{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T15:20:55Z","timestamp":1742397655352,"version":"3.37.3"},"reference-count":23,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T00:00:00Z","timestamp":1596499200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.61003175","No.61973220"],"award-info":[{"award-number":["No.61003175","No.61973220"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shenzhen Science and Technology Innovation Council","award":["JCY20170818141853626","JCY20160608173106220"],"award-info":[{"award-number":["JCY20170818141853626","JCY20160608173106220"]}]},{"name":"Natural Science Foundation of Liaoning Province of China","award":["No.201602228"],"award-info":[{"award-number":["No.201602228"]}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["No. 20170520153"],"award-info":[{"award-number":["No. 20170520153"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1007\/s11517-020-02232-7","type":"journal-article","created":{"date-parts":[[2020,8,4]],"date-time":"2020-08-04T08:02:37Z","timestamp":1596528157000},"page":"2413-2425","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A comprehensive hierarchical classification based on multi-features of breast DCE-MRI for cancer diagnosis"],"prefix":"10.1007","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2594-7386","authenticated-orcid":false,"given":"Hui","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinke","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiyue","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuowei","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guo","family":"Dan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,4]]},"reference":[{"issue":"8","key":"2232_CR1","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.1002\/cncr.24172","volume":"115","author":"RD Tendulkar","year":"2009","unstructured":"Tendulkar RD, Chellman-Jeffers M, Rybicki LA, Rim A, Kotwal A, Macklis R, Obi BB (2009) Preoperative breast magnetic resonance imaging in early breast cancer. Cancer. 115(8):1621\u20131630","journal-title":"Cancer."},{"key":"2232_CR2","doi-asserted-by":"crossref","unstructured":"Nutter EL, Weiss JE, Marotti JD, Jr Eliassen MS, Goodrich ME, et al (2017) Personal history of proliferative breast disease with atypia and risk of multifocal breast cancer. Cancer 124(suppl 2)","DOI":"10.1002\/cncr.31202"},{"key":"2232_CR3","first-page":"1","volume":"3","author":"MM Eltoukhy","year":"2018","unstructured":"Eltoukhy MM, Elhoseny M, Hosny KM, Singh AK (2018) Computer aided detection of mammographic mass using exact Gaussian\u2013Hermite moments. J Ambient Intell Humaniz Comput 3:1\u20139","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"2232_CR4","first-page":"1","volume":"6","author":"M Al-Ayyoub","year":"2018","unstructured":"Al-Ayyoub M, Al-Mnayyis N, Alsmirat MA, Alawneh K, Jararweh Y, Gupta BB (2018) SIFT based ROI extraction for lumbar disk herniation CAD system from MRI axial scans. J Ambient Intell Humaniz Comput 6:1\u20139","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"4","key":"2232_CR5","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1118\/1.3691178","volume":"39","author":"S Agliozzo","year":"2012","unstructured":"Agliozzo S, De LM, Bracco C, Vignati A, Giannini V, Martincich L et al (2012) Computer-aided diagnosis for dynamic contrast-enhanced breast MRI of mass-like lesions using a multiparametric model combining a selection of morphological, kinetic, and spatiotemporal features. Med Phys 39(4):1704\u20131715","journal-title":"Med Phys"},{"issue":"5","key":"2232_CR6","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1016\/j.mri.2014.01.008","volume":"32","author":"C Yeun-Chung","year":"2014","unstructured":"Yeun-Chung C, Yan-Hao H, Chiun-Sheng H, Jeon-Hor C, Ruey-Feng C (2014) Computerized breast lesions detection using kinetic and morphologic analysis for dynamic contrast-enhanced MRI. Magn Reson Imaging 32(5):514\u2013522","journal-title":"Magn Reson Imaging"},{"issue":"1","key":"2232_CR7","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1118\/1.4937787","volume":"43","author":"MU Dalm\u0131\u015f","year":"2016","unstructured":"Dalm\u0131\u015f MU, Gubern-M\u00e9rida A, Vreemann S, Karssemeijer N, Mann R, Platel B (2016) A computer-aided diagnosis system for breast DCE-MRI at high spatiotemporal resolution. Med Phys 43(1):84","journal-title":"Med Phys"},{"issue":"4","key":"2232_CR8","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1002\/jmri.25661","volume":"46","author":"J Wu","year":"2017","unstructured":"Wu J, Sun X, Wang J, Cui Y, Kato F, Shirato H, Ikeda DM, Li R (2017) Identifying relations between imaging phenotypes and molecular subtypes of breast cancer: model discovery and external validation. J Magn Reson Imaging 46(4):1017\u20131027","journal-title":"J Magn Reson Imaging"},{"issue":"3","key":"2232_CR9","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.mri.2011.12.002","volume":"30","author":"C Yeun-Chung","year":"2012","unstructured":"Yeun-Chung C, Yan-Hao H, Chiun-Sheng H, Pei-Kang C, Jeon-Hor C, Ruey-Feng C (2012) Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering. Magn Reson Imaging 30(3):312\u2013322","journal-title":"Magn Reson Imaging"},{"issue":"1","key":"2232_CR10","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1118\/1.4903280","volume":"42","author":"Q Yang","year":"2015","unstructured":"Yang Q, Li L, Zhang J, Shao G, Zheng B (2015) A new quantitative image analysis method for improving breast cancer diagnosis using