{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T10:47:05Z","timestamp":1776077225953,"version":"3.50.1"},"reference-count":41,"publisher":"Elsevier BV","issue":"4","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100003785","name":"Guangdong Medical Research Foundation","doi-asserted-by":"publisher","award":["B2025808"],"award-info":[{"award-number":["B2025808"]}],"id":[{"id":"10.13039\/501100003785","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Basic and Applied Basic Research Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2024A1515140197"],"award-info":[{"award-number":["2024A1515140197"]}],"id":[{"id":"10.13039\/501100021171","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":[[2026,4]]},"DOI":"10.1016\/j.acra.2026.01.018","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T12:15:48Z","timestamp":1770034548000},"page":"1454-1465","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable MRI-Based Machine Learning Model for Noninvasive Prediction of Axillary Lymph Node Metastasis After Neoadjuvant Chemotherapy in Breast Cancer"],"prefix":"10.1016","volume":"33","author":[{"given":"Xiaoyu","family":"Lai","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Liang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhifeng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tingxi","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiting","family":"Han","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiling","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cuiling","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruijun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gengxi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongmei","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6908-663X","authenticated-orcid":false,"given":"Yunjun","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.acra.2026.01.018_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"},{"key":"10.1016\/j.acra.2026.01.018_bib2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ctrv.2022.102339","article-title":"Global challenges and policy solutions in breast cancer control","volume":"104","author":"Trapani","year":"2022","journal-title":"Cancer Treat Rev"},{"issue":"1","key":"10.1016\/j.acra.2026.01.018_bib3","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1038\/s41392-024-02108-4","article-title":"Breast cancer: pathogenesis and treatments","volume":"10","author":"Xiong","year":"2025","journal-title":"Signal Transduct Target Ther"},{"issue":"10","key":"10.1016\/j.acra.2026.01.018_bib4","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.1245\/s10434-018-6595-2","article-title":"Axillary nodal evaluation in elderly breast cancer patients: potential effects on treatment decisions and survival","volume":"25","author":"Tamirisa","year":"2018","journal-title":"Ann Surg Oncol"},{"issue":"3","key":"10.1016\/j.acra.2026.01.018_bib5","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1148\/radiol.2020192534","article-title":"Axillary nodal evaluation in breast cancer: state of the art","volume":"295","author":"Chang","year":"2020","journal-title":"Radiology"},{"issue":"4","key":"10.1016\/j.acra.2026.01.018_bib6","doi-asserted-by":"crossref","first-page":"572","DOI":"10.1097\/SLA.0000000000005671","article-title":"Impact of axillary lymph node dissection and sentinel lymph node biopsy on upper limb morbidity in breast cancer patients: a systematic review and meta-analysis","volume":"277","author":"Che Bakri","year":"2023","journal-title":"Ann Surg"},{"issue":"7","key":"10.1016\/j.acra.2026.01.018_bib7","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1016\/j.ejso.2021.01.024","article-title":"Effect of sentinel lymph node biopsy on upper limb function in women with early breast cancer: a\u00a0systematic review of clinical trials","volume":"47","author":"Pilger","year":"2021","journal-title":"Eur J Surg Oncol"},{"issue":"14","key":"10.1016\/j.acra.2026.01.018_bib8","doi-asserted-by":"crossref","first-page":"1720","DOI":"10.1200\/JCO-25-00099","article-title":"Sentinel lymph node biopsy in early-stage breast cancer: ASCO guideline update","volume":"43","author":"Park","year":"2025","journal-title":"J Clin Oncol"},{"issue":"8","key":"10.1016\/j.acra.2026.01.018_bib9","doi-asserted-by":"crossref","first-page":"6116","DOI":"10.1007\/s00330-020-07572-4","article-title":"Introducing the node reporting and data system 1.0 (Node-RADS): a concept for standardized assessment of lymph nodes in cancer","volume":"31","author":"Elsholtz","year":"2021","journal-title":"Eur Radiol"},{"issue":"4","key":"10.1016\/j.acra.2026.01.018_bib10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.diii.2024.10.005","article-title":"Diagnostic and prognostic value of MRI-based Node-RADS for the assessment of regional lymph node metastasis in renal cell carcinoma","volume":"106","author":"Bai","year":"2025","journal-title":"Diagn Interv Imaging"},{"issue":"6","key":"10.1016\/j.acra.2026.01.018_bib11","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1007\/s00330-024-11234-0","article-title":"Validation of the node reporting and data system (Node-RADS) for standardized CT evaluation of regional lymph nodes in esophageal squamous cell