Predicting Ustekinumab Treatment Response in Crohn's Disease Using Pre-Treatment Biopsy Images.

Motivation: Crohn's disease (CD) exhibits substantial variability in response to biological therapies such as ustekinumab (UST), a monoclonal antibody targeting interleukin-12/23. However, predicting individual treatment responses remains difficult due to the lack of reliable histopathological biomarkers and the morphological complexity of tissue. While recent deep learning methods have leveraged whole-slide images (WSIs), most lack effective mechanisms for selecting relevant regions and integrating patch-level evidence into robust patient-level predictions. Therefore, A framework that captures local histological cues and global tissue context is needed to improve prediction performance.Ustekinumab (UST) is a relatively recent biologic agent used in the treatment of Crohn's Disease (CD). Clinical studies on the treatment response of UST are relatively scarce. However, its efficacy varies among CD patients, highlighting the need for accurate to prediction of its treatment response. In this paper, We developed an artificial intelligence (AI) model based on whole-slide images (WSIs) and weakly supervised learning to predict the treatment response of UST in CD patients.

Results: We propose a novel clustering-enhanced weakly supervised learning framework to predict UST treatment response from pre-treatment WSIs of CD patients. First, patches from WSIs were encoded using a pre-trained vision foundation model, and k-means clustering was applied to identify representative morphological patterns. Discriminative patches associated with treatment outcomes were selected via a DenseNet-based classifier, with Grad-CAM used to enhance interpretability. To aggregate patch-level predictions, we adopted a multi-instance learning approach, from which whole-slide features were extracted using both patch likelihood histograms and bag-of-words representations. These features were subsequently used to train a classifier for final response prediction. Experimental results on an independent test set demonstrated that our WSI-level model achieved superior predictive performance with an AUC of 0.938 (95% CI: 0.879-0.996), sensitivity of 0.951, and specificity of 0.825, outperforming baseline patch-level models. These findings suggest that our method enables accurate, interpretable, and scalable prediction of biological therapy response in CD, potentially supporting personalized treatment strategies in clinical settings.402 tissue samples from CD patients treated with UST were categorized into non-response and response groups based on clinical outcomes. Initially, we selected relevant patches from WSIs, then patch-level treatment efficacy predictions were constructed using deep learning methods. Subsequently, pathological features generated by patches predict results aggregation were combined with various machine learning algorithms to develop a WSI-level AI model. This enables automatic prediction of UST treatment response for CD from label-free WSIs. Our model demonstrated competitive performance in predicting UST treatment response, with AUC of 0.866 (95%CI:0.865-0.867), sensitivity of 0.807, and specificity of 0.746 at the patch-level in the independent testset. The multi-instance learning (MIL) method, which aggregates patch-level result features to predict WSI-level treatment response, further enhanced the model's performance. Our model achieved an AUC of 0.938 (95%CI:0.879-0.996), with a sensitivity of 0.951 and a specificity of 0.825 in the independent test set, surpassing patch-level prediction performance.The AI model developed in this study, based on pre-treatment biopsy pathology images, accurately predicts UST treatment response in CD patients and can potentially be extended to other similar prediction tasks.

Availability and implementation: https://github.com/caicai2526/USTAIM.

Supplementary information: Supplementary data are available at Bioinformatics online.