{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T00:45:34Z","timestamp":1793321134673,"version":"4.5.0"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100018956","name":"NIHR Cambridge Biomedical Research Centre","doi-asserted-by":"publisher","award":["BRC-1215-20014; NIHR203312"],"award-info":[{"award-number":["BRC-1215-20014; NIHR203312"]}],"id":[{"id":"10.13039\/501100018956","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>The aim of this work is to evaluate the performance of deep learning (DL) models for breast cancer diagnosis with MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A literature search was conducted on Web of Science, PubMed, and IEEE Xplore for relevant studies published from January 2015 to February 2024. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42024485371). The quality assessment of diagnostic accuracy studies-2 (QUADAS2) tool and the Must AI Criteria-10 (MAIC-10) checklist were used to assess quality and risk of bias. The meta-analysis included studies reporting DL for breast cancer diagnosis and their performance, from which pooled summary estimates for the area under the curve (AUC), sensitivity, and specificity were calculated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 40 studies were included, of which only 21 were eligible for quantitative analysis. Convolutional neural networks (CNNs) were used in 62.5% (25\/40) of the implemented models, with the remaining 37.5% (15\/40) hybrid composite models (HCMs). The pooled estimates of AUC, sensitivity, and specificity were 0.90 (95% CI: 0.87, 0.93), 88% (95% CI: 86, 91%), and 90% (95% CI: 87, 93%), respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>DL models used for breast cancer diagnosis on MRI achieve high performance. However, there is considerable inherent variability in this analysis. Therefore, continuous evaluation and refinement of DL models is essential to ensure their practicality in the clinical setting.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Key Points<\/jats:title>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Question<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>Can DL models improve diagnostic accuracy in breast MRI, addressing challenges like overfitting and heterogeneity in study designs and imaging sequences<\/jats:italic>\n                      ?\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Findings<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>DL achieved high diagnostic accuracy (AUC 0.90, sensitivity 88%, specificity 90%) in breast MRI, with training size significantly impacting performance metrics (p<\/jats:italic>\n                      \u2009\n                      <jats:italic>&lt;<\/jats:italic>\n                      \u2009\n                      <jats:italic>0.001)<\/jats:italic>\n                      .\n                    <\/jats:p>\n                    <jats:p>\n                      <jats:bold>\n                        <jats:italic>Clinical<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:bold>\n                        <jats:italic>relevance<\/jats:italic>\n                      <\/jats:bold>\n                      <jats:italic>DL models demonstrate high accuracy in breast cancer diagnosis using MRI, showing the potential to enhance diagnostic confidence and reduce radiologist workload, especially with larger datasets minimizing overfitting and improving clinical reliability<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00330-025-11406-6","type":"journal-article","created":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T09:49:51Z","timestamp":1738748991000},"page":"4474-4489","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis"],"prefix":"10.1007","volume":"35","author":[{"given":"Kamarul Amin","family":"Abdullah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Marziali","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muzna","family":"Nanaa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lorena","family":"Escudero S\u00e1nchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas R.","family":"Payne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0124-9962","authenticated-orcid":false,"given":"Fiona J.","family":"Gilbert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,5]]},"reference":[{"key":"11406_CR1","unstructured":"Office for National Statistics. Cancer