{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T10:34:39Z","timestamp":1791282879765,"version":"4.1.0"},"reference-count":32,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012659","name":"Foundation for Innovative Research Groups of the National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072004"],"award-info":[{"award-number":["82072004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","clinical-breast-cancer.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Clinical Breast Cancer"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1016\/j.clbc.2024.01.005","type":"journal-article","created":{"date-parts":[[2024,1,21]],"date-time":"2024-01-21T08:07:28Z","timestamp":1705824448000},"page":"215-226","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":22,"title":["Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study"],"prefix":"10.1016","volume":"24","author":[{"given":"Junqi","family":"Han","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Hua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Fei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingjing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yijun","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjuan","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7032-505X","authenticated-orcid":false,"given":"Jingjing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.clbc.2024.01.005_bib0001","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21708","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J Clin"},{"key":"10.1016\/j.clbc.2024.01.005_bib0002","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20210348","article-title":"Mammography-based radiomics for predicting the risk of breast cancer recurrence: a multicenter study","volume":"94","author":"Mao","year":"2021","journal-title":"Br J Radiol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0003","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.ejca.2010.11.035","article-title":"Preoperative MRI and surgical management in patients with nonpalpable breast cancer: the MONET\u2014randomised controlled trial","volume":"47","author":"Peters","year":"2011","journal-title":"Eur J Cancer"},{"key":"10.1016\/j.clbc.2024.01.005_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.ctrv.2022.102375","article-title":"Adjuvant and neoadjuvant breast cancer treatments: a systematic review of their effects on mortality","volume":"105","author":"Kerr","year":"2022","journal-title":"Cancer Treat Rev"},{"key":"10.1016\/j.clbc.2024.01.005_bib0005","doi-asserted-by":"crossref","first-page":"621","DOI":"10.1007\/s40291-020-00482-7","article-title":"Oncotype DX Breast Recurrence Score\u00ae: a review of its use in early-stage breast cancer","volume":"24","author":"Syed","year":"2020","journal-title":"Mol Diagn Ther"},{"key":"10.1016\/j.clbc.2024.01.005_bib0006","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1056\/NEJMoa1804710","article-title":"Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer","volume":"379","author":"Sparano","year":"2018","journal-title":"N Engl J Med"},{"key":"10.1016\/j.clbc.2024.01.005_bib0007","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1200\/JCO.2015.62.3504","article-title":"Annual hazard rates of recurrence for breast cancer during 24 years of follow-up: results from the international breast cancer study group trials I to V","volume":"34","author":"Colleoni","year":"2016","journal-title":"J Clin Oncol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0008","doi-asserted-by":"crossref","first-page":"4579","DOI":"10.2147\/CMAR.S276422","article-title":"Associations of estrogen receptor, progesterone receptor, human epidemic growth factor receptor-2 and Ki-67 with ultrasound signs and prognosis of breast cancer patients","volume":"13","author":"Zhao","year":"2021","journal-title":"Cancer Manag Res"},{"key":"10.1016\/j.clbc.2024.01.005_bib0009","doi-asserted-by":"crossref","first-page":"5677","DOI":"10.2147\/CMAR.S198844","article-title":"Conversion of immunohistochemical markers and breast density are associated with pathological response and prognosis in very young breast cancer patients who fail to achieve a pathological complete response after neoadjuvant chemotherapy","volume":"11","author":"Zhao","year":"2019","journal-title":"Cancer Manag Res"},{"key":"10.1016\/j.clbc.2024.01.005_bib0010","doi-asserted-by":"crossref","first-page":"159","DOI":"10.3348\/kjr.2019.0588","article-title":"Survival rates of breast cancer patients aged 40 to 49 years according to detection modality in Korea: screening ultrasound versus mammography","volume":"22","author":"Won","year":"2021","journal-title":"Korean J Radiol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0011","doi-asserted-by":"crossref","DOI":"10.1155\/2020\/5418364","article-title":"A nomogram based on radiomics with mammography texture analysis for the prognostic prediction in patients with triple-negative breast cancer","volume":"2020","author":"Jiang","year":"2020","journal-title":"Contrast Media Mol Imaging"},{"key":"10.1016\/j.clbc.2024.01.005_bib0012","doi-asserted-by":"crossref","DOI":"10.1259\/bjr.20210188","article-title":"Radiomics features on ultrasound imaging for the prediction of disease-free survival in triple negative breast cancer: a multi-institutional study","volume":"94","author":"Yu","year":"2021","journal-title":"Br J Radiol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0013","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/978-3-030-33128-3_1","article-title":"Deep learning in medical image analysis","volume":"1213","author":"Chan","year":"2020","journal-title":"Adv Exp Med Biol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0014","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1038\/s41523-022-00478-y","article-title":"Deep learning models for histologic grading of breast cancer and association with disease prognosis","volume":"8","author":"Jaroensri","year":"2022","journal-title":"NPJ Breast Cancer"},{"key":"10.1016\/j.clbc.2024.01.005_bib0015","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1007\/s00330-021-08293-y","article-title":"Deep learning radiomics of ultrasonography can predict response to