{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T15:02:02Z","timestamp":1780153322508,"version":"3.54.0"},"reference-count":28,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T00:00:00Z","timestamp":1779062400000},"content-version":"vor","delay-in-days":118,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2023YFF1204600"],"award-info":[{"award-number":["2023YFF1204600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project Science Foundation of Heilongjiang Province","award":["ZD2022H003"],"award-info":[{"award-number":["ZD2022H003"]}]},{"name":"Key Discipline Construction Project of Shanghai Municipal Health System","award":["2024ZDXK0061"],"award-info":[{"award-number":["2024ZDXK0061"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20478"],"award-info":[{"award-number":["U23A20478"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82441008"],"award-info":[{"award-number":["82441008"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The recurrence of colorectal cancer (CRC) after surgery poses a significant threat to patient recovery, but current prediction methods lack sensitivity. In this study, we aimed to develop a radiomics-based model for recurrence prediction and to investigate its association with the tumor immune microenvironment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospectively cohort study was conducted with 197 patients from the Second Affiliated Hospital of Harbin Medical University (Harbin, China; training set) and 130 patients from Shandong First Medical University Affiliated Central Hospital (Jinan, China; validation set). Clinical characteristics and blood biomarkers (alpha-fetoprotein, carcinoembryonic antigen, and cancer antigen 19-9) were compared between the recurrence and nonrecurrence groups, and magnetic resonance imaging (MRI)-derived radiomics features were extracted by using 3D Slicer and PyRadiomics. After this, LASSO regression was used to identify key features, and univariate analysis was used to retain statistically significant predictors. A multivariate logistic regression model was then developed to predict recurrence, and the relationship between radiomics features and immune cells was subsequently explored by using a CRC mouse model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>There were no significant differences in the clinical characteristics between recurrence and nonrecurrence groups. The initial model, comprising 10 features, achieved an area under the curve (AUC) of 0.99 in the training set but only 0.6 in the validation set. After excluding six features, the refined model, which included original gray level co-occurrence matrix sum entropy (OGSE), log-sigma-3-0-mm-3D neighborhood gray tone difference matrix Busyness (LNB), original first order total energy (OFTE), wavelet-LLH gray level size zone matrix zone variance (WGZV), achieved an AUC of 0.98 in the validation set, outperforming traditional blood biomarkers. In a murine model, regulatory T cells exhibited a strong positive correlation with OGSE (r\u2009=\u20090.84, P\u2009&amp;lt;\u20090.001) and moderate correlations with OFTE (r\u2009=\u20090.41, P\u2009=\u20090.035) and WGZV (r\u2009=\u20090.52, P\u2009=\u20090.007), indicating a link between radiomics features and immune cell infiltration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>MRI-derived radiomics features, particularly OGSE, LNB, OFTE, and WGZV, can effectively predict postoperative CRC recurrence. These features are correlated with the tumor immune microenvironment, supporting the use of radiomics as a noninvasive tool for tumor assessment in CRC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/gastro\/goag039","type":"journal-article","created":{"date-parts":[[2026,4,19]],"date-time":"2026-04-19T11:38:55Z","timestamp":1776598735000},"source":"Crossref","is-referenced-by-count":0,"title":["Predicting postoperative recurrence in colorectal cancer using MRI-derived radiomics features and their correlation with the tumor immune microenvironment"],"prefix":"10.1093","volume":"14","author":[{"given":"Jie","family":"Zhao","sequence":"first","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Clinical Laboratory, Shandong Provincial Hospital affiliated to Shandong First Medical University , Jinan, Shandong, 210000,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjie","family":"Xin","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juxian","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xue","family":"Qiao","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zonghui","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Shanghai Jing\u2019an District Central Hospital , Shanghai, 200000,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Orthopedics, The First Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]},{"name":"State Key Laboratory of Frigid Zone Cardiovascular Diseases (SKLFZCD), Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huijie","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Second Affiliated Hospital of Harbin Medical University , Harbin, Heilongjiang, 