DCE-MRI examinations. Med Phys 42(1):103\u2013109","journal-title":"Med Phys"},{"issue":"1","key":"2232_CR11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13046-014-0119-0","volume":"34","author":"J Yin","year":"2015","unstructured":"Yin J, Yang J, Han L, Guo Q, Zhang W (2015) Quantitative discrimination between invasive ductal carcinomas and benign lesions based on semi-automatic analysis of time intensity curves from breast dynamic contrast enhanced MRI. J Exp Clin Cancer Res 34(1):1\u201310","journal-title":"J Exp Clin Cancer Res"},{"issue":"1","key":"2232_CR12","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1186\/s12880-017-0239-z","volume":"17","author":"K Holli-Helenius","year":"2017","unstructured":"Holli-Helenius K, Salminen A, Rinta-Kiikka I, Koskivuo I, Br\u00fcck N, Bostr\u00f6m P, Parkkola R (2017) MRI texture analysis in differentiating luminal A and luminal B breast cancer molecular subtypes - a feasibility study. BMC Med Imaging 17(1):69","journal-title":"BMC Med Imaging"},{"key":"2232_CR13","doi-asserted-by":"crossref","unstructured":"Fan M, Cheng H, Zhang P, Gao X, Zhang J, Shao G et al (2017) DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers. J Magn Reson Imaging","DOI":"10.1002\/jmri.25921"},{"key":"2232_CR14","doi-asserted-by":"crossref","unstructured":"Milenkovi\u0107 J, Dalm\u0131\u015f MU, J \u017dgajnar, Platel B. Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol. Medical Physics. 2017;44(9):4652-4664","DOI":"10.1002\/mp.12408"},{"issue":"1","key":"2232_CR15","doi-asserted-by":"crossref","first-page":"13638","DOI":"10.1038\/s41598-017-13977-x","volume":"7","author":"A Sadeghinaini","year":"2017","unstructured":"Sadeghinaini A, Suraweera H, Tran WT (2017) Breast-lesion characterization using textural features of quantitative ultrasound parametric maps. Sci Rep 7(1):13638","journal-title":"Sci Rep"},{"issue":"3","key":"2232_CR16","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1007\/s10278-015-9856-7","volume":"29","author":"E Honda","year":"2015","unstructured":"Honda E, Nakayama R, Koyama H, Yamashita A (2015) Computer-aided diagnosis scheme for distinguishing between benign and malignant masses in breast DCE-MRI. J Digit Imaging 29(3):388\u2013393","journal-title":"J Digit Imaging"},{"issue":"3","key":"2232_CR17","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1148\/radiol.2015150241","volume":"278","author":"C Gallego-Ortiz","year":"2016","unstructured":"Gallego-Ortiz C, Martel AL (2016) Improving the accuracy of computer-aided diagnosis for breast MR imaging by differentiating between mass and nonmass lesions. Radiology. 278(3):679\u2013688","journal-title":"Radiology."},{"key":"2232_CR18","doi-asserted-by":"crossref","unstructured":"Antropova N, Huynh B, Giger M (eds) (2017) Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI. Medical Imaging: Computer-aided Diagnosis","DOI":"10.1117\/12.2255582"},{"key":"2232_CR19","doi-asserted-by":"crossref","unstructured":"Amit G, Ben-Ari R, Hadad O, Monovich E, Granot N, Hashoul S (2017) Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches: SPIE","DOI":"10.1117\/12.2249981"},{"key":"2232_CR20","doi-asserted-by":"crossref","unstructured":"Li J, Fan M, Zhang J, Li L. Discriminating between benign and malignant breast tumors using 3D convolutional neural network in dynamic contrast enhanced-MR images: SPIE; 2017","DOI":"10.1117\/12.2254716"},{"key":"2232_CR21","doi-asserted-by":"crossref","unstructured":"Zhu Z, Albadawy E, Saha A, Zhang J, Harowicz M, Mazurowski M (2017) Deep learning for identifying radiogenomic associations in breast cancer. Comput Biol Med 109","DOI":"10.1016\/j.compbiomed.2019.04.018"},{"issue":"8","key":"2232_CR22","first-page":"082303","volume":"41","author":"L Hui","year":"2014","unstructured":"Hui L, Yiping L, Zuowei Z, Lina Z, Tianshuang Q (2014) A new background distribution-based active contour model for three-dimensional lesion segmentation in breast DCE-MRI. Med Phys 41(8):082303","journal-title":"Med Phys"},{"issue":"2","key":"2232_CR23","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1002\/asmb.537","volume":"21","author":"PH Chen","year":"2005","unstructured":"Chen PH, Lin CJ, Sch\u00f6lkopf B (2005) A tutorial on \u03bd-support vector machines. Appl Stoch Model Bus Ind 21(2):111\u2013136","journal-title":"Appl Stoch Model Bus Ind"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02232-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-020-02232-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-020-02232-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,3]],"date-time":"2021-08-03T23:14:44Z","timestamp":1628032484000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-020-02232-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,4]]},"references-count":23,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2020,10]]}},"alternative-id":["2232"],"URL":"https:\/\/doi.org\/10.1007\/s11517-020-02232-7","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"type":"print","value":"0140-0118"},{"type":"electronic","value":"1741-0444"}],"subject":[],"published":{"date-parts":[[2020,8,4]]},"assertion":[{"value":"22 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 July 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 August 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"The Ethics Committee of Dalian University of Technology reviews the study and the study is approved. IRB Approval Number is 2019-053.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}]}}