{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T23:59:52Z","timestamp":1772150392661,"version":"3.50.1"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National key research and development plan digital diagnosis and treatment equipment research and development fund","award":["2017YFC0108900"],"award-info":[{"award-number":["2017YFC0108900"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Imaging"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objective<\/jats:title>\n                <jats:p>This study is aimed to explore the value of mammography-based radiomics signature for preoperative prediction of triple-negative breast cancer (TNBC).<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Materials and methods<\/jats:title>\n                <jats:p>Initially, the clinical and X-ray data of patients (n\u2009=\u2009319, age of 54\u2009\u00b1\u200914) with breast cancer (triple-negative\u201465, non-triple-negative\u2014254) from the First Affiliated Hospital of Soochow University (n\u2009=\u2009211, as a training set) and Suzhou Municipal Hospital (n\u2009=\u2009108, as a verification set) from January 2018 to February 2021 are retrospectively analyzed. Comparing the mediolateral oblique (MLO) and cranial cauda (CC) mammography images, the mammography images with larger lesion areas are selected, and the image segmentation and radiomics feature extraction are then performed by the MaZda software. Further, the Fisher coefficients (Fisher), classification error probability combined average correlation coefficients (POE\u2009+\u2009ACC), and mutual information (MI) are used to select three sets of feature subsets. Moreover, the score of each patient\u2019s radiomics signature (Radscore) is calculated. Finally, the receiver operating characteristic curve (ROC) is analyzed to calculate the AUC, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of TNBC.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>A significant difference in the mammography manifestation between the triple-negative and the non-triple-negative groups (<jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001) is observed. The (POE\u2009+\u2009ACC)-NDA method showed the highest accuracy of 88.39%. The Radscore of triple-negative and non-triple-negative groups in the training set includes \u2212\u20090.678 (\u2212\u20091.292, 0.088) and \u2212\u20092.536 (\u2212\u20093.496, \u2212\u20091.324), respectively, with a statistically significant difference (<jats:italic>Z<\/jats:italic>\u2009=\u2009\u2212\u20096.314, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001). In contrast, the Radscore in the validation set includes \u2212\u20090.750 (\u2212\u20091.332, \u2212\u20090.054) and \u2212\u20092.223 (\u2212\u20092.963, \u2212\u20091.256), with a statistically significant difference (<jats:italic>Z<\/jats:italic>\u2009=\u2009\u2212\u20094.669, <jats:italic>P<\/jats:italic>\u2009&lt;\u20090.001). In the training set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC include 0.821 (95% confidence interval 0.752\u20130.890), 74.4%, 82.5%, 72.5%, 41.2%, and 94.6%, respectively. In the validation set, the AUC, accuracy, sensitivity, specificity, positive predictive value and negative predictive value of TNBC are of 0.809 (95% confidence interval 0.711\u20130.907), 80.6%, 72.0%, 80.7%, 55.5%, and 93.1%, respectively.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In summary, we firmly believe that this mammography-based radiomics signature could be useful in the preoperative prediction of TNBC due to its high value.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12880-022-00875-6","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T04:02:44Z","timestamp":1663128164000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Application of mammography-based radiomics signature for preoperative prediction of triple-negative breast cancer"],"prefix":"10.1186","volume":"22","author":[{"given":"Shuai","family":"Ge","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Yixing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Jia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Ling","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"issue":"1","key":"875_CR1","doi-asserted-by":"publisher","first-page":"52","DOI":"10.3322\/caac.21203","volume":"64","author":"C DeSantis","year":"2014","unstructured":"DeSantis C, et al. Breast cancer statistics, 2013. CA Cancer J Clin. 2014;64(1):52\u201362.","journal-title":"CA Cancer J Clin"},{"key":"875_CR2","doi-asserted-by":"publisher","first-page":"c3620","DOI":"10.1136\/bmj.c3620","volume":"341","author":"P Autier","year":"2010","unstructured":"Autier P, et al. Disparities in breast cancer mortality trends between 30 European countries: retrospective trend analysis of WHO mortality database. BMJ. 2010;341:c3620.","journal-title":"BMJ"},{"issue":"22","key":"875_CR3","doi-asserted-by":"publisher","first-page":"5463","DOI":"10.1002\/cncr.27581","volume":"118","author":"NU Lin","year":"2012","unstructured":"Lin NU, et al. Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network. Cancer. 2012;118(22):5463\u201372.","journal-title":"Cancer"},{"issue":"4","key":"875_CR4","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1097\/GRF.0000000000000239","volume":"59","author":"K Rojas","year":"2016","unstructured":"Rojas K, Stuckey A. Breast cancer epidemiology and risk factors. Clin Obstet Gynecol. 2016;59(4):651\u201372.","journal-title":"Clin Obstet Gynecol"},{"issue":"6","key":"875_CR5","doi-asserted-by":"publisher","first-page":"432","DOI":"10.3322\/caac.21643","volume":"70","author":"JR Bellon","year":"2020","unstructured":"Bellon JR, et al. Multidisciplinary considerations in the treatment of triple-negative breast cancer. CA Cancer J Clin. 2020;70(6):432\u201342.","journal-title":"CA Cancer J Clin"},{"issue":"1","key":"875_CR6","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1148\/radiol.2019182718","volume":"294","author":"NL Eun","year":"2020","unstructured":"Eun NL, et al. Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer. Radiology. 