{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,20]],"date-time":"2026-01-20T16:54:49Z","timestamp":1768928089046,"version":"3.49.0"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T00:00:00Z","timestamp":1609891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100008980","name":"Soci\u00e9t\u00e9 Fran\u00e7aise de Radiologie","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008980","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s00330-020-07519-9","type":"journal-article","created":{"date-parts":[[2021,1,6]],"date-time":"2021-01-06T06:02:52Z","timestamp":1609912972000},"page":"4848-4859","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["Radiomic analysis of HTR-DCE MR sequences improves diagnostic performance compared to BI-RADS analysis of breast MR lesions"],"prefix":"10.1007","volume":"31","author":[{"given":"Saskia Vande","family":"Perre","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lo\u00efc","family":"Duron","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Audrey","family":"Milon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asma","family":"Bekhouche","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Balvay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francois H.","family":"Cornelis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laure","family":"Fournier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0919-0133","authenticated-orcid":false,"given":"Isabelle","family":"Thomassin-Naggara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,1,6]]},"reference":[{"key":"7519_CR1","doi-asserted-by":"crossref","unstructured":"Sardanelli F, Boetes C, Borisch B et al (2010) Magnetic resonance imaging of the breast: recommendations from the EUSOMA working group. Eur J Cancer 1990 46(8):1296\u20131316","DOI":"10.1016\/j.ejca.2010.02.015"},{"issue":"3","key":"7519_CR2","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1148\/radiol.2019182947","volume":"292","author":"RM Mann","year":"2019","unstructured":"Mann RM, Cho N, Moy L (2019) Breast MRI: state of the art. Radiology. 292(3):520\u2013536","journal-title":"Radiology."},{"issue":"2","key":"7519_CR3","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1148\/radiol.2442051620","volume":"244","author":"C Kuhl","year":"2007","unstructured":"Kuhl C (2007) The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology. 244(2):356\u2013378","journal-title":"Radiology."},{"issue":"9","key":"7519_CR4","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1097\/RLI.0000000000000057","volume":"49","author":"RM Mann","year":"2014","unstructured":"Mann RM, Mus RD, van Zelst J, Geppert C, Karssemeijer N, Platel B (2014) A novel approach to contrast-enhanced breast magnetic resonance imaging for screening: high-resolution ultrafast dynamic imaging. Invest Radiol 49(9):579\u2013585","journal-title":"Invest Radiol"},{"key":"7519_CR5","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.ejrad.2019.06.022","volume":"117","author":"A Milon","year":"2019","unstructured":"Milon A, Vande Perre S, Poujol J et al (2019) Abbreviated breast MRI combining FAST protocol and high temporal resolution (HTR) dynamic contrast enhanced (DCE) sequence. Eur J Radiol 117:199\u2013208","journal-title":"Eur J Radiol"},{"key":"7519_CR6","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1038\/s41523-017-0045-3","volume":"3","author":"VS Parekh","year":"2017","unstructured":"Parekh VS, Jacobs MA (2017) Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. NPJ Breast Cancer 3:43","journal-title":"NPJ Breast Cancer"},{"issue":"11","key":"7519_CR7","doi-asserted-by":"publisher","first-page":"4849","DOI":"10.1007\/s00330-018-5389-z","volume":"28","author":"S Rizzo","year":"2018","unstructured":"Rizzo S, Botta F, Raimondi S et al (2018) Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months. Eur