{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T09:32:16Z","timestamp":1778578336747,"version":"3.51.4"},"reference-count":25,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T00:00:00Z","timestamp":1676851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Radiol."],"abstract":"<jats:p>Breast cancer is a leading cause of death for women globally. A characteristic of breast cancer includes its ability to metastasize to distant regions of the body, and the disease achieves this through first spreading to the axillary lymph nodes. Traditional diagnosis of axillary lymph node metastasis includes an invasive technique that leads to potential clinical complications for breast cancer patients. The rise of artificial intelligence in the medical imaging field has led to the creation of innovative deep learning models that can predict the metastatic status of axillary lymph nodes noninvasively, which would result in no unnecessary biopsies and dissections for patients. In this review, we discuss the success of various deep learning artificial intelligence models across multiple imaging modalities in their performance of predicting axillary lymph node metastasis.<\/jats:p>","DOI":"10.3389\/fradi.2023.928639","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T06:15:07Z","timestamp":1676873707000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["Application of artificial intelligence in predicting lymph node metastasis in breast cancer"],"prefix":"10.3389","volume":"3","author":[{"given":"Gabrielle O.","family":"Windsor","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harrison","family":"Bai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana P.","family":"Lourenco","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhicheng","family":"Jiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/s12013-014-0459-6","article-title":"Breast cancer: epidemiology and etiology","volume":"72","author":"Tao","year":"2015","journal-title":"Cell Biochem Biophys"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e231","DOI":"10.1634\/theoncologist.2019-0427","article-title":"Lymph node imaging in patients with primary breast cancer: concurrent diagnostic tools","volume":"25","author":"Marino","year":"2020","journal-title":"Oncologist"},{"key":"B3","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13550-021-00751-4","article-title":"Preliminary study of AI-assisted diagnosis using FDG-PET\/CT for axillary lymph node metastasis in patients with breast cancer","volume":"11","author":"Li","year":"2021","journal-title":"EJNMMI Res"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1002\/1097-0142(19951101)76:9%3C1491::aid-cncr2820760902%3E3.0.co;2-8","article-title":"Axillary lymph nodes and breast cancer: a review","volume":"76","author":"Recht","year":"1995","journal-title":"Cancer"},{"key":"B5","doi-asserted-by":"publisher","first-page":"1283","DOI":"10.1148\/rg.2021210053","article-title":"Leung, imaging updates to breast cancer lymph node management","volume":"41","author":"Chung","year":"2021","journal-title":"Radiographics"},{"key":"B6","doi-asserted-by":"publisher","first-page":"1236","DOI":"10.1038\/s41467-020-15027-z","article-title":"Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer","volume":"11","author":"Zheng","year":"2020","journal-title":"Nat Commun"},{"key":"B7","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1148\/radiol.2019190372","article-title":"Lymph node metastasis prediction from primary breast cancer US images using deep learning","volume":"294","author":"Zhou","year":"2020","journal-title":"Radiology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/s41591-018-0029-3","article-title":"AI for medical imaging goes deep","volume":"24","author":"Ting","year":"2018","journal-title":"Nat Med"},{"key":"B9","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.ejrad.2018.06.020","article-title":"The past, present and future role of artificial intelligence in imaging","volume":"105","author":"Fazal","year":"2018","journal-title":"Eur J Radiol"},{"key":"B10","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"B11","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu Rev Biomed Eng"},{"key":"B12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal"},{"key":"B13","doi-asserted-by":"publisher","first-page":"156","DOI":"10.1186\/s13244-021-01034-1","article-title":"A meta-analysis of the diagnostic performance of machine learning-based MRI in the prediction of axillary lymph node metastasis in breast cancer patients","volume":"11","author":"Chen","year":"2021","journal-title":"Insights Imaging"},{"key":"B14","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1053\/j.semnuclmed.2022.02.003","article-title":"Application of deep learning in breast cancer imaging","volume":"52","author":"Balkenende","year":"2022","journal-title":"Semin Nucl Med"},{"key":"B15","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1016\/j.clinimag.2012.09.024","article-title":"Computer-aided detection\/diagnosis of breast cancer in mammography and ultrasound: a review","volume":"37","author":"Jalalian","year":"2013","journal-title":"Clin Imaging"},{"key":"B16","doi-asserted-by":"publisher","first-page":"e180089","DOI":"10.1148\/ryai.2019180089","article-title":"The rebirth of CAD: how is modern AI different from the CAD we know?","volume":"1","author":"Oakden-Rayner","year":"2019","journal-title":"Radiol Artif Intell"},{"key":"B17","doi-asserted-by":"publisher","first-page":"329","DOI":"10.1177\/01617346211035315","article-title":"Assessment of axillary lymph nodes for metastasis on ultrasound using artificial intelligence","volume":"43","author":"Tahmasebi","year":"2021","journal-title":"Ultrason Imaging"},{"key":"B18","doi-asserted-by":"publisher","first-page":"105250","DOI":"10.1016\/j.compbiomed.2022.105250","article-title":"Deep learning prediction of axillary lymph node status using ultrasound images","volume":"143","author":"Sun","year":"2022","journal-title":"Comput Biol Med"},{"key":"B19","doi-asserted-by":"publisher","first-page":"8616535","DOI":"10.1155\/2022\/8616535","article-title":"Deep learning mechanism for predicting the axillary lymph node metastasis in patients with primary breast cancer","volume":"2022","author":"Ashokkumar","year":"2022","journal-title":"Biomed Res Int"},{"key":"B20","doi-asserted-by":"publisher","first-page":"103018","DOI":"10.1016\/j.ebiom.2020.103018","article-title":"Deep learning radiomics of ultrasonography: identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer","volume":"60","author":"Guo","year":"2020","journal-title":"EBioMedicine"},{"key":"B21","doi-asserted-by":"publisher","first-page":"104715","DOI":"10.1016\/j.compbiomed.2021.104715","article-title":"Axillary lymph node metastasis prediction by contrast-enhanced computed tomography images for breast cancer patients based on deep learning","volume":"136","author":"Liu","year":"2021","journal-title":"Comput Biol Med"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1226","DOI":"10.1016\/j.acra.2019.11.007","article-title":"Deep learning signature based on staging CT for preoperative prediction of sentinel lymph node metastasis in breast cancer","volume":"27","author":"Yang","year":"2020","journal-title":"Acad Radiol"},{"key":"B23","doi-asserted-by":"publisher","first-page":"e301","DOI":"10.1016\/j.clbc.2019.11.009","article-title":"Convolutional neural network detection of axillary lymph node metastasis using standard clinical breast MRI","volume":"20","author":"Ren","year":"2020","journal-title":"Clin Breast Cancer"},{"key":"B24","doi-asserted-by":"publisher","first-page":"e2028086","DOI":"10.1001\/jamanetworkopen.2020.28086","article-title":"Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer","volume":"3","author":"Yu","year":"2020","journal-title":"JAMA Netw Open"},{"key":"B25","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1186\/s40644-022-00450-w","article-title":"Dynamic contrast-enhanced MRI radiomics nomogram for predicting axillary lymph node metastasis in breast cancer","volume":"22","author":"Song","year":"2022","journal-title":"Cancer Imaging"}],"container-title":["Frontiers in Radiology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fradi.2023.928639\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T06:15:09Z","timestamp":1676873709000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fradi.2023.928639\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,20]]},"references-count":25,"alternative-id":["10.3389\/fradi.2023.928639"],"URL":"https:\/\/doi.org\/10.3389\/fradi.2023.928639","relation":{},"ISSN":["2673-8740"],"issn-type":[{"value":"2673-8740","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,20]]},"article-number":"928639"}}