{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T23:24:48Z","timestamp":1789255488479,"version":"build-2803163510"},"reference-count":35,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T00:00:00Z","timestamp":1777766400000},"content-version":"vor","delay-in-days":2,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82303742"],"award-info":[{"award-number":["82303742"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Response to neoadjuvant chemoradiotherapy (NACRT) in locally advanced rectal cancer (LARC) is highly heterogeneous. Reliable pretreatment prediction of tumor regression and prognosis remains an unmet clinical need to optimize personalized management.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A multicenter retrospective study of 434 Stage II\u2010III LARC patients was conducted across three tertiary hospitals. Pretreatment T2\u2010weighted MRI was used to build intratumoral and peritumoral macrohabitats. Local radiomic features within the tumor were clustered using K\u2010means to generate intratumoral habitats, with the optimal cluster number determined by the Calinski\u2013Harabasz score. Radiomic and 3D deep\u2010learning features from each habitat and the peritumoral region were fused after LASSO\u2010based selection. Machine\u2010learning classifiers (support vector machine, logistic regression, multilayer perceptron) were trained to predict tumor regression grade (TRG). Performance was assessed by ROC and decision\u2013curve analyses, and prognostic value for progression\u2010free survival (PFS) was evaluated using Kaplan\u2013Meier analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The macro habitat\u2010based fusion model demonstrated superior performance compared with intratumoral, peritumoral, or single\u2010habitat models, achieving AUCs of 0.807\u20130.830 in the external validation cohort. The derived risk score showed a significant association with progression\u2010free survival (PFS) (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.011 in the training and\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.030 in the validation cohorts).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The macro habitat\u2010based MRI radiomics and deep learning fusion model provides a noninvasive, interpretable, and robust biomarker for predicting treatment response and prognosis in LARC. It holds potential to guide personalized therapeutic strategies, including organ\u2010preserving approaches and tailored surveillance after NACRT.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cam4.71773","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T04:21:04Z","timestamp":1777868464000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Macro Habitat\u2010Based\n                    <scp>T2<\/scp>\n                    \u2010Weighted\n                    <scp>MRI<\/scp>\n                    Radiomics and Deep Learning Fusion for Predicting Treatment Response and Prognosis After Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer"],"prefix":"10.1002","volume":"15","author":[{"given":"Xiaoli","family":"Jin","sequence":"first","affiliation":[{"name":"Department of Gastrointestinal Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine  Hangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, the Second Affiliated Hospital and Cancer Institute, Zhejiang University School of Medicine  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeting","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine  Hangzhou Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3873-9041","authenticated-orcid":false,"given":"Xin","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology, Guangzhou First People's Hospital School of Medicine, South China University of Technology  Guangzhou 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