Radiol 28(11):4849\u20134859","journal-title":"Eur Radiol"},{"issue":"5","key":"7519_CR8","doi-asserted-by":"publisher","first-page":"2680","DOI":"10.1007\/s00330-019-06597-8","volume":"30","author":"G Wu","year":"2020","unstructured":"Wu G, Woodruff HC, Sanduleanu S et al (2020) Preoperative CT-based radiomics combined with intraoperative frozen section is predictive of invasive adenocarcinoma in pulmonary nodules: a multicenter study. Eur Radiol 30(5):2680\u20132691","journal-title":"Eur Radiol"},{"key":"7519_CR9","doi-asserted-by":"publisher","unstructured":"Thomassin-Naggara I, Soualhi N, Balvay D, Darai E, Cuenod C-A (2017) Quantifying tumor vascular heterogeneity with DCE-MRI in complex adnexal masses: a preliminary study. J Magn Reason Imaging. https:\/\/doi.org\/10.1002\/jmri.25707","DOI":"10.1002\/jmri.25707"},{"issue":"16","key":"7519_CR10","doi-asserted-by":"publisher","first-page":"3593","DOI":"10.1088\/0031-9155\/49\/16\/007","volume":"49","author":"Y-C Chang","year":"2004","unstructured":"Chang Y-C, Huang C-S, Liu Y-J, Chen J-H, Lu Y-S, Tseng W-YI (2004) Angiogenic response of locally advanced breast cancer to neoadjuvant chemotherapy evaluated with parametric histogram from dynamic contrast-enhanced MRI. Phys Med Biol 49(16):3593\u20133602","journal-title":"Phys Med Biol"},{"issue":"1","key":"7519_CR11","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1002\/jmri.23971","volume":"38","author":"A Ahmed","year":"2013","unstructured":"Ahmed A, Gibbs P, Pickles M, Turnbull L (2013) Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reason Imaging 38(1):89\u2013101","journal-title":"J Magn Reason Imaging"},{"issue":"3","key":"7519_CR12","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.tranon.2015.03.005","volume":"8","author":"A Ashraf","year":"2015","unstructured":"Ashraf A, Gaonkar B, Mies C et al (2015) Breast DCE-MRI kinetic heterogeneity tumor markers: preliminary associations with neoadjuvant chemotherapy response. Transl Oncol 8(3):154\u2013162","journal-title":"Transl Oncol"},{"issue":"3","key":"7519_CR13","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1148\/radiol.09090838","volume":"254","author":"N Bhooshan","year":"2010","unstructured":"Bhooshan N, Giger ML, Jansen SA, Li H, Lan L, Newstead GM (2010) Cancerous breast lesions on dynamic contrast-enhanced MR images: computerized characterization for image-based prognostic markers. Radiology. 254(3):680\u2013690","journal-title":"Radiology."},{"issue":"3","key":"7519_CR14","doi-asserted-by":"publisher","first-page":"665","DOI":"10.1148\/radiol.2016160261","volume":"282","author":"J-H Kim","year":"2017","unstructured":"Kim J-H, Ko ES, Lim Y et al (2017) Breast cancer heterogeneity: MR imaging texture analysis and survival outcomes. Radiology. 282(3):665\u2013675","journal-title":"Radiology."},{"issue":"1","key":"7519_CR15","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1148\/radiol.14130569","volume":"272","author":"J Parikh","year":"2014","unstructured":"Parikh J, Selmi M, Charles-Edwards G et al (2014) Changes in primary breast cancer heterogeneity may augment midtreatment MR imaging assessment of response to neoadjuvant chemotherapy. Radiology. 272(1):100\u2013112","journal-title":"Radiology."},{"issue":"1","key":"7519_CR16","doi-asserted-by":"publisher","first-page":"23","DOI":"10.18383\/j.tom.2016.00241","volume":"3","author":"G Thibault","year":"2017","unstructured":"Thibault G, Tudorica A, Afzal A et al (2017) DCE-MRI texture features for early prediction of breast cancer therapy response. Tomography 3(1):23\u201332","journal-title":"Tomography"},{"issue":"1","key":"7519_CR17","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1148\/radiol.2018172462","volume":"288","author":"J Wu","year":"2018","unstructured":"Wu J, Cao G, Sun X et al (2018) Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy. Radiology. 