{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T05:53:12Z","timestamp":1776405192387,"version":"3.51.2"},"reference-count":38,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"DOI":"10.1186\/s12880-025-01988-4","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T16:30:45Z","timestamp":1763656245000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Predicting axillary lymph node metastasis in breast cancer patients using CNN-GCN on DCE-MRI: a multicenter study"],"prefix":"10.1186","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1919-1885","authenticated-orcid":false,"given":"Yi","family":"Dai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yun","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingling","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ziyin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun","family":"Lian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanxun","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8668-2799","authenticated-orcid":false,"given":"Haicheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"issue":"1","key":"1988_CR1","first-page":"17","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u201348 10.3322\/caac.21763","journal-title":"CA Cancer J Clin"},{"key":"1988_CR2","doi-asserted-by":"crossref","unstructured":"Bray F, Laversanne MSung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;10.3322\/caac.21834","DOI":"10.3322\/caac.21834"},{"issue":"3","key":"1988_CR3","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1148\/radiol.2020192534","volume":"295","author":"JM Chang","year":"2020","unstructured":"Chang JM, Leung JWT, Moy L, Ha SM, Moon WK. Axillary nodal evaluation in breast cancer: state of the art. Radiology. 2020;295(3):500\u201315. https:\/\/doi.org\/10.1148\/radiol.2020192534","journal-title":"Radiology"},{"issue":"9","key":"1988_CR4","doi-asserted-by":"publisher","first-page":"2533\u201341 10.1245","DOI":"10.1245\/s10434-008-9996-9","volume":"15","author":"J Kootstra","year":"2008","unstructured":"Kootstra J, Hoekstra-Weebers JERietman H, et al. Quality of life after sentinel lymph node biopsy or axillary lymph node dissection in stage I\/II breast cancer patients: a prospective longitudinal study. Ann Surg Oncol. 2008;15(9):2533\u201341 10.1245\/s10434\u2013008\u20139996\u20139","journal-title":"Ann Surg Oncol"},{"issue":"3","key":"1988_CR5","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1002\/pro6.1241","volume":"8","author":"X Xu","year":"2024","unstructured":"Xu X, Yin Y, Zhang L, Wang D, Zhou Y, Li Q. Research progress of cardiotoxicity caused by radiotherapy in breast cancer. Precis Radiat Oncol. 2024;8(3):153\u201358. https:\/\/doi.org\/10.1002\/pro6.1241","journal-title":"Precis Radiat Oncol"},{"issue":"5","key":"1988_CR6","doi-asserted-by":"publisher","first-page":"1136\u201342 10.1245","DOI":"10.1245\/s10434-009-0399-3","volume":"16","author":"M Klar","year":"2009","unstructured":"Klar M, Foeldi M, Markert S, Gitsch G, Stickeler E, Watermann D. Good prediction of the likelihood for sentinel lymph node metastasis by using the MSKCC nomogram in a German breast cancer population. Ann Surg Oncol. 2009;16(5):1136\u201342 10.1245\/s10434\u2013009\u20130399\u20133","journal-title":"Ann Surg Oncol"},{"issue":"3","key":"1988_CR7","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.ejca.2012.04.025","volume":"49","author":"van la Parra Rf","year":"2013","unstructured":"van la Parra Rf, Francissen CMPeer PG, et al. Assessment of the memorial Sloan-Kettering cancer Center nomogram to predict sentinel lymph node metastases in a Dutch breast cancer population. Eur J Cancer. 2013;49(3):564\u201371. https:\/\/doi.org\/10.1016\/j.ejca.2012.04.025","journal-title":"Eur J Cancer"},{"issue":"3","key":"1988_CR8","doi-asserted-by":"publisher","first-page":"839\u201348 10.1007\/","DOI":"10.1007\/s10549-012-2219-x","volume":"135","author":"JY Chen","year":"2012","unstructured":"Chen JY, Chen JJ, Yang BL, et al. Predicting sentinel lymph node metastasis in a Chinese breast cancer population: assessment of an existing nomogram and a new predictive nomogram. Breast Cancer Res Treat. 