Rieder, Florian

INTRODUCTION: Computed tomography enterography (CTE) is a noninvasive cross-sectional imaging modality routinely used for diagnosis of Crohn's disease (CD) strictures. However, CTE is limited in individually defining the severity of inflammation, fibrosis, or smooth muscle hyperplasia/hypertrophy (SMH) within such strictures. We developed and internally validated a radiomics-based machine learning model for separately characterizing the degree of histopathological inflammation, fibrosis, and SMH in CD strictures and compared it with centrally read visual radiologist scoring of CTE. METHODS: This single-center, cross-sectional study included 100 patients with CD (n = 66 for discovery; n = 34 for validation) with terminal ileal strictures confirmed on diagnostic CTE within 15 weeks of intestinal resection. Histopathological specimens were scored for inflammation, fibrosis, and SMH and spatially linked with corresponding pre-surgical CTE sequences. Annotated stricture regions on CTE were scored visually by radiologists as well as underwent three-dimensional radiomics-based machine-learning analysis; both evaluated against histopathology scoring. RESULTS: Three distinct sets of radiomic features capturing textural heterogeneity within strictures were separately linked with severe inflammation, fibrosis, and SMH across both discovery (accuracy = 0.74, 0.71, 0.71, respectively) and validation (accuracy = 0.74, 0.68, 0.68, respectively) cohorts. Radiologist visual scoring was limited, with accuracy of 0.51 for identifying severe inflammation, 0.48 for severe fibrosis, and 0.58 for severe SMH. Augmenting radiologist scoring with radiomic features yielded improved performance in identifying severe inflammation, fibrosis, or SMH compared to radiologist scoring alone. DISCUSSION: Radiomic features of CD strictures on CTE can identify severe histopathological fibrosis, inflammation, and smooth muscle hyperplasia and perform better than radiologist visual scoring in stricture characterization.