{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,21]],"date-time":"2026-08-21T12:21:40Z","timestamp":1787314900792,"version":"build-2736575974"},"reference-count":36,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072004"],"award-info":[{"award-number":["82072004"]}],"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":["81801781"],"award-info":[{"award-number":["81801781"]}],"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":["82172025"],"award-info":[{"award-number":["82172025"]}],"id":[{"id":"10.13039\/501100001809","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":[[2024,10]]},"abstract":"\n Background<\/jats:title>\n Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)\u2010based tumor heterogeneity in assessing ALN metastasis in BC is unclear.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To assess the value of deep learning (DL)\u2010derived kinetic heterogeneity parameters based on BC dynamic contrast\u2010enhanced (DCE)\u2010MRI to infer the ALN status.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Subjects<\/jats:title>\n 1256\/539\/153\/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively.<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n 1.5\u2009T\/3.0\u2009T, non\u2010contrast T1\u2010weighted spin\u2010echo sequence imaging (T1WI), DCE\u2010T1WI, and diffusion\u2010weighted imaging.<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n \n Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE\u2010MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KH\n image<\/jats:sup>\n (kinetic heterogeneity of DCE\u2010MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated.\n <\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Chi\u2010squared, Fisher's exact, Student's\n t<\/jats:italic>\n , Mann\u2013Whitney\n U<\/jats:italic>\n tests, and receiver operating characteristics (ROC) analysis were performed.\n P<\/jats:italic>\n \u2009<\u20090.05 was considered significant.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785\u20130.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC\u2009=\u20090.685\u20130.868). The patients with larger tumor sizes (>5\u2009cm) were more susceptible to ALN metastasis with KH\n image<\/jats:sup>\n scores of 0.527\u20130.827.\n <\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC.<\/jats:p>\n <\/jats:sec>\n \n Level of Evidence<\/jats:title>\n 3<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy<\/jats:title>\n Stage 2<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.29225","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T05:34:05Z","timestamp":1704951245000},"page":"1352-1364","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["MRI\u2010Based<\/scp>\n Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid\n CNN\u2010RNN<\/scp>\n Model"],"prefix":"10.1002","volume":"60","author":[{"given":"Yi\u2010Jun","family":"Guo","sequence":"first","affiliation":[{"name":"Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education Tianjin's Clinical Research Center for Cancer Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Biomedical Engineering & Technology Tianjin Medical University Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiology Baoding No. 1 Central Hospital Baoding 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Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education Tianjin's Clinical Research Center for Cancer Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education Tianjin's Clinical Research Center for Cancer Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei\u2010Fang","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education Tianjin's Clinical Research Center for Cancer Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing\u2010Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Breast Imaging The Affiliated Hospital of Qingdao University Qingdao China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7739-4891","authenticated-orcid":false,"given":"Wen\u2010Juan","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Breast Imaging, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education Tianjin's Clinical Research Center for Cancer Tianjin 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