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MRI radiomics features were extracted from T2WI images before (pre-nRCT-RF) and after (post-nRCT-RF) neoadjuvant radiochemotherapy, and the variation of radiomics features before and after neoadjuvant radiochemotherapy (delta-RF) were calculated. Eight, eight, and five most relevant features were identified for pre-nRCT-RF, post-nRCT-RF, and delta-RF, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>Eighty-six patients were included and randomized 3:1 to the training and test set (n\u2009=\u200965 and n\u2009=\u200921, respectively). The prediction model based on delta-RF had areas under the curve (AUCs) of 0.80 and 0.85 in the training and test set, respectively. A higher rate of difficult operations was observed in patients with severe inflammation (65.5% vs. 42.9%, P\u2009=\u20090.045).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The prediction model based on MRI delta-RF may be a useful tool for predicting severe inflammatory response after neoadjuvant radiochemotherapy in locally advanced RC patients.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00423-024-03416-7","type":"journal-article","created":{"date-parts":[[2024,7,17]],"date-time":"2024-07-17T05:01:58Z","timestamp":1721192518000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Magnetic resonance imaging radiomics-based prediction of severe inflammatory response in locally advanced rectal cancer patients after neoadjuvant radiochemotherapy"],"prefix":"10.1007","volume":"409","author":[{"given":"Li","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenchao","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Engeng","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,17]]},"reference":[{"issue":"3","key":"3416_CR1","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","volume":"71","author":"H Sung","year":"2021","unstructured":"Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. 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