{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T11:44:55Z","timestamp":1753875895795,"version":"3.41.2"},"reference-count":43,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82441029","62171230","62101365","92159301","62301263","62301265","62302228","82302291","82302352","62401272"],"award-info":[{"award-number":["82441029","62171230","62101365","92159301","62301263","62301265","62302228","82302291","82302352","62401272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFC3402800"],"award-info":[{"award-number":["2023YFC3402800"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,2]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Crohn\u2019s 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>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\u20130.996), 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>https:\/\/github.com\/caicai2526\/USTAIM.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaf301","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T15:42:32Z","timestamp":1747237352000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting ustekinumab treatment response in Crohn\u2019s disease using pre-treatment biopsy images"],"prefix":"10.1093","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9582-0348","authenticated-orcid":false,"given":"Chengfei","family":"Cai","sequence":"first","affiliation":[{"name":"Jiangsu 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210008,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caiyun","family":"Lv","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, the Second Affiliated Hospital of Soochow University , Suzhou 215004,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0787-3424","authenticated-orcid":false,"given":"Yiping","family":"Jiao","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lanqing","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Pathology and Pathophysiology, Medical College of Soochow University, Soochow University , Suzhou 215123,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pathology and Pathophysiology, Medical College of Soochow University, Soochow University , Suzhou 215123,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qi","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Pathology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School , Nanjing 210008,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qianyun","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Pathology, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School , Nanjing 210008,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Xu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Intelligent Medical Image Computing, School of Artificial Intelligence, Nanjing University of Information Science and Technology , Nanjing 210044,","place":["China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, the Second Affiliated Hospital of Soochow University , Suzhou 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