registration statistics, England (2016) Available via https:\/\/www.ons.gov.uk\/peoplepopulationandcommunity\/healthandsocialcare\/conditionsanddiseases\/datasets\/cancerregistrationstatisticscancerregistrationstatisticsengland. Accessed 14 May 2024"},{"key":"11406_CR2","doi-asserted-by":"publisher","first-page":"1909","DOI":"10.1007\/s00330-017-5121-4","volume":"28","author":"P Clauser","year":"2018","unstructured":"Clauser P, Mann R, Athanasiou A et al (2018) A survey by the European Society of Breast Imaging on the utilisation of breast MRI in clinical practice. Eur Radiol 28:1909\u20131918. https:\/\/doi.org\/10.1007\/s00330-017-5121-4","journal-title":"Eur Radiol"},{"key":"11406_CR3","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1056\/NEJMoa1903986","volume":"381","author":"MF Bakker","year":"2019","unstructured":"Bakker MF, de Lange SV, Pijnappel RM et al (2019) Supplemental MRI screening for women with extremely dense breast tissue. N Engl J Med 381:2091\u20132102. https:\/\/doi.org\/10.1056\/NEJMoa1903986","journal-title":"N Engl J Med"},{"key":"11406_CR4","doi-asserted-by":"publisher","first-page":"527","DOI":"10.1148\/radiol.2015141827","volume":"277","author":"MJ Emaus","year":"2015","unstructured":"Emaus MJ, Bakker MF, Peeters PH et al (2015) MR imaging as an additional screening modality for the detection of breast cancer in women aged 50\u201375 years with extremely dense breasts: the DENSE trial study design. Radiology 277:527\u2013537. https:\/\/doi.org\/10.1148\/radiol.2015141827","journal-title":"Radiology"},{"key":"11406_CR5","doi-asserted-by":"publisher","first-page":"483","DOI":"10.1159\/000515698","volume":"99","author":"S Batchu","year":"2021","unstructured":"Batchu S, Liu F, Amireh A, Waller J, Umair M (2021) A review of applications of machine learning in mammography and future challenges. Oncology 99:483\u2013490. https:\/\/doi.org\/10.1159\/000515698","journal-title":"Oncology"},{"key":"11406_CR6","doi-asserted-by":"publisher","first-page":"S69","DOI":"10.1016\/j.radi.2021.07.021","volume":"27","author":"A-R Wuni","year":"2021","unstructured":"Wuni A-R, Botwe B, Akudjedu T (2021) Impact of artificial intelligence on clinical radiography practice: futuristic prospects in a low resource setting. Radiography (Lond) 27:S69\u2013S73. https:\/\/doi.org\/10.1016\/j.radi.2021.07.021","journal-title":"Radiography (Lond)"},{"key":"11406_CR7","first-page":"531","volume":"101","author":"D Skegg","year":"1988","unstructured":"Skegg D, Paul C, Benson-Cooper D et al (1988) Mammographic screening for breast cancer: prospects for New Zealand. N Z Med J 101:531\u2013533","journal-title":"N Z Med J"},{"key":"11406_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102391","volume":"78","author":"DA Wood","year":"2022","unstructured":"Wood DA, Kafiabadi S, Al Busaidi A et al (2022) Deep learning models for triaging hospital head MRI examinations. Med Image Anal 78:102391. https:\/\/doi.org\/10.1016\/j.media.2022.102391","journal-title":"Med Image Anal"},{"key":"11406_CR9","doi-asserted-by":"publisher","first-page":"618","DOI":"10.1161\/CIRCOUTCOMES.116.003308","volume":"9","author":"RC Deo","year":"2016","unstructured":"Deo RC, Nallamothu BK (2016) Learning about machine learning: the promise and pitfalls of big data and the electronic health record. Circ Cardiovasc Qual Outcomes 9:618\u2013620. https:\/\/doi.org\/10.1161\/CIRCOUTCOMES.116.003308","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"11406_CR10","doi-asserted-by":"publisher","first-page":"686","DOI":"10.1002\/jmri.27332","volume":"54","author":"A Meyer\u2010Base","year":"2021","unstructured":"Meyer\u2010Base A, Morra L, Tahmassebi A, Lobbes M, Meyer\u2010Base U, Pinker K (2021) AI\u2010enhanced diagnosis of challenging lesions in breast MRI: a methodology and application primer. J Magn Reson Imaging 54:686\u2013702","journal-title":"J Magn Reson Imaging"},{"key":"11406_CR11","doi-asserted-by":"publisher","first-page":"190","DOI":"10.3390\/jimaging7090190","volume":"7","author":"P Oza","year":"2021","unstructured":"Oza P, Sharma P, Patel S, Bruno A (2021) A bottom-up review of image analysis methods for suspicious region detection in mammograms. J Imaging 7:190. https:\/\/doi.org\/10.3390\/jimaging7090190","journal-title":"J Imaging"},{"key":"11406_CR12","doi-asserted-by":"publisher","first-page":"1274","DOI":"10.1016\/j.jiph.2020.06.033","volume":"13","author":"T