288(1):26\u201335","journal-title":"Radiology."},{"key":"7519_CR18","doi-asserted-by":"crossref","unstructured":"Wu J, Gong G, Cui Y, Li R (2016) Intratumor partitioning and texture analysis of dynamic contrast-enhanced (DCE)-MRI identifies relevant tumor subregions to predict pathological response of breast cancer to neoadjuvant chemotherapy. J Magn Reason Imaging 44(5):1107\u20131115","DOI":"10.1002\/jmri.25279"},{"issue":"1","key":"7519_CR19","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","volume":"19","author":"NM Braman","year":"2017","unstructured":"Braman NM, Etesami M, Prasanna P et al (2017) Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res 19(1):57","journal-title":"Breast Cancer Res"},{"issue":"2","key":"7519_CR20","doi-asserted-by":"publisher","first-page":"e0171683","DOI":"10.1371\/journal.pone.0171683","volume":"12","author":"M Fan","year":"2017","unstructured":"Fan M, Li H, Wang S, Zheng B, Zhang J, Li L (2017) Radiomic analysis reveals DCE-MRI features for prediction of molecular subtypes of breast cancer. PLoS One 12(2):e0171683","journal-title":"PLoS One"},{"issue":"1","key":"7519_CR21","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1002\/jmri.25921","volume":"48","author":"M Fan","year":"2018","unstructured":"Fan M, Cheng H, Zhang P et al (2018) DCE-MRI texture analysis with tumor subregion partitioning for predicting Ki-67 status of estrogen receptor-positive breast cancers. J Magn Reason Imaging 48(1):237\u2013247","journal-title":"J Magn Reason Imaging"},{"issue":"2","key":"7519_CR22","doi-asserted-by":"publisher","first-page":"382","DOI":"10.1148\/radiol.2016152110","volume":"281","author":"H Li","year":"2016","unstructured":"Li H, Zhu Y, Burnside ES et al (2016) MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 gene assays. Radiology. 281(2):382\u2013391","journal-title":"Radiology."},{"issue":"2","key":"7519_CR23","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1148\/radiol.2018181352","volume":"290","author":"D Truhn","year":"2019","unstructured":"Truhn D, Schrading S, Haarburger C, Schneider H, Merhof D, Kuhl C (2019) Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology. 290(2):290\u2013297","journal-title":"Radiology."},{"issue":"8","key":"7519_CR24","doi-asserted-by":"publisher","first-page":"4456","DOI":"10.1007\/s00330-018-5891-3","volume":"29","author":"M Fan","year":"2019","unstructured":"Fan M, Zhang P, Wang Y et al (2019) Radiomic analysis of imaging heterogeneity in tumours and the surrounding parenchyma based on unsupervised decomposition of DCE-MRI for predicting molecular subtypes of breast cancer. Eur Radiol 29(8):4456\u20134467","journal-title":"Eur Radiol"},{"key":"7519_CR25","doi-asserted-by":"publisher","unstructured":"Lo Gullo R, Daimiel I, Rossi Saccarelli C et al (2020) Improved characterization of sub-centimeter enhancing breast masses on MRI with radiomics and machine learning in BRCA mutation carriers. Eur Radiol. https:\/\/doi.org\/10.1007\/s00330-020-06991-7","DOI":"10.1007\/s00330-020-06991-7"},{"issue":"1","key":"7519_CR26","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s41747-019-0131-4","volume":"4","author":"NC D\u2019Amico","year":"2020","unstructured":"D\u2019Amico NC, Grossi E, Valbusa G et al (2020) A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI. Eur Radiol Exp 4(1):5","journal-title":"Eur Radiol Exp"},{"issue":"6","key":"7519_CR27","doi-asserted-by":"publisher","first-page":"1484","DOI":"10.1002\/jmri.23602","volume":"35","author":"M Saranathan","year":"2012","unstructured":"Saranathan M, Rettmann DW, Hargreaves BA, Clarke SE, Vasanawala SS (2012) DIfferential Subsampling with Cartesian Ordering (DISCO): a high spatio-temporal resolution Dixon imaging