neoadjuvant chemotherapy in breast cancer at an early stage of treatment: a prospective study","volume":"32","author":"Gu","year":"2021","journal-title":"Eur Radiol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0016","doi-asserted-by":"crossref","first-page":"596364","DOI":"10.3389\/fonc.2021.596364","article-title":"Deep learning-based prediction model for breast cancer recurrence using adjuvant breast cancer cohort in tertiary cancer center registry","volume":"11","author":"Kim","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0017","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.21037\/qims-20-922","article-title":"Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML vision in ultrasound breast lesions classification: a comparative study","volume":"11","author":"Wan","year":"2021","journal-title":"Quant Imaging Med Surg"},{"key":"10.1016\/j.clbc.2024.01.005_bib0018","doi-asserted-by":"crossref","first-page":"621993","DOI":"10.3389\/fonc.2021.621993","article-title":"Ultrasound-based radiomics analysis for predicting disease-free survival of invasive breast cancer","volume":"11","author":"Xiong","year":"2021","journal-title":"Front Oncol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0019","first-page":"152","article-title":"Association of machine learning ultrasound radiomics and disease outcome in triple negative breast cancer","volume":"12","author":"Wang","year":"2022","journal-title":"Am J Cancer Res"},{"key":"10.1016\/j.clbc.2024.01.005_bib0020","doi-asserted-by":"crossref","first-page":"e48","DOI":"10.5858\/134.7.e48","article-title":"American Society of Clinical Oncology\/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer (unabridged version)","volume":"134","author":"Hammond","year":"2010","journal-title":"Arch Pathol Lab Med"},{"key":"10.1016\/j.clbc.2024.01.005_bib0021","doi-asserted-by":"crossref","first-page":"1736","DOI":"10.1093\/annonc\/mdr304","article-title":"Strategies for subtypes\u2014dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011","volume":"22","author":"Goldhirsch","year":"2011","journal-title":"Ann Oncol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0022","doi-asserted-by":"crossref","first-page":"6575","DOI":"10.1007\/s00330-022-08910-4","article-title":"Survival outcome assessment for triple-negative breast cancer: a nomogram analysis based on integrated clinicopathological, sonographic, and mammographic characteristics","volume":"32","author":"Sheng","year":"2022","journal-title":"Eur Radiol"},{"key":"10.1016\/j.clbc.2024.01.005_bib0023","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.diii.2017.01.001","article-title":"BI-RADS\u00ae fifth edition: a summary of changes","volume":"98","author":"Spak","year":"2017","journal-title":"Diagn Int Imaging"},{"key":"10.1016\/j.clbc.2024.01.005_bib0024","first-page":"859","article-title":"An automated in-depth feature learning algorithm for breast abnormality prognosis and robust characterization from mammography images using deep transfer learning","volume":"10","author":"Mahmood","year":"2021","journal-title":"Biology (Basel)"},{"key":"10.1016\/j.clbc.2024.01.005_bib0025","first-page":"439","article-title":"Ensemble deep-learning-enabled clinical decision support system for breast cancer diagnosis and classification on ultrasound images","volume":"11","author":"Ragab","year":"2022","journal-title":"Biology (Basel)"},{"key":"10.1016\/j.clbc.2024.01.005_bib0026","doi-asserted-by":"crossref","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":"10.1016\/j.clbc.2024.01.005_bib0027","first-page":"1","article-title":"Ultrasound as an adjunct to mammography for breast cancer screening: a health technology assessment","volume":"16","year":"2016","journal-title":"Ontario Health Technol Assess Series"},{"key":"10.1016\/j.clbc.2024.01.005_bib0028","doi-asserted-by":"crossref","first-page":"e7695","DOI":"10.1371\/journal.pone.0007695","article-title":"Breast cancer in young women: poor survival despite intensive treatment","volume":"4","author":"Fredholm","year":"2009","journal-title":"PLoS One"},{"key":"10.1016\/j.clbc.2024.01.005_bib0029","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3892\/ol.2017.6112","article-title":"Mammographic calcification can predict outcome in women with breast cancer treated with breast-conserving surgery","volume":"14","author":"Qi","year":"2017","journal-title":"Oncol Lett"},{"key":"10.1016\/j.clbc.2024.01.005_bib0030","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1016\/j.asjsur.2018.10.009","article-title":"Prognostic factors predicting recurrence in invasive breast cancer: an analysis of radiological and clinicopathological factors","volume":"42","author":"Chung","year":"2019","journal-title":"Asian J Surg"},{"key":"10.1016\/j.clbc.2024.01.005_bib0031","doi-asserted-by":"crossref","first-page":"10544","DOI":"10.1038\/s41598-019-47118-3","article-title":"Mammographic casting-type calcification is an independent prognostic factor in invasive breast cancer","volume":"9","author":"Li","year":"2019","journal-title":"Sci Rep"},{"key":"10.1016\/j.clbc.2024.01.005_bib0032","first-page":"6458","article-title":"Association of ultrasonographic features with NGX6 expression and prognosis in invasive ductal breast carcinoma","volume":"8","author":"YZ","year":"2015","journal-title":"Int J Clin Exp Pathol"}],"container-title":["Clinical Breast Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1526820924000065?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1526820924000065?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T12:59:44Z","timestamp":1729083584000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1526820924000065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":32,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["S1526820924000065"],"URL":"https:\/\/doi.org\/10.1016\/j.clbc.2024.01.005","relation":{},"ISSN":["1526-8209"],"issn-type":[{"value":"1526-8209","type":"print"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study","name":"articletitle","label":"Article Title"},{"value":"Clinical Breast Cancer","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.clbc.2024.01.005","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}