{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T21:56:13Z","timestamp":1786571773154,"version":"build-2736575974"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T00:00:00Z","timestamp":1717804800000},"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":["82171929"],"award-info":[{"award-number":["82171929"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2414050003969"],"award-info":[{"award-number":["2414050003969"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017712","name":"Bureau of Science and Technology of Ganzhou Municipality","doi-asserted-by":"publisher","award":["2022\u2010\u2010RC1349"],"award-info":[{"award-number":["2022\u2010\u2010RC1349"]}],"id":[{"id":"10.13039\/100017712","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":[[2025,1]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Pathological complete response (pCR) is an essential criterion for adjusting follow\u2010up treatment plans for patients with breast cancer (BC). The value of the visual geometry group and long short\u2010term memory (VGG\u2010LSTM) network using time\u2010series dynamic contrast\u2010enhanced magnetic resonance imaging (DCE\u2010MRI) for pCR identification in BC is unclear.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To identify pCR to neoadjuvant chemotherapy (NAC) using deep learning (DL) models based on the VGG\u2010LSTM network.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>Center A: 235 patients (47.7\u2009\u00b1\u200910.0\u2009years) were divided 7:3 into training (<jats:italic>n<\/jats:italic>\u2009=\u2009164) and validation set (<jats:italic>n<\/jats:italic>\u2009=\u200971). Center B: 150 patients (48.5\u2009\u00b1\u200910.4\u2009years) were used as test set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>3\u2010T, T2\u2010weighted spin\u2010echo sequence imaging, and gradient echo DCE sequence imaging.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>Patients underwent MRI examinations at three sequential time points: pretreatment, after three cycles of treatment, and prior to surgery, with tumor regions of interest manually delineated. Histopathology was the gold standard. We used VGG\u2010LSTM network to establish seven DL models using time\u2010series DCE\u2010MR images: pre\u2010NAC images (t0 model), early NAC images (t1 model), post\u2010NAC images (t2 model), pre\u2010NAC and early NAC images (t0\u2009+\u2009t1 model), pre\u2010NAC and post\u2010NAC images (t0\u2009+\u2009t2 model), pre\u2010NAC, early NAC and post\u2010NAC images (t0\u2009+\u2009t1\u2009+\u2009t2 model), and the optimal model combined with the clinical features and imaging features (combined model). The models were trained and optimized on the training and validation set, and tested on the test set.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>The DeLong, Student's <jats:italic>t<\/jats:italic>\u2010test, Mann\u2013Whitney U, Chi\u2010squared, Fisher's exact, Hosmer\u2013Lemeshow tests, decision curve analysis, and receiver operating characteristics analysis were performed. <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.05 was considered significant.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Compared with the other six models, the combined model achieved the best performance in the test set yielding an AUC of 0.927.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>The combined model that used time\u2010series DCE\u2010MR images, clinical features and imaging features shows promise for identifying pCR in BC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>4.<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage 4.<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.29405","type":"journal-article","created":{"date-parts":[[2024,6,8]],"date-time":"2024-06-08T11:04:17Z","timestamp":1717844657000},"page":"184-197","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Time\u2010Series <scp>MR<\/scp> Images Identifying Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Using a Deep Learning Approach"],"prefix":"10.1002","volume":"61","author":[{"given":"Jialing","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Radiology, Nanfang Hospital Southern Medical University  Guangzhou Guangdong Province China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy, Sun Yat\u2010sen Memorial 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