{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T23:49:28Z","timestamp":1787960968333,"version":"build-2784847793"},"reference-count":39,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T00:00:00Z","timestamp":1643760000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2022,9]]},"abstract":"\n Background<\/jats:title>\n Multiparametric magnetic resonance imaging (MRI) is widely used in breast cancer screening. Accurate prediction of the axillary lymph nodes metastasis (ALNM) is essential for breast cancer surgery and treatment. However, there is no mature and effective discerning method for ALNM based on multiparametric MRI.<\/jats:p>\n <\/jats:sec>\n \n Purpose<\/jats:title>\n To evaluate the ALNM using T1\u2010weighted imaging (T1WI), T2\u2010weighted imaging (T2WI), and diffusion\u2010weighted imaging (DWI) sequences, respectively, and construct a quantitative ALNM discerning model of integrated multiparametric MRI.<\/jats:p>\n <\/jats:sec>\n \n Study Type<\/jats:title>\n Retrospective.<\/jats:p>\n <\/jats:sec>\n \n Population<\/jats:title>\n Three\u2010hundred forty\u2010eight breast cancer patients, 163 with ALNM (99.39% females), and 185 without ALNM (100% females). The dataset was randomly divided into the training set (315 cases) and the testing set (33 cases).<\/jats:p>\n <\/jats:sec>\n \n Field Strength\/Sequence<\/jats:title>\n 1.5\u2009T; T1WI (VIBRANT), T2WI (FSE), and DWI (echo planar imaging [EPI]).<\/jats:p>\n <\/jats:sec>\n \n Assessment<\/jats:title>\n The lesion region of interest images were cropped and sent to a pretrained ResNet50 network. Then, the results of different sequences were sent to a classifier for ensemble learning to construct the ALNM model of multiparametric MRI.<\/jats:p>\n <\/jats:sec>\n \n Statistical Tests<\/jats:title>\n \n Performance indicators such as accuracy, the receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) were calculated. Student's\n t<\/jats:italic>\n \u2010test, chi\u2010square test, Fisher's exact test, and Delong test were performed, and\n P<\/jats:italic>\n \u2009<\u20090.05 was considered statistically significant.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n T2WI performed the best among the three sequences, and achieved the accuracy and AUC of 0.933\/0.989 in the testing set. Compared to T1WI with the accuracy and AUC of 0.691\/0.806, the increase is significant. While compared to DWI with the accuracy and AUC of 0.800\/0.910, the improvement is not significant (\n P<\/jats:italic>\n \u00a0=\u00a00.126). After integrating three sequences, the accuracy and AUC improved to 0.970 and 0.996.\n <\/jats:p>\n <\/jats:sec>\n \n Data Conclusion<\/jats:title>\n T2WI performed better than DWI and T1WI in discerning ALNM in this breast cancer dataset. The proposed quantitative model of integrated multiparametric MRI could effectively help the ALNM diagnosis.<\/jats:p>\n <\/jats:sec>\n \n Level of Evidence<\/jats:title>\n 1<\/jats:p>\n <\/jats:sec>\n \n Technical Efficacy Stage<\/jats:title>\n 2<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/jmri.28082","type":"journal-article","created":{"date-parts":[[2022,2,2]],"date-time":"2022-02-02T13:20:59Z","timestamp":1643808059000},"page":"700-709","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using\n CNN<\/scp>\n Based on Multiparametric\n MRI<\/scp>"],"prefix":"10.1002","volume":"56","author":[{"given":"Zijian","family":"Wang","sequence":"first","affiliation":[{"name":"College of Medicine and Biological Information Engineering Northeastern University Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Medicine and Biological Information Engineering Northeastern University Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiology Affiliated Hospital of Guizhou Medical University Guiyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiology Affiliated Hospital of Guizhou Medical University Guiyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fancong","family":"Meng","sequence":"additional","affiliation":[{"name":"College of Medicine and Biological Information Engineering Northeastern University Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2460-8841","authenticated-orcid":false,"given":"Hong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Medicine and Biological Information Engineering Northeastern University Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Radiology Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology Affiliated Hospital of Guizhou Medical University Guiyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Radiology Cancer Hospital of China Medical University, Liaoning Cancer Hospital & Institute Shenyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"e_1_2_6_2_1","doi-asserted-by":"publisher","DOI":"10.13104\/imri.2019.23.2.125"},{"key":"e_1_2_6_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2015.07.007"},{"key":"e_1_2_6_4_1","doi-asserted-by":"publisher","DOI":"10.1177\/000313481608200606"},{"key":"e_1_2_6_5_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.14141167"},{"issue":"5","key":"e_1_2_6_6_1","first-page":"1265","article-title":"Diagnostic value of diffusion weighted magnetic resonance imaging in evaluation of metastatic axillary lymph nodes in a sample of iranian women with breast cancer","volume":"18","author":"Hasanzadeh F","year":"2017","journal-title":"Asian Pac J Cancer