ma; Luanxin Zhu; Bota Cui; Faming Zhang; Haige Li; Jianguo Zhu (BMC Medical Imaging)
Abstract Objectives This study aims to develop and validate nomograms that utilize morphological and radiomics features derived from computed tomography enterography (CTE) to evaluate inflammatory activity in patients with ileocolonic Crohn’s disease (CD). Methods A total of 54 CD patients (237 bowel segments) with clinically confirmed CD were retrospectively analyzed. The Simple Endoscopic Score for Crohn’s Disease (SES-CD) was used as a reference standard to quantify the degree of mucosal inflammation and assess disease severity. We extracted morphological and radiomics features in the training cohort to create a morphological model (M-score) and a radiomics model (Rad-score). A combined nomogram was generated by integrating the M-score and Rad-score. The predictive performance of each model was evaluated using receiver operating characteristic (ROC) curve analysis. Additionally, calibration curve and decision curve analysis (DCA) were employed to assess the accuracy and clinical applicability of the nomogram in the testing cohort. Results The area under the ROC curve (AUC) for the nomogram, which included stenosis, comb sign, and Rad-score, was 0.834 [95% confidence interval (CI): 0.728–0.940] for distinguishing between active and remissive disease. Furthermore, the nomogram created using comb sign and Rad-score achieved a satisfactory AUC of 0.781 (95% CI: 0.611–0.951) in differentiating mild activity from moderate-to-severe activity. The calibration curve and DCA confirmed both nomograms’ accuracy and clinical utility. Conclusions Nomograms that combined CTE-based radiomics and morphological features could serve as valuable tools for assessing inflammatory activity, thereby supporting clinical decision-making in managing CD. Keypoints. 1. Radiomics features from CTE could predict the inflammatory activity of CD.