Rationale and Objectives
To develop computed tomography enterography (CTE)-based radiomics models to assess mucosal healing (MH) in patients with Crohn's disease (CD).
Materials and Methods
CTE images were retrospectively collected from 92 confirmed cases of CD at the post-treatment review. Patients were randomly divided into developing (n = 73) and testing (n = 19) groups. Radiomics features were extracted from the enteric phase images, and the least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection using 5-fold cross-validation on the developing group. The selected features were further identified from the top-ranked features and used to create improved radiomics models. Machine learning models were constructed to compare radiomics models with different radiomics features. The area under the ROC curve (AUC) was calculated to assess the predictive performance for identifying MH in CD.
Among the 92 CD patients included in our study, 36 patients achieved MH. The AUC of the radiomics model 1, which was based on the 26 selected radiomics features, was 0.976 for evaluating MH in the testing cohort. The AUCs of radiomics models 2 and 4, based on the top 10 and top 5 positive and negative radiomics features, were 0.974 and 0.952 in the testing cohort, respectively. The AUC of the radiomics model 3, built by removing features with r > 0.5, was 0.956 in the testing cohort. The clinical utility of the clinical radiomics nomogram was confirmed by the decision curve analysis (DCA).
The CTE-based radiomics models have demonstrated favorable performance in assessing MH in patients with CD. Radiomics features can be used as a promising imaging biomarker for MH.
Abbreviations:AUC (area under the ROC curve), CD (Crohn's disease), CTE (computed tomography enterography), CRP (C-reactive protein), DCA; (decision curve analysis), HBI (Harvey Bradshaw Index), IFX (infliximab), LASSO (least absolute shrinkage and selection operator), MH (mucosal healing), ROI (regions of interest), ROC (receiver operator characteristic), WBC (white blood cell)
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Published online: May 18, 2023
Accepted: April 18, 2023
Received in revised form: April 17, 2023
Received: March 22, 2023
Publication stageIn Press Corrected Proof
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