Saba","year":"2020","unstructured":"Saba T (2020) Recent advancement in cancer detection using machine learning: systematic survey of decades, comparisons and challenges. J Infect Public Health 13:1274\u20131289. https:\/\/doi.org\/10.1016\/j.jiph.2020.06.033","journal-title":"J Infect Public Health"},{"key":"11406_CR13","doi-asserted-by":"publisher","first-page":"8767","DOI":"10.3390\/curroncol29110690","volume":"29","author":"E Mahoro","year":"2022","unstructured":"Mahoro E, Akhloufi MA (2022) Applying deep learning for breast cancer detection in radiology. Curr Oncol 29:8767\u20138793. https:\/\/doi.org\/10.3390\/curroncol29110690","journal-title":"Curr Oncol"},{"key":"11406_CR14","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-67441-4","volume":"10","author":"Q Hu","year":"2020","unstructured":"Hu Q, Whitney HM, Giger ML (2020) A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep 10:10536. https:\/\/doi.org\/10.1038\/s41598-020-67441-4","journal-title":"Sci Rep"},{"key":"11406_CR15","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.abo4802","volume":"14","author":"J Witowski","year":"2022","unstructured":"Witowski J, Heacock L, Reig B et al (2022) Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med 14:eabo4802. https:\/\/doi.org\/10.1126\/scitranslmed.abo4802","journal-title":"Sci Transl Med"},{"key":"11406_CR16","doi-asserted-by":"publisher","first-page":"388","DOI":"10.1001\/jama.2017.19163","volume":"319","author":"MD McInnes","year":"2018","unstructured":"McInnes MD, Moher D, Thombs BD et al (2018) Preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies: the PRISMA-DTA statement. JAMA 319:388\u2013396. https:\/\/doi.org\/10.1001\/jama.2017.19163","journal-title":"JAMA"},{"key":"11406_CR17","unstructured":"Wu H (2016) Automatic computer aided diagnosis of breast cancer in dynamic contrast enhanced magnetic resonance images. University of Toronto, Canada"},{"key":"11406_CR18","doi-asserted-by":"publisher","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","volume":"155","author":"PF Whiting","year":"2011","unstructured":"Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155:529\u2013536. https:\/\/doi.org\/10.7326\/0003-4819-155-8-201110180-00009","journal-title":"Ann Intern Med"},{"key":"11406_CR19","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1186\/s13244022013559","volume":"14","author":"L Cerda-Alberich","year":"2023","unstructured":"Cerda-Alberich L, Solana J, Mallol P et al (2023) MAIC-10 brief quality checklist for publications using artificial intelligence and medical images. Insights Imaging 14:11. https:\/\/doi.org\/10.1186\/s13244022013559","journal-title":"Insights Imaging"},{"key":"11406_CR20","doi-asserted-by":"publisher","first-page":"e200029","DOI":"10.1148\/ryai.2020200029","volume":"2","author":"J Mongan","year":"2020","unstructured":"Mongan J, Moy L, Kahn Jr CE (2020) Checklist for artificial intelligence in medical imaging (CLAIM): a guide for authors and reviewers. Radiol Artif Intell 2:e200029. https:\/\/doi.org\/10.1148\/ryai.2020200029","journal-title":"Radiol Artif Intell"},{"key":"11406_CR21","doi-asserted-by":"publisher","unstructured":"Tejani AS, Klontzas ME, Gatti AA et al (2024) Checklist for artificial intelligence in medical imaging (CLAIM): 2024 update. Radiol Artif Intell. https:\/\/doi.org\/10.1148\/ryai.240300","DOI":"10.1148\/ryai.240300"},{"key":"11406_CR22","doi-asserted-by":"publisher","first-page":"1624","DOI":"10.1177\/21925682221110527","volume":"12","author":"JR Dettori","year":"2022","unstructured":"Dettori JR, Norvell DC, Chapman JR (2022) Fixed-effect vs random-effects models for meta-analysis: 3 points to consider. Global Spine J 12:1624\u20131626. https:\/\/doi.org\/10.1177\/21925682221110527","journal-title":"Global Spine J"},{"key":"11406_CR23","doi-asserted-by":"publisher","first-page":"557","DOI":"10.1136\/bmj.327.7414.557","volume":"327","author":"JP Higgins","year":"2003","unstructured":"Higgins JP, Thompson SG, Deeks JJ, Altman DG (2003) Measuring inconsistency in meta-analyses. BMJ 327:557\u2013560. https:\/\/doi.org\/10.1136\/bmj.327.7414.557","journal-title":"BMJ"},{"key":"11406_CR24","doi-asserted-by":"publisher","unstructured":"O\u2019Shea K, Nash R (2015) An introduction to convolutional neural networks. