{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:43:05Z","timestamp":1786120985561,"version":"build-2736575974"},"reference-count":33,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017520","name":"Chengdu Medical College","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100017520","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["academicradiology.org","clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Academic Radiology"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.acra.2026.06.009","type":"journal-article","created":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T20:37:21Z","timestamp":1784320641000},"update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Machine-Learning Model Using Pre-treatment Multimodal Data to Predict Sentinel Lymph Node Status After Neoadjuvant Chemotherapy in Operable Early-stage Breast Cancer"],"prefix":"10.1016","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0847-4131","authenticated-orcid":false,"given":"Mingli","family":"Jin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aisen","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yangjie","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yimeng","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiangyu","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wengfang","family":"Ou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juxi","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicai","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7386-7217","authenticated-orcid":false,"given":"Jian","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.acra.2026.06.009_bib1","first-page":"229","article-title":"Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"74","author":"Bray","year":"2024","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"10.1016\/j.acra.2026.06.009_bib2","doi-asserted-by":"crossref","first-page":"S17","DOI":"10.1016\/j.breast.2021.12.019","article-title":"Neoadjuvant approach in patients with early breast cancer: patient assessment, staging, and planning","volume":"62","author":"Rubio","year":"2022","journal-title":"Breast"},{"key":"10.1016\/j.acra.2026.06.009_bib3","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1093\/bjs\/znac131","article-title":"Current practice and surgical outcomes of neoadjuvant chemotherapy for early breast cancer: UK NeST study","volume":"109","author":"Fatayer","year":"2022","journal-title":"Br J Surg"},{"key":"10.1016\/j.acra.2026.06.009_bib4","doi-asserted-by":"crossref","first-page":"3056","DOI":"10.1200\/JCO.21.00934","article-title":"Management of the axilla in early-stage breast cancer: Ontario Health (Cancer Care Ontario) and ASCO guideline","volume":"39","author":"Brackstone","year":"2021","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib5","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s10549-021-06414-1","article-title":"Comparison of upper extremity lymphedema after sentinel lymph node biopsy and axillary lymph node dissection: patient-reported outcomes in 3044 patients","volume":"191","author":"Poulsen","year":"2022","journal-title":"Breast Cancer Res Treat"},{"key":"10.1016\/j.acra.2026.06.009_bib6","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1245\/ASO.2006.05.013","article-title":"Surgical complications associated with sentinel lymph node biopsy: results from a prospective international cooperative group trial","volume":"13","author":"Wilke","year":"2006","journal-title":"Ann Surg Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib7","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.breast.2023.01.002","article-title":"Omission of axillary sentinel lymph node biopsy in early invasive breast cancer","volume":"67","author":"Reimer","year":"2023","journal-title":"Breast"},{"issue":"15","key":"10.1016\/j.acra.2026.06.009_bib8","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1200\/JCO.2020.38.15_suppl.564","article-title":"Can sentinel node biopsy after neoadjuvant systemic chemotherapy be safely omitted in selected patients with early breast cancer?","volume":"38","author":"Yoshida","year":"2020","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib9","doi-asserted-by":"crossref","first-page":"x19","DOI":"10.1093\/annonc\/mdx655.013","article-title":"Is sentinel lymph node biopsy necessary in breast cancer patients initially diagnosed as clinically node-negative before neoadjuvant chemotherapy?","volume":"28","author":"Fujita","year":"2017","journal-title":"Ann Oncol"},{"issue":"3","key":"10.1016\/j.acra.2026.06.009_bib10","doi-asserted-by":"crossref","first-page":"1489","DOI":"10.1245\/s10434-021-11223-3","article-title":"Neoadjuvant chemotherapy, endocrine therapy, and targeted therapy for breast cancer: ASCO guideline[J]","volume":"29","author":"Tamirisa","year":"2022","journal-title":"Ann Surg Oncol"},{"issue":"11","key":"10.1016\/j.acra.2026.06.009_bib11","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1111\/tbj.14065","article-title":"Assessing the accuracy of con-ventional gadolinium-enhanced breast MRI in measuring the nodal response to neoadjuvant chemotherapy (NAC) in breast cancer","volume":"26","author":"Murphy","year":"2020","journal-title":"Breast J"},{"key":"10.1016\/j.acra.2026.06.009_bib12","doi-asserted-by":"crossref","first-page":"193","DOI":"10.2217\/fon-2021-0470","article-title":"Does failed mapping predict sentinel lymph node metastasis in cN0 breast cancer?","volume":"18","author":"Magnoni","year":"2022","journal-title":"Future Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib13","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/s12957-022-02531-3","article-title":"Analysis on factors behind sentinel lymph node metastasis in breast cancer by color ultrasonography, molybdenum target, and pathological detection","volume":"20","author":"Yiming","year":"2022","journal-title":"World J Surg Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib14","doi-asserted-by":"crossref","first-page":"1560","DOI":"10.1200\/JCO.2022.40.16_suppl.1560","article-title":"A novel support vector machine to predict sentinel lymph node status in elderly patients with breast cancer","volume":"40","author":"Hassan","year":"2022","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib15","doi-asserted-by":"crossref","DOI":"10.3389\/fcell.2024.1431883","article-title":"Diagnostic value of multimodal ultrasound for breast cancer and prediction of sentinel lymph node metastases","volume":"12","author":"Li","year":"2024","journal-title":"Front