Endoscopic Multimodal Assessment Using Advanced Imaging Integrated AI to Predict Recurrence in pOSt-oPerativE CRohn's Disease - PROSPER Study

Background:

Up to 70% of Crohn's disease (CD) patients will undergo a surgical resection in their lifetime. However, surgery is non-curative since 50% of patients have a recurrence, and about one-third need repeat surgery. The tools currently used to assess CD recurrences, such as faecal calprotectin (FCP), cross-sectional imaging (small bowel ultrasound, MRI scan) and conventional endoscopy, have a limited role in predicting early Post-Operative CD recurrence (POCr). Distinguishing inflammatory disease recurrence from post-surgical ischemic or suture-related alterations poses a significant challenge. Endoscopic Enhanced imaging (EEI) techniques like virtual electronic chromoendoscopy (VCE) and biopsy-like probe-based confocal laser endomicroscopy (pCLE) combined with artificial intelligence, can improve the detection of mucosal/vascular changes before major alterations become evident. VCE is available simply by switching a button. The pCLE probe will be passed through the endoscope channel like a biopsy forceps, enabling real-time, histology-like images of the intestine's lining and the gut barrier.

Study summary:

This is a multicentre prospective international observational study. This study aims to introduce a novel multidimensional approach to precision imaging, enabling the identification and stratification of high-risk patients who can potentially benefit from early treatments to halt the progression of CD.

The investigators will develop a novel endoscopic assessment system using EEI to evaluate early post-surgical changes and predict POCr. By integrating with immune marker profiling, clinical data, and AI assessment of EEI and histology, the investigators further plan to improve risk stratification and reduce interobserver variability. A detailed exploratory analysis will only be done in a cohort of patients in Ireland. The correlation between the new scoring system and established endoscopic and histologic scores, cross-sectional imaging, and non-invasive markers of inflammation will be evaluated. A multimodal machine learning model will be developed on EEI videos, histology, clinical data and immune molecular analysis to stratify patients' risk of early recurrence and long-term outcomes. The study will be divided into three phases: