P995 Development and validation of a deep learning model based on histological characteristics to predict primary non-response of infliximab in Crohn's disease

Abstract

Abstract Background Predicting primary non-response to infliximab (IFX) is important to select the optimal therapy and utilize personalized management in patients of Crohn’s disease (CD). This study aimed to develop and validate a deep learning (DL) model based on histological characteristics for predicting primary non-response to IFX in CD. Methods A training cohort and a validation cohort were recruited from the First Affiliated Hospital of Sun Yat-sen University. Clinical features, serological biomarkers, imaging features and histological characteristics of colonoscopy biopsy at baseline of IFX therapy were obtained as candidate predictors. Primary non-response was assessed at week 14. We developed a model using a multi-instance learning framework. This model combines patch-level features from whole slide images (WSI) with multi-modal information, together with pre-treatment image features and clinical features, to predict the response to infliximab (IFX) therapy. Finally, the model was validated for area under the curve (AUC) in a reference validation cohort. Results A total of 131 patients were included in the derivation cohort. The DL model based on pathologic features only had an under-the-curve receiver operating characteristic (AUROC) value of 0.87 in the 10-fold cross-validation cohort. The mean AUROC for the model combining pathologic and radiographic features was 0.88. The DL model achieved the highest AUROC value of 0.91 when combining pathological, radiomics and clinical features (Figure 1). When the pathological film was visualized by attention thermography, it was found that the ulcerated tissue was focused on, especially in areas dominated by neutrophils, lymphocyte aggregation and abnormal crypt structure. Conclusion The study developed several accurate DL models for predicting primary non-response to IFX by WSI. The DL model combining pathological, radiomics and clinical features achieving best performance. This model may help clinicians select appropriate therapeutic strategy and avoid unnecessary exposure to IFX in CD patients.