Cell Dev Biol"},{"key":"10.1016\/j.acra.2026.06.009_bib16","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.757111","article-title":"Value of CE-MRI radiomics and machine learning in preoperative prediction of sentinel lymph node metastasis in breast cancer","volume":"11","author":"Zhu","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib17","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1007\/s12672-025-02493-4","article-title":"Construction of a risk prediction model for sentinel lymph node metastasis in breast cancer based on machine learning algorithms","volume":"16","author":"Yang","year":"2025","journal-title":"Discov Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib18","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.ultrasmedbio.2023.10.004","article-title":"Machine learning\u2013based breast tumor ultrasound radiomics for pre-operative prediction of axillary sentinel lymph node metastasis burden in early-stage invasive breast cancer","volume":"50","author":"Yao","year":"2024","journal-title":"Ultrasound Med Biol"},{"key":"10.1016\/j.acra.2026.06.009_bib19","article-title":"Preoperative prediction of HER2 expression and sentinel lymph node status in breast cancer using a mammography radiomics model","volume":"15","author":"Zhao","year":"2025","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2026.06.009_bib20","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1038\/s41746-025-01831-8","article-title":"Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer","volume":"8","author":"Zhang","year":"2025","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.acra.2026.06.009_bib21","doi-asserted-by":"crossref","first-page":"6334","DOI":"10.1002\/mp.14538","article-title":"Machine learning prediction of axillary lymph node metastasis in breast cancer: 2D versus 3D radiomic features","volume":"47","author":"Arefan","year":"2020","journal-title":"Med Phys"},{"key":"10.1016\/j.acra.2026.06.009_bib22","doi-asserted-by":"crossref","first-page":"2023","DOI":"10.2463\/mrms.mp.2023-0168","article-title":"Predictive performance of radiomic features extracted from breast MR imaging in postoperative upgrading of ductal carcinoma in situ to invasive carcinoma","volume":"24","author":"Satake","year":"2025","journal-title":"Magn Reson Med Sci"},{"key":"10.1016\/j.acra.2026.06.009_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2021.103460","article-title":"Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study","volume":"69","author":"Yu","year":"2021","journal-title":"EBioMedicine"},{"key":"10.1016\/j.acra.2026.06.009_bib24","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/jmri.26224","article-title":"Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI","volume":"49","author":"Liu","year":"2019","journal-title":"J Magn Reson Imaging"},{"key":"10.1016\/j.acra.2026.06.009_bib25","doi-asserted-by":"crossref","first-page":"1710","DOI":"10.4049\/jimmunol.133.4.1710","article-title":"Cell cycle analysis of a cell proliferation-associated human nuclear antigen defined by the monoclonal antibody Ki-67","volume":"133","author":"Gerdes","year":"1984","journal-title":"J Immunol"},{"key":"10.1016\/j.acra.2026.06.009_bib26","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1111\/j.1075-122X.2005.00043.x","article-title":"Primary tumor characteristics predict sentinel lymph node macrometastasis in breast cancer","volume":"11","author":"Tan","year":"2005","journal-title":"Breast J"},{"key":"10.1016\/j.acra.2026.06.009_bib27","first-page":"246","article-title":"Clinicopathological study of breast cancer: histologic tumor border and prognosis","volume":"31","author":"Yamashiro","year":"1985","journal-title":"Gan No Rinsho"},{"key":"10.1016\/j.acra.2026.06.009_bib28","doi-asserted-by":"crossref","DOI":"10.3389\/fendo.2023.1305473","article-title":"Feature optimization and LightGBM-based prediction of clinical pregnancy outcomes after in vitro fertilization","volume":"14","author":"Li","year":"2023","journal-title":"Front Endocrinol"},{"key":"10.1016\/j.acra.2026.06.009_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2025.108589","article-title":"A machine learning\u2013based 28-day mortality prediction model for elderly neurocritically ill patients","volume":"260","author":"Yuan","year":"2025","journal-title":"Comput Methods Programs Biomed"},{"key":"10.1016\/j.acra.2026.06.009_bib30","doi-asserted-by":"crossref","first-page":"822","DOI":"10.1007\/s00330-021-08166-4","article-title":"Characterization of breast cancer subtypes based on quantitative assessment of intratumoral heterogeneity using dynamic contrast-enhanced and diffusion-weighted MRI","volume":"32","author":"Kim","year":"2022","journal-title":"Eur Radiol"},{"issue":"1","key":"10.1016\/j.acra.2026.06.009_bib31","article-title":"Risk factors of sentinel lymph node metastasis in early-stage invasive breast cancer","volume":"15","author":"Wang","year":"2025","journal-title":"Sci Rep"},{"issue":"1","key":"10.1016\/j.acra.2026.06.009_bib32","doi-asserted-by":"crossref","first-page":"S89","DOI":"10.1016\/j.acra.2021.07.017","article-title":"Magnetic resonance imaging phenotypes of breast cancer molecular subtypes: a systematic review","volume":"29","author":"Ab Mumin","year":"2022","journal-title":"Acad Radiol"},{"key":"10.1016\/j.acra.2026.06.009_bib33","doi-asserted-by":"crossref","DOI":"10.1038\/npjbcancer.2016.12","article-title":"Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA\/TCIA dataset","volume":"2","author":"Li","year":"2016","journal-title":"NPJ Breast Cancer"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226004253?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226004253?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T02:38:49Z","timestamp":1786070329000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633226004253"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":33,"alternative-id":["S1076633226004253"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2026.06.009","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A Machine-Learning Model Using Pre-treatment Multimodal Data to Predict Sentinel Lymph Node Status After Neoadjuvant Chemotherapy in Operable Early-stage Breast Cancer","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2026.06.009","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Association of Academic Radiology. Published by Elsevier Inc.","name":"copyright","label":"Copyright"}]}}