2012;135(3):839\u201348 10.1007\/s10549\u2013012\u20132219\u2013x","journal-title":"Breast Cancer Res Treat"},{"issue":"24","key":"1988_CR9","doi-asserted-by":"publisher","first-page":"3670","DOI":"10.1200\/jco.2006.08.8013","volume":"25","author":"JL Bevilacqua","year":"2007","unstructured":"Bevilacqua JL, Kattan MW, Fey JV, Cody HS 3rd, Borgen PI, Van Zee KJ. Doctor, what are my chances of having a positive sentinel node? A validated nomogram for risk estimation. J Clin Oncol. 2007;25(24):3670\u201379. https:\/\/doi.org\/10.1200\/jco.2006.08.8013","journal-title":"J Clin Oncol"},{"issue":"9","key":"1988_CR10","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.1007\/s003300000370","volume":"10","author":"KA Kvistad","year":"2000","unstructured":"Kvistad KA, Rydland J, Smethurst HB, Lundgren S, Fj\u00f8sne HE, Haraldseth O. Axillary lymph node metastases in breast cancer: preoperative detection with dynamic contrast-enhanced MRI. Eur Radiol. 2000;10(9):1464\u201371. https:\/\/doi.org\/10.1007\/s003300000370","journal-title":"Eur Radiol"},{"issue":"7","key":"1988_CR11","doi-asserted-by":"publisher","first-page":"4872\u201385 10.1007","DOI":"10.1007\/s00330-020-07640-9","volume":"31","author":"WC Ou","year":"2021","unstructured":"Ou WC, Polat D, Dogan BE. Deep learning in breast radiology: current progress and future directions. Eur Radiol. 2021;31(7):4872\u201385 10.1007\/s00330\u2013020\u201307640\u20139","journal-title":"Eur Radiol"},{"issue":"7553","key":"1988_CR12","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436\u201344. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"issue":"8","key":"1988_CR13","doi-asserted-by":"publisher","first-page":"500\u201310 10.1038\/","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts H. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500\u201310 10.1038\/s41568\u2013018\u20130016\u20135","journal-title":"Nat Rev Cancer"},{"issue":"1","key":"1988_CR14","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1002\/pro6.70003","volume":"9","author":"Y Wang","year":"2025","unstructured":"Wang Y, Jian W, Yuan Z, Guan F, Carlson D. Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT. Precis Radiat Oncol. 2025;9(1):13\u201322. https:\/\/doi.org\/10.1002\/pro6.70003","journal-title":"Precis Radiat Oncol"},{"key":"1988_CR15","doi-asserted-by":"publisher","first-page":"102027","DOI":"10.1016\/j.compmedimag.2021.102027","volume":"95","author":"D Ahmedt-Aristizabal","year":"2022","unstructured":"Ahmedt-Aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. A survey on graph-based deep learning for computational histopathology. Comput Med Imag Graph. 2022;95:102027. https:\/\/doi.org\/10.1016\/j.compmedimag.2021.102027","journal-title":"Comput Med Imag Graph"},{"issue":"9","key":"1988_CR16","doi-asserted-by":"publisher","first-page":"4164","DOI":"10.1002\/mp.14327","volume":"47","author":"Z Tian","year":"2020","unstructured":"Tian Z, Li X, Zheng Y, et al. Graph-convolutional-network-based interactive prostate segmentation in MR images. Med Phys. 2020;47(9):4164\u201376. https:\/\/doi.org\/10.1002\/mp.14327","journal-title":"Med Phys"},{"key":"1988_CR17","doi-asserted-by":"publisher","DOI":"10.1093\/schbul\/sbac047","author":"D Lei","year":"2022","unstructured":"Lei D, Qin K, Pinaya WHL, et al. Graph convolutional networks reveal network-level functional dysconnectivity in schizophrenia. Schizophr Bull. 2022. https:\/\/doi.org\/10.1093\/schbul\/sbac047","journal-title":"Schizophr Bull"},{"key":"1988_CR18","doi-asserted-by":"publisher","first-page":"7315665","DOI":"10.1155\/2022\/7315665","volume":"2022","author":"P Guo","year":"2022","unstructured":"Guo P, Li L, Li C, et al. Multiparametric magnetic resonance imaging information fusion using graph convolutional network for glioma grading. J Healthc Eng. 