sequence for multiphasic contrast enhanced abdominal imaging. J Magn Reson Imaging 35(6):1484\u20131492","journal-title":"J Magn Reson Imaging"},{"key":"7519_CR28","volume-title":"ACR BI-RADS Atlas - Breast Imaging Reporting and Data System Atlas","author":"EA Morris","year":"2013","unstructured":"Morris EA, Comstock CE, Lee CH (2013) ACR BI-RADS Atlas - Breast Imaging Reporting and Data System Atlas. American College of Radiology, Reston, VA"},{"issue":"3","key":"7519_CR29","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 44(3):837\u2013845","journal-title":"Biometrics."},{"issue":"1","key":"7519_CR30","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1148\/radiol.2481071120","volume":"248","author":"I Thomassin-Naggara","year":"2008","unstructured":"Thomassin-Naggara I, Bazot M, Dara\u00ef E, Callard P, Thomassin J, Cuenod CA (2008) Epithelial ovarian tumors: value of dynamic contrast-enhanced MR imaging and correlation with tumor angiogenesis. Radiology. 248(1):148\u2013159","journal-title":"Radiology."},{"issue":"1","key":"7519_CR31","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1002\/jmri.21377","volume":"28","author":"I Thomassin-Naggara","year":"2008","unstructured":"Thomassin-Naggara I, Dara\u00ef E, Cuenod CA, Rouzier R, Callard P, Bazot M (2008) Dynamic contrast-enhanced magnetic resonance imaging: a useful tool for characterizing ovarian epithelial tumors. J Magn Reson Imaging 28(1):111\u2013120","journal-title":"J Magn Reson Imaging"},{"issue":"3","key":"7519_CR32","doi-asserted-by":"publisher","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","volume":"31","author":"PA Yushkevich","year":"2006","unstructured":"Yushkevich PA, Piven J, Hazlett HC et al (2006) User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 31(3):1116\u20131128","journal-title":"Neuroimage"},{"issue":"21","key":"7519_CR33","doi-asserted-by":"publisher","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","volume":"77","author":"JJM van Griethuysen","year":"2017","unstructured":"van Griethuysen JJM, Fedorov A, Parmar C et al (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77(21):e104\u2013e107","journal-title":"Cancer Res"},{"key":"7519_CR34","unstructured":"Zwanenburg A, Leger S, Valli\u00e8res M, L\u00f6ck S, Initiative for the IBS (2016) Image biomarker standardisation initiative. ArXiv161207003 Cs. [cited 2018 Mar 25]; Available from: http:\/\/arxiv.org\/abs\/1612.07003. Accessed 25 March 2018\u00a0"},{"issue":"3","key":"7519_CR35","doi-asserted-by":"publisher","first-page":"e0213459","DOI":"10.1371\/journal.pone.0213459","volume":"14","author":"L Duron","year":"2019","unstructured":"Duron L, Balvay D, Vande Perre S et al (2019) Gray-level discretization impacts reproducible MRI radiomics texture features. PLoS One 14(3):e0213459","journal-title":"PLoS One"},{"key":"7519_CR36","unstructured":"R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Internet]. 2013. Available from:\u00a0http:\/\/www.R-project.org\/. Accessed 25 Feb 2019"},{"issue":"1","key":"7519_CR37","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","volume":"58","author":"R Tibshirani","year":"1996","unstructured":"Tibshirani R (1996) Regression shrinkage and selection via the Lasso. J R Stat Soc Ser B Methodol 58(1):267\u2013288","journal-title":"J R Stat Soc Ser B Methodol"},{"key":"7519_CR38","doi-asserted-by":"crossref","unstructured":"Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14(12):749\u2013762","DOI":"10.1038\/nrclinonc.2017.141"},{"issue":"9","key":"7519_CR39","doi-asserted-by":"publisher","first-page":"1111","DOI":"10.1016\/j.acra.2018.01.006","volume":"25","author":"C Liang","year":"2018","unstructured":"Liang C, Cheng Z, Huang Y et al (2018) An MRI-based radiomics classifier for preoperative prediction of Ki-67 status in breast cancer. Acad