Quantitative MRI radiomics approach for evaluating muscular alteration in Crohn disease: development of a machine learning-nomogram composite diagnostic tool.

Abstract

Background: Emerging evidence underscores smooth muscle hyperplasia and hypertrophy, rather than fibrosis, as the defining characteristics of fibrostenotic lesions in Crohn disease (CD). However, non-invasive methods for quantifying these muscular changes have yet to be fully explored.

Aims: To explore the application value of radiomics based on magnetic resonance imaging (MRI) post-contrast T1-weighted images to identify muscular alteration in CD lesions with significant inflammation.

Methods: A total of 68 cases were randomly assigned in this study, with 48 cases allocated to the training dataset and the remaining 20 cases assigned to the independent test dataset. Radiomic features were extracted and constructed a diagnosis model by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Construct a nomogram based on multivariate logistic regression analysis, integrating radiomics signature, MRI features and clinical characteristics.

Results: The radiomics model constructed based on the selected features of the post-contrasted T1-weighted images has good diagnostic performance, which yielded a sensitivity of 0.880, a specificity of 0.783, and an accuracy of 0.833 [AUC = 0.856, 95% confidence interval (CI) = 0.765-0.947]. Moreover, the nomogram representing the integrated model achieved good discrimination performances, which yielded a sensitivity of 0.836, a specificity of 0.892, and an accuracy of 0.864 (AUC = 0.926, 95% CI = 0.865-0.988), and it was better than that of the radiomics model alone.

Conclusions: The radiomics based on post-contrasted T1-weighted images provides additional biomarkers for Crohn disease. Additionally, integrating DCE-MRI, radiomics, and clinical data into a comprehensive model significantly improves diagnostic accuracy for identifying muscular alteration.