carcinoma patients","volume":"35","author":"Fang","year":"2025","journal-title":"Eur Radiol"},{"issue":"12","key":"10.1016\/j.acra.2026.01.018_bib12","doi-asserted-by":"crossref","first-page":"7734","DOI":"10.1007\/s00330-024-10828-y","article-title":"Performance of MRI for standardized lymph nodes assessment in breast cancer: are we ready for Node-RADS?","volume":"34","author":"Pediconi","year":"2024","journal-title":"Eur Radiol"},{"issue":"2","key":"10.1016\/j.acra.2026.01.018_bib13","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41571-021-00560-7","article-title":"Predicting cancer outcomes with radiomics and artificial intelligence in radiology","volume":"19","author":"Bera","year":"2022","journal-title":"Nat Rev Clin Oncol"},{"issue":"8","key":"10.1016\/j.acra.2026.01.018_bib14","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"issue":"9","key":"10.1016\/j.acra.2026.01.018_bib15","article-title":"Radiomics in breast cancer: current advances and future directions","volume":"5","author":"Qi","year":"2024","journal-title":"Cell Rep Med"},{"issue":"11","key":"10.1016\/j.acra.2026.01.018_bib16","doi-asserted-by":"crossref","first-page":"7080","DOI":"10.1007\/s00330-024-10786-5","article-title":"Longitudinal ultrasound-based AI model predicts axillary lymph node response to neoadjuvant chemotherapy in breast cancer: a multicenter study","volume":"34","author":"Fu","year":"2024","journal-title":"Eur Radiol"},{"key":"10.1016\/j.acra.2026.01.018_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.breast.2024.103737","article-title":"Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models","volume":"76","author":"Lin","year":"2024","journal-title":"Breast"},{"key":"10.1016\/j.acra.2026.01.018_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2022.110247","article-title":"Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer:\u00a0a\u00a0systematic review and meta-analysis","volume":"150","author":"Liang","year":"2022","journal-title":"Eur J Radiol"},{"issue":"8","key":"10.1016\/j.acra.2026.01.018_bib19","article-title":"Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics","volume":"4","author":"Jiang","year":"2023","journal-title":"Cell Rep Med"},{"key":"10.1016\/j.acra.2026.01.018_bib20","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2023.101899","article-title":"Longitudinal MRI-based fusion novel model predicts pathological complete response in breast cancer treated with neoadjuvant chemotherapy: a multicenter, retrospective study","volume":"58","author":"Huang","year":"2023","journal-title":"EClinicalMedicine"},{"issue":"6","key":"10.1016\/j.acra.2026.01.018_bib21","doi-asserted-by":"crossref","first-page":"3384","DOI":"10.1016\/j.acra.2024.12.049","article-title":"Integration of deep learning and sub-regional radiomics improves the prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer patients","volume":"32","author":"Wu","year":"2025","journal-title":"Acad Radiol"},{"issue":"5","key":"10.1016\/j.acra.2026.01.018_bib22","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.diii.2024.01.004","article-title":"Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis","volume":"105","author":"Huang","year":"2024","journal-title":"Diagn Interv Imaging"},{"key":"10.1016\/j.acra.2026.01.018_bib23","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"10.1016\/j.acra.2026.01.018_bib24","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2024.102805","article-title":"A transformer-based deep learning model for early prediction of lymph node metastasis in locally advanced gastric cancer after neoadjuvant chemotherapy using pretreatment CT images","volume":"75","author":"Zheng","year":"2024","journal-title":"EClinicalMedicine"},{"issue":"2","key":"10.1016\/j.acra.2026.01.018_bib25","first-page":"336","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int\u00a0J\u00a0Comput\u00a0Vis"},{"issue":"2","key":"10.1016\/j.acra.2026.01.018_bib26","doi-asserted-by":"crossref","first-page":"S38","DOI":"10.1016\/j.acra.2023.04.009","article-title":"Radiomics based on DCE-MRI for predicting response to neoadjuvant therapy in breast cancer","volume":"30","author":"Zeng","year":"2023","journal-title":"Acad Radiol"},{"issue":"1","key":"10.1016\/j.acra.2026.01.018_bib27","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","article-title":"Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI","volume":"19","author":"Braman","year":"2017","journal-title":"Breast Cancer Res"},{"issue":"1","key":"10.1016\/j.acra.2026.01.018_bib28","doi-asserted-by":"crossref","first-page":"S145","DOI":"10.1016\/j.acra.2020.10.020","article-title":"MRI radiomics for assessment of molecular subtype, pathological complete response, and residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy","volume":"29 1","author":"Choudhery","year":"2022","journal-title":"Acad Radiol"},{"issue":"8","key":"10.1016\/j.acra.2026.01.018_bib29","doi-asserted-by":"crossref","first-page":"1638","DOI":"10.1016\/j.acra.2022.11.006","article-title":"Radiomics based on dynamic contrast-enhanced MRI to early predict pathologic complete response in breast cancer patients treated with neoadjuvant therapy","volume":"30","author":"Zeng","year":"2023","journal-title":"Acad