150086,","place":["P. R. China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,18]]},"reference":[{"key":"2026053010365308800_goag039-B1","doi-asserted-by":"crossref","first-page":"556","DOI":"10.1038\/s41416-022-02069-x","article-title":"The Glasgow Microenvironment Score and risk and site of recurrence in TNM I-III colorectal cancer","volume":"128","author":"Alexander","year":"2023","journal-title":"Br J Cancer"},{"key":"2026053010365308800_goag039-B2","doi-asserted-by":"crossref","first-page":"116883","DOI":"10.1016\/j.biopha.2024.116883","article-title":"Rapamycin circumvents anti PD-1 therapy resistance in colorectal cancer by reducing PD-L1 expression and optimizing the tumor microenvironment","volume":"176","author":"Jia","year":"2024","journal-title":"Biomed Pharmacother"},{"key":"2026053010365308800_goag039-B3","doi-asserted-by":"crossref","first-page":"206","DOI":"10.21037\/jgo-22-1169","article-title":"Clinical significance and changes to the immune microenvironment of colorectal cancer patients with liver metastasis","volume":"14","author":"Wang","year":"2023","journal-title":"J Gastrointest Oncol"},{"key":"2026053010365308800_goag039-B4","doi-asserted-by":"crossref","first-page":"216778","DOI":"10.1016\/j.canlet.2024.216778","article-title":"Association of preoperative aspartate aminotransferase to platelet ratio index with outcomes and tumour microenvironment among colorectal cancer with liver metastases","volume":"588","author":"Chen","year":"2024","journal-title":"Cancer Lett"},{"key":"2026053010365308800_goag039-B5","doi-asserted-by":"crossref","first-page":"121485","DOI":"10.1016\/j.biomaterials.2022.121485","article-title":"LncRNA-targeting bio-scaffold mediates triple immune effects for postoperative colorectal cancer immunotherapy","volume":"284","author":"Liu","year":"2022","journal-title":"Biomaterials"},{"key":"2026053010365308800_goag039-B6","doi-asserted-by":"crossref","first-page":"1671","DOI":"10.1007\/s00595-021-02273-x","article-title":"Postoperative elevation in the plasma CCL2 level is a predictive biomarker of colorectal cancer recurrence","volume":"51","author":"Fukunaga","year":"2021","journal-title":"Surg Today"},{"key":"2026053010365308800_goag039-B7","doi-asserted-by":"crossref","first-page":"644933","DOI":"10.3389\/fonc.2021.644933","article-title":"CT-based radiomics signature: a potential biomarker for predicting postoperative recurrence risk in stage II colorectal cancer","volume":"11","author":"Fan","year":"2021","journal-title":"Front Oncol"},{"key":"2026053010365308800_goag039-B8","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1007\/s00330-021-08104-4","article-title":"Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study","volume":"32","author":"Badic","year":"2022","journal-title":"Eur Radiol"},{"key":"2026053010365308800_goag039-B9","doi-asserted-by":"crossref","first-page":"103735","DOI":"10.1016\/j.esmoop.2024.103735","article-title":"MRI-based radiomics for stratifying recurrence risk of early-onset rectal cancer: a multicenter study","volume":"9","author":"Xie","year":"2024","journal-title":"ESMO Open"},{"key":"2026053010365308800_goag039-B10","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1245\/s10434-020-08974-w","article-title":"Radiomics approach outperforms diameter criteria for predicting pathological lateral lymph node metastasis after neoadjuvant (chemo)radiotherapy in advanced low rectal cancer","volume":"27","author":"Nakanishi","year":"2020","journal-title":"Ann Surg Oncol"},{"key":"2026053010365308800_goag039-B11","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1007\/s00261-023-03910-4","article-title":"MRI radiomics signature to predict lymph node metastasis after neoadjuvant chemoradiation therapy in locally advanced rectal cancer","volume":"48","author":"Fang","year":"2023","journal-title":"Abdom Radiol (NY)"},{"key":"2026053010365308800_goag039-B12","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1186\/s13045-022-01225-3","article-title":"Development of a novel combined nomogram model integrating deep learning-pathomics, radiomics and immunoscore to predict postoperative outcome of colorectal cancer lung metastasis patients","volume":"15","author":"Wang","year":"2022","journal-title":"J Hematol Oncol"},{"key":"2026053010365308800_goag039-B13","doi-asserted-by":"crossref","first-page":"872656","DOI":"10.3389\/fonc.2022.872656","article-title":"Radiomics-guided precision medicine approaches for colorectal cancer","volume":"12","author":"Quraishi","year":"2022","journal-title":"Front Oncol"},{"key":"2026053010365308800_goag039-B14","doi-asserted-by":"crossref","first-page":"689509","DOI":"10.3389\/fonc.2021.689509","article-title":"Radiomics and radiogenomics in evaluation of colorectal cancer liver metastasis","volume":"11","author":"Wang","year":"2021","journal-title":"Front Oncol"},{"key":"2026053010365308800_goag039-B15","doi-asserted-by":"crossref","first-page":"102271","DOI":"10.1016\/j.eclinm.2023.102271","article-title":"Deep radiomics-based fusion model for prediction of bevacizumab treatment response and outcome in patients with colorectal cancer liver metastases: a