Radiomics to Detect Inflammation and Fibrosis on Magnetic Resonance Enterography in Stricturing Crohn's Disease.

Abstract

Non-invasive cross-sectional imaging via magnetic resonance enterography (MRE) offers excellent accuracy for the diagnosis of stricturing complications in Crohn's disease (CD) but is limited in determining the degrees of fibrosis and inflammation within a stricture. We developed and validated a radiomics-based machine-learning model for separately characterizing the degree of histopathologic inflammation and fibrosis in CD strictures and compared it to centrally read visual radiologist scoring of MRE. This single center, cross-sectional study, included 51 CD patients (n=34 for discovery; n=17 for validation) with terminal ileal strictures confirmed on diagnostic MRE within 15 weeks of resection. Histopathological specimens were scored for inflammation and fibrosis and spatially linked with corresponding pre-surgical MRE sequences. Annotated stricture regions on MRE were scored visually by radiologists as well as underwent 3D radiomics-based machine learning analysis; both evaluated against histopathology. Two distinct sets of radiomic features capturing textural heterogeneity within strictures were linked with each of severe inflammation or severe fibrosis across both discovery (area under the curve (AUC)=0.69, 0.83) and validation (AUCs=0.67,0.78) cohorts. Radiologist visual scoring had an AUC=0.67 for identifying severe inflammation and AUC=0.35 for severe fibrosis. Use of combined radiomics and radiologist scoring robustly augmented identification of severe inflammation (AUC=0.79) and modestly improved assessment of severe fibrosis (AUC=0.79 for severe fibrosis) over individual approaches. Radiomic features of CD strictures on MRE can accurately identify severe histopathologic inflammation and severe histopathologic fibrosis, as well as augment performance of radiologist visual scoring in stricture characterization.