{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,10]],"date-time":"2026-09-10T04:53:42Z","timestamp":1789016022596,"version":"build-2803163510"},"reference-count":33,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100012175","name":"Zhejiang Traditional Chinese Medicine Administration","doi-asserted-by":"publisher","award":["2024ZL040"],"award-info":[{"award-number":["2024ZL040"]}],"id":[{"id":"10.13039\/501100012175","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017594","name":"Medical Science and Technology Project of Zhejiang Province","doi-asserted-by":"publisher","award":["2024KY052"],"award-info":[{"award-number":["2024KY052"]}],"id":[{"id":"10.13039\/501100017594","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,9]]},"abstract":"ABSTRACT<\/jats:title>\n \n Background<\/jats:title>\n Accurate preoperative assessment of perineural invasion (PNI) remains challenging in rectal cancer.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To develop assessment models based on preoperative multiparametric MRI (mpMRI) habitat analysis for evaluating PNI status and to explore their prognostic value.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n \n Six hundred and twenty\u2010one rectal cancer patients were enrolled from two centers, divided into a training set (\n n<\/jats:italic>\n \u2009=\u2009330; 65.8\u2009\u00b1\u200911.22\u2009years; 215 males), an internal validation set (in\u2010vad,\n n<\/jats:italic>\n \u2009=\u2009152; 67.85\u2009\u00b1\u200912.43\u2009years; 105 males), and an external validation set (ex\u2010vad,\n n<\/jats:italic>\n \u2009=\u2009139; 62.82\u2009\u00b1\u200911.79\u2009years; 96 males).\n <\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n \n 1.\n 5T<\/jats:styled-content>\n ,\n 3T, T2<\/jats:styled-content>\n \u2010weighted imaging using turbo spin\u2010echo sequence, diffusion\u2010weighted imaging using echo planar imaging, and contrast\u2010enhanced\n T1<\/jats:styled-content>\n \u2010weighted imaging using\n 3D<\/jats:styled-content>\n spoiled gradient echo sequence.\n <\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n Tumor voxels were partitioned into subregions using k\u2010means clustering, and habitat\u2010based submodels were developed with deep learning. The Boruta algorithm combined with univariate and multivariate analyses identified key variables.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Student's\n t<\/jats:italic>\n test, Mann\u2013Whitney\n U<\/jats:italic>\n test, chi\u2010square test, Boruta analysis, and DeLong's test. Significance was defined as\n p<\/jats:italic>\n \u2009<\u20090.05. A clinical model was constructed from selected significant variables, and a nomogram integrating the clinical model with habitat\u2010based submodels was subsequently developed.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Tumors were divided into three imaging\u2010derived subregions, generating three habitat submodels. Habitat 1, 2, 3, mrN, and mrEMVI were independent PNI variables. The nomogram exhibited the highest performance, with area under the curve (AUC) values of 0.967 (95% confidence interval [CI], 0.950\u20130.983), 0.965 (0.941\u20130.990), and 0.977 (0.949\u20131.000) in the training, in\u2010vad, and ex\u2010vad sets, respectively. Kaplan\u2013Meier analysis further confirmed its effective stratification of 3\u2010year disease\u2010free survival.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n The MRI\u2010based habitat analysis model and the derived nomogram demonstrate high predictive value for preoperative assessment of PNI in rectal cancer. The nomogram also shows promising capability for prognostic risk stratification.<\/jats:p>\n <\/jats:sec>\n \n Level of Evidence<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy Stage<\/jats:title>\n 3.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.70375","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:10:27Z","timestamp":1778857827000},"page":"629-641","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["MRI<\/scp>\n Habitat Analysis for Preoperative Prediction of Perineural Invasion and Prognostic Stratification in Rectal Cancer"],"prefix":"10.1002","volume":"64","author":[{"given":"Weiqun","family":"Ao","sequence":"first","affiliation":[{"name":"Department of Radiology Tongde Hospital of Zhejiang Province Hangzhou Zhejiang Province China"},{"name":"Zhejiang Academy of Traditional Chinese Medicine Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijiang","family":"Huang","sequence":"additional","affiliation":[{"name":"The Integrated Traditional Chinese and Western Medicine School of Clinical Medicine Zhejiang Chinese Medical University Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sikai","family":"Wu","sequence":"additional","affiliation":[{"name":"The Integrated Traditional Chinese and Western Medicine School of Clinical Medicine Zhejiang Chinese Medical University Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Pathology Tongde Hospital of Zhejiang Province Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guoqun","family":"Mao","sequence":"additional","affiliation":[{"name":"Department of Radiology Tongde Hospital of Zhejiang Province Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingfeng","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Radiology, Putuo People's Hospital School of Medicine Tongji University Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2964-4331","authenticated-orcid":false,"given":"Shuitang","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Radiology Tongde Hospital of Zhejiang Province Hangzhou Zhejiang Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21871"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-025-96618-y"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2023.05.008"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000030687"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-024-12206-9"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12967-025-06104-2"},{"key":"e_1_2_11_8_1","unstructured":"National Comprehensive Cancer Network \u201cNCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines): Rectal Cancer. 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