P153 Artificial Intelligence to predict interleukin-23 signalling activity from H&E stained whole slide images of Inflammatory Bowel Disease

Abstract

Abstract Background Interleukin23 (IL-23) is a cytokine that plays a crucial role in the pathogenesis of inflammatory bowel disease (IBD), making it a highly validated therapeutic target. Understanding the role of IL-23 in IBD at the histopathological level is crucial for determining effective treatment strategies, providing insights into IBD patients who fail to respond to targeted therapies, or predicting those who are likely to lose response. In this context, there is a surge in utilizing artificial intelligence (AI) for histopathological data in IBD and other disease indications. Here, we present an automated computer vision approach to predict IL-23 signalling activity directly from routinely stained Hematoxylin and Eosin (H&E) images Methods A total of 1502 samples with matched clinical data and H&E biopsy images were included from 991 Crohn’s disease (CD) and 511 ulcerative colitis (UC) samples. IL-23 signalling activity was calculated using gene set variation analysis on RNA-seq data collected from the same tissue biopsies. The data were obtained from the IBD Plexus program of the Crohn’s & Colitis Foundation. The proposed approach is based on vision transformers (ViTs) which is a type of deep learning model. ViTs divide the input image into fixed-size patches, transform it into linear embedding, and analyze it with the self-attention mechanism. This enables ViTs to incorporate relationships between different patches of the input image to identify regions predictive of IL-23. Our approach was trained in a weakly supervised manner to automatically identify tissue regions that correlate with IL-23 signaling activity. The model produces interpretable heatmaps to interrogate model predictions and allow clinicians to visualize and interpret the significance of different tissue regions predictive of IL-23 Results We performed 5-fold cross-validation on the splits obtained at the patient level, retaining the data distribution of IL-23 signaling activity, biopsy location, and diagnosis. We separately validated the performance of the proposed model on both disease categories, including CD and UC. The proposed approach achieved an area under the curve (AUC) of 0.82 ± 0.04 on unseen data from CD and an AUC of 0.80 ± 0.02 for UC. The 5-fold results for both disease categories are shown below Conclusion The presented results highlight the significance of computational pathology algorithms to identify IL-23 signalling activity from H&E images. Pathological interpretation from the heatmaps may help understand disease pathomechanism and optimize the treatment options for IBD patients by timely identification of IL-23 status. We are further validating the clinical utility of such heatmaps and expanding the use of H&E to predict other patient-centric endpoints