Prev"},{"key":"e_1_2_6_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109093"},{"key":"e_1_2_6_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12282-015-0598-7"},{"key":"e_1_2_6_9_1","doi-asserted-by":"publisher","DOI":"10.1177\/0284185120903449"},{"key":"e_1_2_6_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26865"},{"key":"e_1_2_6_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clbc.2016.10.010"},{"key":"e_1_2_6_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.11110639"},{"key":"e_1_2_6_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2013.10.001"},{"key":"e_1_2_6_14_1","doi-asserted-by":"crossref","unstructured":"AshrafAB LinL GavenonisSC et al. Predicting axillary lymph node metastasis from kinetic statistics of DCE\u2010MRI breast images. In Medical Imaging 2012: Computer\u2010Aided Diagnosis. International Society for Optics and Photonics San Diego; 2012; 8315 p. 831525.","DOI":"10.1117\/12.911576"},{"key":"e_1_2_6_15_1","first-page":"1526","article-title":"Convolutional neural network of multiparametric MRI accurately detects axillary lymph node metastasis in breast cancer patients with pre Neoadjuvant chemotherapy","author":"Ren T","year":"2021","journal-title":"Clin Breast Cancer"},{"key":"e_1_2_6_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acra.2019.11.004"},{"key":"e_1_2_6_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.541849"},{"key":"e_1_2_6_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40644-020-00342-x"},{"key":"e_1_2_6_19_1","doi-asserted-by":"crossref","unstructured":"CordelliE SiciliaR SantucciD et al. Radiomics\u2010based non\u2010invasive lymph node metastases prediction in breast cancer. In 2020 IEEE 33rd International Symposium on Computer\u2010Based Medical Systems (CBMS) Rochester Institute of Electrical and Electronics Engineers; 2020 pp. 486\u2010491.","DOI":"10.1109\/CBMS49503.2020.00098"},{"key":"e_1_2_6_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-018-5981-2"},{"key":"e_1_2_6_21_1","first-page":"1076","article-title":"Prediction of axillary lymph node metastasis in breast cancer using intra\u2010peritumoral textural transition analysis based on dynamic contrast\u2010enhanced magnetic resonance imaging","author":"Zhan C","year":"2021","journal-title":"Acad Radiol"},{"key":"e_1_2_6_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2021.103460"},{"key":"e_1_2_6_23_1","doi-asserted-by":"crossref","unstructured":"YuY HeZ OuyangJ et al. Machine learning magnetic resonance imaging radiomics predicts axillary lymph node metastasis in invasive breast cancer. 2020.","DOI":"10.21203\/rs.3.rs-104661\/v1"},{"key":"e_1_2_6_24_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.28086"},{"key":"e_1_2_6_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-020-07674-z"},{"key":"e_1_2_6_26_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2020.01463"},{"key":"e_1_2_6_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26701"},{"key":"e_1_2_6_28_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0247074"},{"key":"e_1_2_6_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-017-5005-7"},{"key":"e_1_2_6_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.26224"},{"key":"e_1_2_6_31_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13040757"},{"key":"e_1_2_6_32_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers13092228"},{"key":"e_1_2_6_33_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-54638-5"},{"key":"e_1_2_6_34_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.00980"},{"key":"e_1_2_6_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14538"},{"key":"e_1_2_6_36_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aaf241"},{"key":"e_1_2_6_37_1","doi-asserted-by":"crossref","unstructured":"NguyenS PolatD KarbasiP et al. Preoperative prediction of lymph node metastasis from clinical DCE MRI of the primary breast tumor using a 4D CNN. In International conference on medical image computing and computer\u2010assisted intervention. Switzerland; 2020; 12262 pp. 326\u2010334.","DOI":"10.1007\/978-3-030-59713-9_32"},{"key":"e_1_2_6_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-018-0086-7"},{"key":"e_1_2_6_39_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clbc.2019.11.009"},{"issue":"1","key":"e_1_2_6_40_1","first-page":"136","article-title":"Support vector machine ensemble learning algorithm research based on heterogeneous data","volume":"37","author":"Chang T\u2010t","year":"2010","journal-title":"J Xidian University"}],"container-title":["Journal of Magnetic Resonance Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmri.28082","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/jmri.28082","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/jmri.28082","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T21:27:23Z","timestamp":1692739643000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/jmri.28082"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,2]]},"references-count":39,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["10.1002\/jmri.28082"],"URL":"https:\/\/doi.org\/10.1002\/jmri.28082","archive":["Portico"],"relation":{},"ISSN":["1053-1807","1522-2586"],"issn-type":[{"value":"1053-1807","type":"print"},{"value":"1522-2586","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,2]]},"assertion":[{"value":"2021-11-19","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-01-13","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-02-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}