Radiol"},{"issue":"1","key":"10.1016\/j.acra.2026.01.018_bib30","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1186\/s13058-020-01292-9","article-title":"Ultrafast dynamic contrast-enhanced breast MRI may generate prognostic imaging markers of breast cancer","volume":"22","author":"Onishi","year":"2020","journal-title":"Breast Cancer Res"},{"issue":"3","key":"10.1016\/j.acra.2026.01.018_bib31","doi-asserted-by":"crossref","first-page":"663","DOI":"10.1148\/radiol.2016160176","article-title":"Neoadjuvant systemic therapy in breast cancer: association of contrast-enhanced MR imaging findings, diffusion-weighted imaging findings, and tumor subtype with tumor response","volume":"283","author":"Santamar\u00eda","year":"2017","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2026.01.018_bib32","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jclinepi.2019.02.004","article-title":"A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models","volume":"110","author":"Christodoulou","year":"2019","journal-title":"J Clin Epidemiol"},{"key":"10.1016\/j.acra.2026.01.018_bib33","doi-asserted-by":"crossref","first-page":"112079","DOI":"10.1016\/j.ejrad.2025.112079","article-title":"The Node Reporting and Data System (Node-RADS) for standardized MRI evaluation of lymph nodes in endometrial cancer, integrated with clinicopathological and molecular data","volume":"187","author":"Riccardi","year":"2025","journal-title":"Eur J Radiol"},{"issue":"3","key":"10.1016\/j.acra.2026.01.018_bib34","doi-asserted-by":"crossref","DOI":"10.1148\/radiol.243823","article-title":"Node reporting and data system evaluation of axillary nodes in invasive ductal and lobular carcinoma","volume":"316","author":"Kim","year":"2025","journal-title":"Radiology"},{"key":"10.1016\/j.acra.2026.01.018_bib35","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.786346","article-title":"A clinical-radiomics model for predicting axillary pathologic complete response in breast cancer with axillary lymph node metastases","volume":"11","author":"Gan","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.acra.2026.01.018_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejro.2024.100561","article-title":"Machine learning approaches in the prediction of positive axillary lymph nodes post neoadjuvant chemotherapy using MRI, CT, or ultrasound: a\u00a0systematic review","volume":"12","author":"Yaghoobpoor","year":"2024","journal-title":"Eur J Radiol Open"},{"key":"10.1016\/j.acra.2026.01.018_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.breast.2024.103786","article-title":"Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: a\u00a0radiomics and deep learning analysis on longitudinal DCE-MRI data","volume":"77","author":"Yu","year":"2024","journal-title":"Breast"},{"issue":"1","key":"10.1016\/j.acra.2026.01.018_bib38","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.acra.2024.07.052","article-title":"Delta radiomics based on MRI for predicting axillary lymph node pathologic complete response after neoadjuvant chemotherapy in breast cancer patients","volume":"32","author":"Mao","year":"2025","journal-title":"Acad Radiol"},{"issue":"3","key":"10.1016\/j.acra.2026.01.018_bib39","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1002\/cncr.34540","article-title":"Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: a\u00a0multicenter study","volume":"129","author":"Gu","year":"2023","journal-title":"Cancer"},{"issue":"7","key":"10.1016\/j.acra.2026.01.018_bib40","doi-asserted-by":"crossref","first-page":"3801","DOI":"10.1016\/j.acra.2025.03.010","article-title":"Multiparametric MRI-based interpretable machine learning radiomics model for distinguishing between luminal and non-luminal tumors in breast cancer: a multicenter study","volume":"32","author":"Zhou","year":"2025","journal-title":"Acad Radiol"},{"issue":"1","key":"10.1016\/j.acra.2026.01.018_bib41","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1186\/s13244-024-01840-3","article-title":"A radiomics-based interpretable machine learning model to predict the HER2 status in bladder cancer: a multicenter study","volume":"15","author":"Wei","year":"2024","journal-title":"Insights Imaging"}],"container-title":["Academic Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226000176?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1076633226000176?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T09:54:32Z","timestamp":1776074072000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1076633226000176"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":41,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["S1076633226000176"],"URL":"https:\/\/doi.org\/10.1016\/j.acra.2026.01.018","relation":{},"ISSN":["1076-6332"],"issn-type":[{"value":"1076-6332","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Interpretable MRI-Based Machine Learning Model for Noninvasive Prediction of Axillary Lymph Node Metastasis After Neoadjuvant Chemotherapy in Breast Cancer","name":"articletitle","label":"Article Title"},{"value":"Academic Radiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acra.2026.01.018","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. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}]}}