{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T12:32:37Z","timestamp":1789648357747,"version":"4.0.0"},"reference-count":32,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T00:00:00Z","timestamp":1659312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2018JJ2641"],"award-info":[{"award-number":["2018JJ2641"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Natural Science Foundation of Hunan Province","doi-asserted-by":"publisher","award":["2022JJ30979"],"award-info":[{"award-number":["2022JJ30979"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2023,2]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background and Purpose<\/jats:title><jats:p>Early detection of non\u2010response to neoadjuvant chemoradiotherapy (nCRT) for locally advanced colorectal cancer (LARC) remains challenging. We aimed to assess whether pretreatment radiotherapy planning computed tomography (CT) radiomics could distinguish the patients with no response or no downstaging after nCRT from those with response and downstaging after nCRT.<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials and Methods<\/jats:title><jats:p>Patients with LARC who were treated with nCRT were retrospectively enrolled between March 2009 and March 2019. Traditional radiological characteristics were analyzed by visual inspection and radiomic features were analyzed through computational methods from the pretreatment radiotherapy planning CT images. Differentiation models were constructed using radiomic methods and clinicopathological characteristics for predicting non\u2010response to nCRT. Model performance was assessed for classification efficiency, calibration, discrimination, and clinical application.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>This study enrolled a total of 215 patients, including 151 patients in the training cohort (50 non\u2010responders and 101 responders) and 64 patients in the validation cohort (21 non\u2010responders and 43 responders). For predicting non\u2010response, the model constructed with an ensemble machine learning method had higher performance with area under the curve (AUC) values of 0.92 and 0.89 as compared to the model constructed with the logistic regression method (AUC: 0.72 and 0.71 for the training and validation cohorts, respectively). Both decision curve and calibration curve analyses confirmed that the ensemble machine learning model had higher prediction performance.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Pretreatment CT radiomics achieved satisfying performance in predicting non\u2010response to nCRT and could be helpful to assist in treatment planning for patients with LARC.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.5086","type":"journal-article","created":{"date-parts":[[2022,8,1]],"date-time":"2022-08-01T10:34:10Z","timestamp":1659350050000},"page":"2463-2473","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["<scp>CT<\/scp> radiomics identifying non\u2010responders to neoadjuvant chemoradiotherapy among patients with locally advanced rectal cancer"],"prefix":"10.1002","volume":"12","author":[{"given":"Zinan","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Radiology (Xiangya Hospital) Central South University  Changsha Hunan P.R. China"},{"name":"Department of Gastroenterology (The Third Xiangya Hospital) Central South University  Changsha Hunan P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4220-1981","authenticated-orcid":false,"given":"Xiaoping","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Radiology (Xiangya Hospital) Central South University  Changsha Hunan P.R. China"},{"name":"National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology Xiangya Hospital  Changsha Hunan P.R. China"},{"name":"National Clinical Research Center for Geriatric Disorders (Xiangya Hospital) Central South University  Changsha Hunan P.R. China"},{"name":"Hunan Key Laboratory of Skin Cancer and Psoriasis  Changsha Hunan P.R. China"},{"name":"Hunan Engineering Research Center of Skin Health and Disease  Changsha Hunan P.R. 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haipeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiology (Xiangya Hospital) Central South University  Changsha Hunan P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaide","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Radiology (Xiangya Hospital) Central South University  Changsha Hunan P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changyong","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology (Xiangya Hospital) Central South University  Changsha Hunan P.R. China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peipei","family":"Pang","sequence":"additional","affiliation":[{"name":"Department of Pharmaceuticals and Diagnosis GE Healthcare  Changsha P.R. 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