Radiol 25(9):1111\u20131117","journal-title":"Acad Radiol"},{"key":"7519_CR40","doi-asserted-by":"crossref","unstructured":"Harvey SC, Di Carlo PA, Lee B, Obadina E, Sippo D, Mullen L (2016) An abbreviated protocol for high-risk screening breast MRI saves time and resources. J Am Coll Radiol 13(11S):R74\u2013R80","DOI":"10.1016\/j.jacr.2016.09.031"},{"issue":"1090","key":"7519_CR41","doi-asserted-by":"publisher","first-page":"20170441","DOI":"10.1259\/bjr.20170441","volume":"91","author":"CK Kuhl","year":"2018","unstructured":"Kuhl CK (2018) Abbreviated breast MRI for screening women with dense breast: the EA1141 trial. Br J Radiol 91(1090):20170441","journal-title":"Br J Radiol"},{"issue":"12","key":"7519_CR42","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1016\/j.diii.2013.10.010","volume":"94","author":"CA Cuenod","year":"2013","unstructured":"Cuenod CA, Balvay D (2013) Perfusion and vascular permeability: basic concepts and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging 94(12):1187\u20131204","journal-title":"Diagn Interv Imaging"},{"issue":"8","key":"7519_CR43","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/S0730-725X(03)00186-3","volume":"21","author":"C Pradel","year":"2003","unstructured":"Pradel C, Siauve N, Bruneteau G et al (2003) Reduced capillary perfusion and permeability in human tumour xenografts treated with the VEGF signalling inhibitor ZD4190: an in vivo assessment using dynamic MR imaging and macromolecular contrast media. Magn Reson Imaging 21(8):845\u2013851","journal-title":"Magn Reson Imaging"},{"key":"7519_CR44","doi-asserted-by":"crossref","unstructured":"Folkman J. Tumor angiogenesis. In: Klein G, Weinhouse S, editors. Advances in cancer research. Academic Press; 1985 [cited 2018 Jun 6]. p. 175\u2013203. Available from: http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0065230X0860946X.\u00a0Accessed 06 June 2018","DOI":"10.1016\/S0065-230X(08)60946-X"},{"issue":"6","key":"7519_CR45","doi-asserted-by":"publisher","first-page":"713","DOI":"10.1038\/nm0603-713","volume":"9","author":"DM McDonald","year":"2003","unstructured":"McDonald DM, Choyke PL (2003) Imaging of angiogenesis: from microscope to clinic. Nat Med 9(6):713\u2013725","journal-title":"Nat Med"},{"issue":"7\u20138","key":"7519_CR46","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1111\/j.1600-0463.2008.01148.x","volume":"116","author":"D Fukumura","year":"2008","unstructured":"Fukumura D, Jain RK (2008) Imaging angiogenesis and the microenvironment. APMIS 116(7\u20138):695\u2013715","journal-title":"APMIS"},{"issue":"6","key":"7519_CR47","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1038\/nrc1093","volume":"3","author":"G Bergers","year":"2003","unstructured":"Bergers G, Benjamin LE (2003) Tumorigenesis and the angiogenic switch. Nat Rev Cancer 3(6):401\u2013410","journal-title":"Nat Rev Cancer"},{"key":"7519_CR48","doi-asserted-by":"publisher","unstructured":"Reig B, Heacock L, Geras KJ, Moy L (2019) Machine learning in breast MRI. J Magn Reason Imaging. https:\/\/doi.org\/10.1002\/jmri.26852","DOI":"10.1002\/jmri.26852"},{"issue":"3","key":"7519_CR49","doi-asserted-by":"publisher","first-page":"446","DOI":"10.1007\/s10278-010-9298-1","volume":"24","author":"SC Agner","year":"2011","unstructured":"Agner SC, Soman S, Libfeld E et al (2011) Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification. J Digit Imaging 24(3):446\u2013463","journal-title":"J Digit Imaging"},{"issue":"9","key":"7519_CR50","doi-asserted-by":"publisher","first-page":"4652","DOI":"10.1002\/mp.12408","volume":"44","author":"J Milenkovi\u0107","year":"2017","unstructured":"Milenkovi\u0107 J, Dalm\u0131\u015f MU, \u017dgajnar J, Platel B (2017) Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol. Med Phys 44(9):4652\u20134664","journal-title":"Med