Viswanath, Satish E

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.

Abstract Background and Aims 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 histopathological inflammation and fibrosis in CD strictures and compared it to centrally read visual radiologist scoring of MRE. Methods This single-centre, 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 were evaluated against histopathology. Results Two distinct sets of radiomic features capturing textural heterogeneity within strictures were linked with each of severe inflammation or severe fibrosis across both the discovery (area under the curve [AUC = 0.69, 0.83] and validation [AUC = 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. Conclusions Radiomic features of CD strictures on MRE can accurately identify severe histopathological inflammation and severe histopathological fibrosis, as well as augment performance of the radiologist visual scoring in stricture characterization.

Abstract Background and Aims Perianal fistuliing Crohn’s disease [PFCD] is an aggressive phenotype of Crohn’s disease defined by frequent relapses and disabling symptoms. A novel consensus classification system was recently outlined by the TOpCLASS consortium, which seeks to unify disease severity with patient-centred goals but has not yet been validated. We aimed to apply this to a real-world cohort and to identify factors that predict transition between classes over time. Methods We identified all patients with PFCD and at least one baseline and one follow-up pelvic MRI [pMRI]. TOpCLASS classification, disease characteristics, and imaging indices were collected retrospectively at time periods corresponding with respective MRIs. Results We identified 100 patients with PFCD, of whom 96 were assigned TOpCLASS Classes 1–2c at baseline. Most patients [78.1%] started in Class 2b, but changes in classification were observed in 52.1% of all patients. Male sex [72.0%, 46.6%, 40.0%, p = 0.03] and prior perianal surgery [52.0% vs 44.6% vs 40.0%, p = 0.02] were more frequently observed in those with improved class compared to unchanged and worsened class. Baseline pMRI indices were not associated with changes in classification; however, greater improvements in mVAI, MODIFI-CD, and PEMPAC were seen among those who improved. Linear mixed effect modelling identified only male sex [-0.31, 95% CI -0.60 to -0.02] with improvement in class. Conclusion The TOpCLASS classification highlights the dynamic nature of PFCD over time. However, our ability to predict transitions between classes remains limited and requires prospective assessment. Improvement in MRI index scores over time was associated with a transition to lower TOpCLASS classification.