P0616 ViT-based deep learning and unsupervised clustering analysis in Crohn’s Disease based on body composition to identify distinct phenogroups and predict the effectiveness of anti-TNF therapy

Abstract

Background: Anti-TNF therapy is considered as a crucial treatment in Crohn's disease (CD). However, it has a primary nonresponse rate in up to 30% of patients and secondary loss of response rate of about 5% per patient-year with unknown reason. Herein, we aimed to establish an artificial intelligence algorithm to identify patients with beneficial response and predict the effectiveness of anti-TNF theray based on body composition.Methods: Between July 2020 and August 2023, 105 CD patients who underwent anti-TNF therapy from Shanghai Ninth people's Hospital were enrolled in this study. An unsupervised clustering analysis (K-means clustering) was utilized to classify subjects into distinct groups based on body composition parameters before anti-TNF therapy. We analyzed the relationship between different phenogroups and the response to anti-TNF therapy. Besides, radiomics features were extracted from subcutaneous fat (SF), visceral fat (VF), and skeletal muscle (SM) at the level of L3/L4 based on CTE. A deep learning model based on Vision Transformer (ViT) was established to predict the response of anti-TNF therapy.Results: Our analysis identified three phenogroups: Cluster 1 (PhAhigh/BCMhigh/BMChigh/SMIhigh/BMIhigh/proteinhigh/IWhigh/EWhigh), Cluster 2 (PhAlow/BCMlow/BMClow/SMIlow/VFAhigh/BFPhigh/proteinlow/IWlow/EWlow) and Cluster 3 (VFAlow/BMIlow/BFPlow). Patients in Cluster 1 showed best response of anti-TNF thrapy (p = 0.023). Among patients in Cluster 3, those with high nutritional risks showed significantly earlier loss of response than those with low nutritional risks (p = 0.041). The prediction model achieved satisfactory accuracy in predicting the effectiveness of anti-TNF therapy (SF: AUC = 0.700; VF: AUC = 0.745; SM: AUC = 0.785) in the validation cohort.Conclusion: Patients with good nutritional status have a better response to anti-TNF therapy in CD. Earlier loss of response occurred in patients with high nutritional risks, which may be improved by nutritional support. Subcutaneous fat, visceral fat, and skeletal muscle at the level of L3/L4 showed a satisfactory performance in predicting the response to anti-TNF therapy.