Development and Validation of a CTE-Based Radiomics Nomogram for Predicting Clinical Adverse Outcomes in Patients with Stricturing Crohn's Disease.

OBJECTIVE

This study sought to develop and validate a radiomics nomogram using computed tomography enterography (CTE) to predict clinical adverse outcomes (CAO) in patients with stricturing Crohn's disease (CD), aiding in personalized treatment planning.

METHODS

We retrospectively collected data from 219 patients diagnosed with stricturing CD between January 2018 and March 2023 at our institution, dividing them into a training set (n=153) and a testing set (n=66). Radiomics features from strictured segments were extracted and the most predictive features were identified using Pearson correlation, SelectKBest, and Least Absolute Shrinkage and Selection Operator (LASSO) regression to derive a Radiomics score (Rad-score). Cox regression was used to select key clinical predictors of CAO. A radiomics nomogram was developed to predict CAO, evaluated using Harrell's concordance index (C-index), time-dependent Receiver Operating Characteristic (ROC) curves, and Decision Curve Analysis (DCA).

RESULTS

Univariate and multivariate Cox regression analyses of the training set identified the HBI score (HR=0.443, 95% CI=0.212-0.925, =0.030) and the diameter of the upstream lumen (HR=1.080, 95% CI=1.050-1.111, <0.001) as independent clinical predictors of CAO in stricturing CD. Nineteen features related to CAO outcomes were selected for Rad-score calculation. In the testing set, the C-index for the clinical, radiomics, and nomogram models were 0.752, 0.775, and 0.849, respectively. The AUCs of the nomogram model at 1, 2, and 3 years were 0.874, 0.863, and 0.956, respectively.

CONCLUSION

The CTE-based radiomics nomogram significantly outperformed clinical and radiomics models alone and demonstrated excellent predictive accuracy for CAO risk. By integrating the HBI score and upstream lumen diameter with radiomics features, this tool provides clinicians with a validated, noninvasive method to stratify stricturing CD patients by risk and guide personalized therapeutic decisions.