P277 Predicting disease relevant features in Crohn's Disease and Ulcerative Colitis from Haematoxylin & Eosin stained whole slide images using self-supervised deep learning. (30th January 2023) - Mokhtari, R; Hamidinekoo, A; Lewis, A; Sutton, D; Angermann, B; Gehrmann, U; Lundin, P; Khachapuridze, N; Adissu, H; Marks, D; Cairns, J; Burlutskiy, N
Abstract: Background: Histopathological endpoints are evolving as a treatment target in Inflammatory Bowel Disease (IBD). Use of histology to screen entrants could add value in IBD clinical trials; for example, by refining eligibility criteria to ensure studies recruit patients with definitive active inflammation at the microscopic level. Several histopathological indices have been developed, but the relative complexity of available scores hinders the development of an AI algorithm without large-scale labour-intensive annotation by a pathologist. We aimed to develop computer vision tools to assist decoding the complex clinical disease features at the histological level for both Crohn's Disease (CD) and Ulcerative Colitis (UC). This will inform understanding of disease pathology and patient stratification to support clinical trial development strategies. Methods: A total of 1397 clinically annotated Haematoxylin & Eosin (H&E) images were included from 418 CD and 218 UC patients enrolled in a multicentred longitudinal Study of a Prospective Adult Research Cohort with IBD (SPARC IBD) obtained from the IBD Plexus program of the Crohn's & Colitis Foundation. We developed an image quality control (QC) algorithm to automatically identify image and tissue processing/staining artefacts negatively impacting analysis (e.g., out-of-focus, tissue folds, overstained regions) and excluded these regions (Fig. 1). Next, a self-supervised learning (SSL) deep learning computer vison model was developed and trained to predict disease relevant features including disease diagnosis and lesional macroscopic appearance (inflammation, erosions and ulcers). Finally, to better understand the model's predictions, we generated heatmap overlays on the tissue that show regions which the model considers to be most predictive (Fig. 2) and shared these with pathologists for qualitative evaluation. Results: We find that the SSL model performs well on different downstream classification tasks such as UC vs CD (area under curve (AUC) = 0.79) and normal vs lesional tissue (AUC = 0.76). Specialist pathologist collaboration further confirmed that the heatmap overlays identified clinically relevant tissue features, including inflammatory cell infiltrates (Fig. 2). Conclusion: These encouraging results support further exploration of this deep-learning algorithm to distinguish disease specific characteristics in this set of images from CD and UC patients. Further work is ongoing to validate the heatmap approach on endoscopic scores, we also plan to validate our model on IBD clinical trial datasets.