2022;2022:7315665. https:\/\/doi.org\/10.1155\/2022\/7315665","journal-title":"J Healthc Eng"},{"issue":"7","key":"1988_CR19","doi-asserted-by":"publisher","first-page":"3163","DOI":"10.1109\/jbhi.2022.3153671","volume":"26","author":"Z Gao","year":"2022","unstructured":"Gao Z, Lu Z, Wang J, Ying S, Shi J. A convolutional neural network and graph convolutional network based framework for classification of breast histopathological images. IEEE J Biomed Health Inf. 2022;26(7):3163\u201373. https:\/\/doi.org\/10.1109\/jbhi.2022.3153671","journal-title":"IEEE J Biomed Health Inf"},{"issue":"3","key":"1988_CR20","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1093\/brain\/awab340","volume":"145","author":"G Li","year":"2022","unstructured":"Li G, Li L, Li Y, et al. An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas. Brain. 2022;145(3):1151\u201361. https:\/\/doi.org\/10.1093\/brain\/awab340","journal-title":"Brain"},{"issue":"1","key":"1988_CR21","doi-asserted-by":"publisher","first-page":"471 10.1186\/s12","DOI":"10.1186\/s12967-022-03688-x","volume":"20","author":"GH Su","year":"2022","unstructured":"Su GH, Xiao YJiang L, et al. Radiomics features for assessing tumor-infiltrating lymphocytes correlate with molecular traits of triple-negative breast cancer. J Transl Med. 2022;20(1):471 10.1186\/s12967\u2013022\u201303688\u2013x","journal-title":"J Transl Med"},{"issue":"11","key":"1988_CR22","doi-asserted-by":"publisher","first-page":"e012799 10.1136","DOI":"10.1136\/bmjopen-2016-012799","volume":"6","author":"JF Cohen","year":"2016","unstructured":"Cohen JF, Korevaar DAAltman DG, et al. STARD, 2015 guidelines for reporting diagnostic accuracy studies: explanation and elaboration. BMJ Open. 2016;6(11):e012799 10.1136\/bmjopen-2016\u2013012799","journal-title":"BMJ Open"},{"issue":"6","key":"1988_CR23","doi-asserted-by":"publisher","first-page":"1842","DOI":"10.1002\/jmri.28464","volume":"57","author":"J Gao","year":"2023","unstructured":"Gao J, Zhong X, Li W, et al. Attention-based deep learning for the preoperative differentiation of axillary lymph node metastasis in breast cancer on DCE-MRI. J Magnetic Reson Imag. 2023;57(6):1842\u201353","journal-title":"J Magnetic Reson Imag"},{"issue":"4","key":"1988_CR24","doi-asserted-by":"publisher","first-page":"508\u201316 10.1038\/","DOI":"10.1038\/s41416-018-0185-8","volume":"119","author":"A Saha","year":"2018","unstructured":"Saha A, Harowicz MRGrimm LJ, et al. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. Br J Cancer. 2018;119(4):508\u201316 10.1038\/s41416\u2013018\u20130185\u20138","journal-title":"Br J Cancer"},{"issue":"3","key":"1988_CR25","doi-asserted-by":"publisher","first-page":"297","DOI":"10.2307\/1932409","volume":"26","author":"LR Dice","year":"1945","unstructured":"Dice LR. Measures of the amount of ecologic association between species. Ecology. 1945;26(3):297\u2013302","journal-title":"Ecology"},{"key":"1988_CR26","doi-asserted-by":"crossref","unstructured":"Hara K, Kataoka H, Satoh Y, editors. Learning spatio-temporal features with 3d residual networks for action recognition. Proceedings of the IEEE international conference on computer vision workshops. 2017","DOI":"10.1109\/ICCVW.2017.373"},{"key":"1988_CR27","doi-asserted-by":"crossref","unstructured":"Chollet F, Xception, editors. Deep learning with depthwise separable convolutions. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017","DOI":"10.1109\/CVPR.2017.195"},{"key":"1988_CR28","doi-asserted-by":"crossref","unstructured":"Sun K, Xiao B, Liu D, Wang J. Deep high-resolution representation learning for human pose Estimation2019 February 01. 2019: [arXiv: 1902.09212 p.]. Available from: https:\/\/ui.adsabs.harvard.edu\/abs\/2019arXiv190209212S","DOI":"10.1109\/CVPR.2019.00584"},{"issue":"7","key":"1988_CR29","doi-asserted-by":"publisher","first-page":"2038","DOI":"10.1016\/j.patcog.2006.12.019","volume":"40","author":"M-L Zhang","year":"2007","unstructured":"Zhang M-L, Zhou Z-H. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 2007;40(7):2038\u201348. https:\/\/doi.org\/10.1016\/j.patcog.2006.12.019","journal-title":"Pattern Recognit"},{"key":"1988_CR30","unstructured":"Kipf TN, Welling M. Semi-supervised classification with graph convolutional Networks 2016. [arXiv:1609.02907 p.]. Available from: https:\/\/ui.adsabs.harvard.edu\/abs\/2016arXiv160902907K. September 1, 2016"},{"key":"1988_CR31","doi-asserted-by":"crossref","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44(3):837\u201345","DOI":"10.2307\/2531595"},{"issue":"1","key":"1988_CR32","first-page":"1236, 10.1038\/s","volume":"11","author":"X Zheng","year":"2020","unstructured":"Zheng X, Yao Z, Huang Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer. Nat Commun. 2020;11(1):1236, 10.1038\/s41467\u2013020\u201315027\u2013z","journal-title":"Nat Commun"},{"key":"1988_CR33","doi-asserted-by":"publisher","first-page":"940655","DOI":"10.3389\/fonc.2022.940655","volume":"12","author":"D Wang","year":"2022","unstructured":"Wang D, Hu Y, Zhan C, Zhang Q, Wu Y, Ai T. A nomogram based on radiomics signature and deep-learning signature for preoperative prediction of axillary lymph node metastasis in breast cancer. Front Oncol. 2022;12:940655. https:\/\/doi.org\/10.3389\/fonc.2022.940655","journal-title":"Front Oncol"},{"key":"1988_CR34","doi-asserted-by":"crossref","unstructured":"Chen Y, Wang L, Dong X, et al. Deep learning radiomics of preoperative breast MRI for prediction of axillary lymph node metastasis in breast cancer. J Digit Imag. 2023;1\u20139 10.1007\/s10278\u2013023\u201300818\u20139","DOI":"10.1007\/s10278-023-00818-9"},{"issue":"6","key":"1988_CR35","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1097\/rli.0000000000000544","volume":"54","author":"MU Dalmi\u015f","year":"2019","unstructured":"Dalmi\u015f MU, Gubern-M\u00e9rida AVreemann S, et al. Artificial intelligence-based classification of breast lesions imaged with a Multiparametric breast MRI protocol with ultrafast DCE-MRI, T2, and DWI. Invest Radiol. 2019;54(6):325\u201332. https:\/\/doi.org\/10.1097\/rli.0000000000000544","journal-title":"Invest Radiol"},{"key":"1988_CR36","doi-asserted-by":"publisher","unstructured":"Zhang Q, Peng Y, Liu W, et al. Radiomics based on multimodal MRI for the differential diagnosis of benign and malignant breast lesions. J Magn Reson Imag. 2020;52(2):596\u2013607. https:\/\/doi.org\/10.1002\/jmri.27098","DOI":"10.1002\/jmri.27098"},{"key":"1988_CR37","doi-asserted-by":"crossref","unstructured":"She Y, He B, Wang F, et al. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: a multicentre study. EBioMedicine. 2022;86","DOI":"10.1016\/j.ebiom.2022.104364"},{"key":"1988_CR38","doi-asserted-by":"publisher","unstructured":"Kiaei SZF, Nouralishahi AGhasemirad M, et al. Advances in natural killer cell therapies for breast cancer. Immunol Cell Biol. 2023;101(8):705\u201326. https:\/\/doi.org\/10.1111\/imcb.12658","DOI":"10.1111\/imcb.12658"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01988-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-025-01988-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-025-01988-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T16:30:47Z","timestamp":1763656247000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-025-01988-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":38,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1988"],"URL":"https:\/\/doi.org\/10.1186\/s12880-025-01988-4","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]},"assertion":[{"value":"14 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Ethical approvals for this study (No. 2022\u2013236 and No. 2022\u2013247) were provided by the Ethics Committee of Yantai Yuhuangding Hospital, Yantai, China. This study was conducted in accordance with the Declaration of Helsinki. For the retrospective part, informed consent was waived; for the prospective part, written informed consent was obtained from each patient.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"481"}}