{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T19:01:13Z","timestamp":1790794873929,"version":"4.1.0"},"reference-count":41,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2026,8,8]],"date-time":"2026-08-08T00:00:00Z","timestamp":1786147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["P30 DK078392"],"award-info":[{"award-number":["P30 DK078392"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01 EB030582"],"award-info":[{"award-number":["R01 EB030582"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Anti-tumor necrosis factor (TNF) medical therapy is a major advancement for Crohn\u2019s disease (CD) management, yet many patients fail to respond. This study aims to develop and evaluate a multi-modal ensemble model to predict anti-TNF treatment response in pediatric patients with CD using pre-treatment magnetic resonance enterography (MRE) and non-imaging clinical data.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective study included 92 pediatric CD patients (median [IQR] age, 14.6 [12.7, 16.6] years; 65.2% male; 37 responders and 55 non-responders) who underwent MRE within 3 months before initiating anti-TNF therapy between 2009 and 2021. Responders achieved mucosal healing within 36\u2009months; non-responders did not, underwent surgery, or changed therapy. Four pre-treatment feature sets were used: (1) radiologist MRE assessment, (2) radiomic features from bowel regions, (3) deep learning features from representative bowel images, and (4) non-imaging clinical features. A 2-stage stacking ensemble model was trained and evaluated. The area under the receiver operating characteristic curve (AUROC) was the primary performance metric.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The multi-modal ensemble model integrating all MRE-based features achieved an AUROC of 0.771 [95% confidence intervals (CI): 0.745, 0.797], outperforming models using individual feature types (radiologist assessment: 0.706 [0.691, 0.721]; radiomic: 0.710 [0.691, 0.729]; deep learning: 0.724 [0.700, 0.749]). Combining MRE and clinical features (0.739 [0.696, 0.781]) achieved the highest AUROC of 0.817 [0.793, 0.842], though not significantly (P\u2009=\u2009.10). Radiomic features reflecting intensity distribution and texture heterogeneity were most predictive of treatment response.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A multi-modal ensemble model combining MRE-based radiologist assessment, radiomic, deep learning, and clinical features accurately predicted anti-TNF treatment response in pediatric CD patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ibd\/izag156","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:41:52Z","timestamp":1784893312000},"page":"1980-1992","source":"Crossref","is-referenced-by-count":0,"title":["Multi-modal ensemble learning prediction of anti-TNF treatment response in pediatric patients with Crohn\u2019s disease using clinical, radiomic, and deep learning MR enterography features"],"prefix":"10.1093","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5267-2875","authenticated-orcid":false,"given":"Hailong","family":"Li","sequence":"first","affiliation":[{"name":"Imaging Research Center, Department of Radiology, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Artificial Intelligence Imaging Research Center, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Department of Radiology, University of Cincinnati College of Medicine , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhixiu","family":"Lu","sequence":"additional","affiliation":[{"name":"Imaging Research Center, Department of Radiology, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Denson","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Pan","sequence":"additional","affiliation":[{"name":"Imaging Research Center, Department of Radiology, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Smith","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan","family":"Murphy","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily R","family":"Miraldi","sequence":"additional","affiliation":[{"name":"Division of Immunobiology, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Division of Biomedical Informatics, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Department of Pediatrics, University of Cincinnati College of Medicine , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander G","family":"Miethke","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Department of Pediatrics, University of Cincinnati College of Medicine , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6472-9210","authenticated-orcid":false,"given":"Jasbir","family":"Dhaliwal","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Division of Biomedical Informatics, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Department of Pediatrics, University of Cincinnati College of Medicine , Cincinnati, OH,","place":["United States"]},{"name":"Cincinnati Children\u2019s Hospital Medical Center James M. Anderson Center for Health Systems Excellence, , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lee A","family":"Denson","sequence":"additional","affiliation":[{"name":"Division of Gastroenterology, Hepatology, and Nutrition, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Department of Pediatrics, University of Cincinnati College of Medicine , Cincinnati, OH,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lili","family":"He","sequence":"additional","affiliation":[{"name":"Imaging Research Center, Department of Radiology, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Artificial Intelligence Imaging Research Center, Cincinnati Children\u2019s Hospital Medical Center , Cincinnati, OH,","place":["United States"]},{"name":"Department of Radiology, University of Cincinnati 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