2020;294(1):31\u201341.","journal-title":"Radiology"},{"issue":"2\u20134","key":"875_CR7","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.currproblcancer.2016.09.004","volume":"40","author":"A Gucalp","year":"2016","unstructured":"Gucalp A, Traina TA. Targeting the androgen receptor in triple-negative breast cancer. Curr Probl Cancer. 2016;40(2\u20134):141\u201350.","journal-title":"Curr Probl Cancer"},{"issue":"1","key":"875_CR8","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1186\/s41747-018-0068-z","volume":"2","author":"S Rizzo","year":"2018","unstructured":"Rizzo S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36.","journal-title":"Eur Radiol Exp"},{"issue":"9","key":"875_CR9","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1093\/jnci\/djy222","volume":"111","author":"A Rodriguez-Ruiz","year":"2019","unstructured":"Rodriguez-Ruiz A, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst. 2019;111(9):916\u201322.","journal-title":"J Natl Cancer Inst"},{"issue":"3","key":"875_CR10","first-page":"304","volume":"06","author":"SK Wu","year":"2012","unstructured":"Wu SK, Song ST. Understanding of detection of estrogen and progesterone receptors in breast cancer. Chin J Breast Dis. 2012;06(3):304\u20138.","journal-title":"Chin J Breast Dis"},{"issue":"1","key":"875_CR11","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.cmpb.2008.08.005","volume":"94","author":"PM Szczypinski","year":"2009","unstructured":"Szczypinski PM, et al. MaZda: a software package for image texture analysis. Comput Methods Programs Biomed. 2009;94(1):66\u201376.","journal-title":"Comput Methods Programs Biomed"},{"key":"875_CR12","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.breast.2019.10.018","volume":"49","author":"AS Tagliafico","year":"2020","unstructured":"Tagliafico AS, et al. Overview of radiomics in breast cancer diagnosis and prognostication. Breast. 2020;49:74\u201380.","journal-title":"Breast"},{"issue":"S16","key":"875_CR13","doi-asserted-by":"publisher","first-page":"3857","DOI":"10.1002\/cncr.32963","volume":"126","author":"Y Tang","year":"2020","unstructured":"Tang Y, et al. Nomogram predicting survival as a selection criterion for postmastectomy radiotherapy in patients with T1 to T2 breast cancer with 1 to 3 positive lymph nodes. Cancer. 2020;126(S16):3857\u201366.","journal-title":"Cancer"},{"issue":"6","key":"875_CR14","doi-asserted-by":"publisher","first-page":"e0157368","DOI":"10.1371\/journal.pone.0157368","volume":"11","author":"BD Lehmann","year":"2016","unstructured":"Lehmann BD, et al. Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS ONE. 2016;11(6):e0157368.","journal-title":"PLoS ONE"},{"issue":"3","key":"875_CR15","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1007\/s10549-007-9810-6","volume":"111","author":"WT Yang","year":"2008","unstructured":"Yang WT, et al. Mammographic features of triple receptor-negative primary breast cancers in young premenopausal women. Breast Cancer Res Treat. 2008;111(3):405\u201310.","journal-title":"Breast Cancer Res Treat"},{"issue":"1","key":"875_CR16","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1002\/jcu.22312","volume":"44","author":"M Costantini","year":"2016","unstructured":"Costantini M, Belli P, Bufi E, et al. Association between sonographic appearances of breast cancers and their histopathologic features and biomarkers. J Clin Ultrasound. 2016;44(1):26\u201333.","journal-title":"J Clin Ultrasound"},{"issue":"1","key":"875_CR17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1002\/jcu.21990","volume":"41","author":"M Aho","year":"2013","unstructured":"Aho M, Irshad A, Ackerman SJ, et al. Correlation of sonographic features of invasive ductal mammary carcinoma with age, tumor grade, and hormone-receptor status. J Clin Ultrasound. 2013;41(1):10\u20137.","journal-title":"J Clin Ultrasound"},{"issue":"11","key":"875_CR18","doi-asserted-by":"publisher","first-page":"842","DOI":"10.3760\/cma.j.issn.1005-1201.2018.11.006","volume":"52","author":"WJ Ma","year":"2018","unstructured":"Ma WJ, Zhao Y, Ji Y, et al. The value of predictive model based on mammography features in distinguishing triple-negative and non-triple-negative breast cancer. Chin J Radiol. 2018;52(11):842\u20136. https:\/\/doi.org\/10.3760\/cma.j.issn.1005-1201.2018.11.006.","journal-title":"Chin J Radiol"},{"issue":"3","key":"875_CR19","first-page":"485","volume":"27","author":"HX Zhang","year":"2019","unstructured":"Zhang HX, Sun ZQ, Cheng YG, et al. A pilot study of radiomics technology based on X-ray mammography in patients with triple-negative breast cancer. J Xray Sci Technol. 2019;27(3):485\u201392.","journal-title":"J Xray Sci Technol"},{"issue":"9","key":"875_CR20","first-page":"947","volume":"34","author":"W Zhang","year":"2019","unstructured":"Zhang W, et al. Predicting triple-negative breast cancer based on the imaging omics label of preoperative staging CT. Radiol Pract. 2019;34(9):947\u201351.","journal-title":"Radiol Pract"}],"container-title":["BMC Medical Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00875-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12880-022-00875-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12880-022-00875-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T04:04:35Z","timestamp":1663128275000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedimaging.biomedcentral.com\/articles\/10.1186\/s12880-022-00875-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":20,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["875"],"URL":"https:\/\/doi.org\/10.1186\/s12880-022-00875-6","relation":{},"ISSN":["1471-2342"],"issn-type":[{"value":"1471-2342","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]},"assertion":[{"value":"10 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was under ethics approval of the First Affiliated Hospital of Soochow University. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"All authors report no conflicts of interest.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"166"}}