Prediction Crohn's Disease Activity Using Computed Tomography Enterography-Based Radiomics and Serum Markers.
Authors
Affiliations (3)
Affiliations (3)
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, 200093, Shanghai, China (P.W., Y.W.).
- Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, No. 639, Zhizaoju Road, Huangpu District, Shanghai 200011, China (Y.L.).
- School of Health Science and Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, 200093, Shanghai, China (P.W., Y.W.). Electronic address: [email protected].
Abstract
Accurate stratification of the activity index of Crohn's disease (CD) using computed tomography enterography (CTE) radiomics and serum markers can aid in predicting disease progression and assist physicians in personalizing therapeutic regimens for patients with CD. This retrospective study enrolled 233 patients diagnosed with CD between January 2019 and August 2024. Patients were divided into training and testing cohorts at a ratio of 7:3 and further categorized into remission, mild active phase, and moderate-severe active phase groups based on simple endoscopic score for CD (SEC-CD). Radiomics features were extracted from CTE venous images, and T-test and least absolute shrinkage and selection operator (LASSO) regression were applied for feature selection. The serum markers were selected based on the variance analysis. We also developed a random forest (RF) model for multi-class stratification of CD. The model performance was evaluated by the area under the receiver operating characteristic curve (AUC) and quantified the contribution of each feature in the dataset to CD activity via Shapley additive exPlanations (SHAP) values. Finally, we enrolled gender, radiomics scores, and serum scores to develop a nomogram model to verify the effectiveness of feature extraction. 14 non-zero coefficient radiomics features and six serum markers with significant differences (P<0.01) were ultimately selected to predict CD activity. The AUC (micro/macro) for the ensemble machine learning model combining the radiomics features and serum markers is 0.931/0.928 for three-class. The AUC for the remission phase, the mild active phase, and the moderate-severe active phase were 0.983, 0.852, and 0.917, respectively. The mean AUC for the nomogram model was 0.940. A radiomics model was developed by integrating radiomics and serum markers of CD patients, achieving enhanced consistency with SEC-CD in grade CD. This model has the potential to assist clinicians in accurate diagnosis and treatment.