multicentre cohort study","volume":"65","author":"Zhou","year":"2023","journal-title":"EClinicalMedicine"},{"key":"2026053010365308800_goag039-B16","doi-asserted-by":"crossref","first-page":"2307","DOI":"10.1007\/s00259-018-4100-6","article-title":"Radiomics analysis of pre-treatment [18F]FDG PET\/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment","volume":"45","author":"van Helden","year":"2018","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"2026053010365308800_goag039-B17","doi-asserted-by":"crossref","first-page":"861892","DOI":"10.3389\/fonc.2022.861892","article-title":"Assessment of primary colorectal cancer CT radiomics to predict metachronous liver metastasis","volume":"12","author":"Li","year":"2022","journal-title":"Front Oncol"},{"key":"2026053010365308800_goag039-B18","first-page":"52","article-title":"Radiomics for the prediction of treatment outcome and survival in patients with colorectal cancer: a systematic review","volume":"20","author":"Staal","year":"2021","journal-title":"Clin Colorectal Cancer"},{"key":"2026053010365308800_goag039-B19","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1148\/radiol.211986","article-title":"MRI radiomics model predicts pathologic complete response of rectal cancer following chemoradiotherapy","volume":"303","author":"Shin","year":"2022","journal-title":"Radiology"},{"key":"2026053010365308800_goag039-B20","doi-asserted-by":"crossref","first-page":"4644","DOI":"10.3390\/cancers15184644","article-title":"Multifaceted insights into innovative approaches to treating colorectal cancer metastasis: from emerging biological factors to radiomics","volume":"15","author":"Ottaiano","year":"2023","journal-title":"Cancers (Basel)"},{"key":"2026053010365308800_goag039-B21","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1186\/s13058-024-01921-7","article-title":"Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and -zero breast cancers: a multicenter study","volume":"26","author":"Chen","year":"2024","journal-title":"Breast Cancer Res"},{"key":"2026053010365308800_goag039-B22","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1093\/carcin\/bgad098","article-title":"A radiomics and genomics-derived model for predicting metastasis and prognosis in colorectal cancer","volume":"45","author":"Li","year":"2024","journal-title":"Carcinogenesis"},{"key":"2026053010365308800_goag039-B23","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1038\/s41586-018-0694-x","article-title":"Lineage tracking reveals dynamic relationships of T cells in colorectal cancer","volume":"564","author":"Zhang","year":"2018","journal-title":"Nature"},{"key":"2026053010365308800_goag039-B24","doi-asserted-by":"crossref","first-page":"903564","DOI":"10.3389\/fimmu.2022.903564","article-title":"Colorectal cancer-infiltrating regulatory T cells: functional heterogeneity, metabolic adaptation, and therapeutic targeting","volume":"13","author":"Aristin Revilla","year":"2022","journal-title":"Front Immunol"},{"key":"2026053010365308800_goag039-B25","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.1016\/j.acra.2023.10.008","article-title":"Radiomics model for predicting FOXP3 expression level and survival in clear cell renal carcinoma","volume":"31","author":"Wang","year":"2024","journal-title":"Acad Radiol"},{"key":"2026053010365308800_goag039-B26","doi-asserted-by":"crossref","first-page":"1895","DOI":"10.3390\/cancers14081895","article-title":"Combined radiomic and immune genomic expression for accurate prognostic classification and identification of therapeutic targets in stage III colorectal cancer","volume":"14","author":"Huang","year":"2022","journal-title":"Cancers (Basel)"},{"key":"2026053010365308800_goag039-B27","first-page":"85","article-title":"Machine learning-based MRI radiomics predict IL18 expression and overall survival of low-grade glioma patients","volume":"9","author":"Zhang","year":"2023","journal-title":"NPJ Precis Oncol"},{"key":"2026053010365308800_goag039-B28","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.radonc.2021.07.004","article-title":"A radiomic signature model to predict the chemoradiation-induced alteration in tumor-infiltrating CD8+ cells in locally advanced rectal cancer","volume":"162","author":"Jeon","year":"2021","journal-title":"Radiother Oncol"}],"container-title":["Gastroenterology Report"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/gastro\/article-pdf\/doi\/10.1093\/gastro\/goag039\/68317633\/goag039.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gastro\/article-pdf\/doi\/10.1093\/gastro\/goag039\/68317633\/goag039.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T14:37:03Z","timestamp":1780151823000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/gastro\/article\/doi\/10.1093\/gastro\/goag039\/8684145"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,20]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1093\/gastro\/goag039","relation":{},"ISSN":["2052-0034"],"issn-type":[{"value":"2052-0034","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,2]]},"published":{"date-parts":[[2026,1,20]]},"article-number":"goag039"}}