Phys"},{"key":"7519_CR51","doi-asserted-by":"crossref","unstructured":"Zhou X, Gao F, Duan S et al (2020) Radiomic features of Pk-DCE MRI parameters based on the extensive Tofts model in application of breast cancer. Phys Eng Sci Med 43(2):517\u2013524","DOI":"10.1007\/s13246-020-00852-9"},{"issue":"2","key":"7519_CR52","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s10549-018-4675-4","volume":"169","author":"F Valdora","year":"2018","unstructured":"Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS (2018) Rapid review: radiomics and breast cancer. Breast Cancer Res Treat 169(2):217\u2013229","journal-title":"Breast Cancer Res Treat"},{"key":"7519_CR53","doi-asserted-by":"crossref","unstructured":"Whitney HM, Li H, Ji Y, Liu P, Giger ML (2020) Harmonization of radiomic features of breast lesions across international DCE-MRI datasets. J Med Imaging (Bellingham) 7(1):012707","DOI":"10.1117\/1.JMI.7.1.012707"},{"issue":"1","key":"7519_CR54","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1007\/s10549-019-05519-y","volume":"180","author":"A Rotili","year":"2020","unstructured":"Rotili A, Trimboli RM, Penco S et al (2020) Double reading of diffusion-weighted magnetic resonance imaging for breast cancer detection. Breast Cancer Res Treat 180(1):111\u2013120","journal-title":"Breast Cancer Res Treat"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-020-07519-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-020-07519-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-020-07519-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T23:06:26Z","timestamp":1724281586000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-020-07519-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,6]]},"references-count":54,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["7519"],"URL":"https:\/\/doi.org\/10.1007\/s00330-020-07519-9","relation":{},"ISSN":["0938-7994","1432-1084"],"issn-type":[{"value":"0938-7994","type":"print"},{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,6]]},"assertion":[{"value":"7 June 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 September 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 January 2021","order":4,"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 scientific guarantor of this publication is Isabelle Thomassin-Naggara.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"Saskia Vande Perre, Loic Duron, Audrey Milon, Asma Bekhouche, Daniel Balvay, and Fran\u00e7ois H. Cornelis: no relationships with any companies, whose products or services may be related to the subject matter of the article.Laure Fournier: Receipt of grants\/research supports (related with the subject matter of the article) with Philips, ArianaPharma, Evolucare, Invectys. Receipt of honoraria or consultation fees (not related with the subject) with Novartis, Janssen, and Sanofi. Speaker fees (not related with the subject) with Novartis, Bayer, Janssen, Sanofi, Pfizer, GE Healthcare.Isabelle Thomassin - Naggara: Remunerated lecture with GE, Hologic, Canon, Guerbet, and one participation to board expert meeting (Siemens).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Nicolas Mion, engineer, kindly provided statistical advice for this manuscript.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.CEROG 2018-GYN-0803.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"One study has been previously published on the same cohort:Milon, A. et al Abbreviated breast MRI combining FAST protocol and high temporal resolution (HTR) dynamic contrast enhanced (DCE) sequence. <i>European Journal of Radiology<\/i> 117, 199\u2013208 (2019).","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 retrospective\u2022 diagnostic study\u2022 performed at one institution","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}