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1511.08458","DOI":"10.48550\/arXiv.1511.08458"},{"key":"11406_CR25","doi-asserted-by":"publisher","unstructured":"Targ S, Almeida D, Lyman K (2016) ResNet in ResNet: generalizing residual architectures. Preprint at https:\/\/doi.org\/10.48550\/arXiv.1603.08029","DOI":"10.48550\/arXiv.1603.08029"},{"key":"11406_CR26","doi-asserted-by":"publisher","unstructured":"Verdhan V (2021) VGGNet and AlexNet networks. In: Computer vision using deep learning. Apress, Berkeley, pp 103\u2013139. https:\/\/doi.org\/10.1007\/978-1-4842-6616-8_4","DOI":"10.1007\/978-1-4842-6616-8_4"},{"key":"11406_CR27","doi-asserted-by":"publisher","first-page":"10251","DOI":"10.1007\/s11042-017-5443-x","volume":"77","author":"Y Guo","year":"2018","unstructured":"Guo Y, Liu Y, Bakker EM, Guo Y, Lew MS (2018) CNN-RNN: a large-scale hierarchical image classification framework. Multimed Tools Appl 77:10251\u201310271. https:\/\/doi.org\/10.1007\/s11042-017-5443-x","journal-title":"Multimed Tools Appl"},{"key":"11406_CR28","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1002\/jmri.28713","volume":"59","author":"C Cong","year":"2024","unstructured":"Cong C, Li X, Zhang C et al (2024) MRI-based breast cancer classification and localization by multiparametric feature extraction and combination using deep learning. J Magn Reson Imaging 59:148\u2013161. https:\/\/doi.org\/10.1002\/jmri.28713","journal-title":"J Magn Reson Imaging"},{"key":"11406_CR29","doi-asserted-by":"publisher","first-page":"S166","DOI":"10.1016\/j.acra.2021.03.032","volume":"29","author":"MZ Liu","year":"2022","unstructured":"Liu MZ, Swintelski C, Sun S et al (2022) Weakly supervised deep learning approach to breast MRI assessment. Acad Radiol 29:S166\u2013S172. https:\/\/doi.org\/10.1016\/j.acra.2021.03.032","journal-title":"Acad Radiol"},{"key":"11406_CR30","doi-asserted-by":"publisher","first-page":"S135","DOI":"10.1016\/j.acra.2020.12.001","volume":"29","author":"Y Zhang","year":"2022","unstructured":"Zhang Y, Chan S, Park VY et al (2022) Automatic detection and segmentation of breast cancer on MRI using mask R-CNN trained on non-fat-sat images and tested on fat-sat images. Acad Radiol 29:S135\u2013S144. https:\/\/doi.org\/10.1016\/j.acra.2020.12.001","journal-title":"Acad Radiol"},{"key":"11406_CR31","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1002\/mp.13849","volume":"47","author":"VS Parekh","year":"2020","unstructured":"Parekh VS, Macura KJ, Harvey SC et al (2020) Multiparametric deep learning tissue signatures for a radiological biomarker of breast cancer: preliminary results. Med Phys 47:75\u201388. https:\/\/doi.org\/10.1002\/mp.13849","journal-title":"Med Phys"},{"key":"11406_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patrec.2019.03.022","volume":"125","author":"S Khan","year":"2019","unstructured":"Khan S, Islam N, Jan Z, Din IU, Rodrigues JJC (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 125:1\u20136. https:\/\/doi.org\/10.1016\/j.patrec.2019.03.022","journal-title":"Pattern Recognit Lett"},{"key":"11406_CR33","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1007\/s10278-020-00394-2","volume":"34","author":"A Hizukuri","year":"2021","unstructured":"Hizukuri A, Nakayama R, Nara M, Suzuki M, Namba K (2021) Computer aided diagnosis scheme for distinguishing between benign and malignant masses on breast DCE-MRI images using deep convolutional neural network with Bayesian optimization. J Digit Imaging 34:116\u2013123. https:\/\/doi.org\/10.1007\/s10278-020-00394-2","journal-title":"J Digit Imaging"},{"key":"11406_CR34","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2021\/4430886","volume":"2021","author":"M Sheng","year":"2021","unstructured":"Sheng M, Tang W, Tang J, Zhang M, Gong S, Xing W (2021) Feasibility of using improved convolutional neural network to classify BI-RADS 4 breast lesions: compare deep learning features of the lesion itself and the minimum bounding cube of lesion. Wirel Commun Mob Comput 2021:1\u20139. https:\/\/doi.org\/10.1155\/2021\/4430886","journal-title":"Wirel Commun Mob Comput"},{"key":"11406_CR35","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.946580","volume":"12","author":"J Zhu","year":"2022","unstructured":"Zhu J, Geng J, Shan W et al (2022) Development and validation of a deep learning model for breast lesion segmentation and characterization in multiparametric MRI. Front Oncol 12:946580. https:\/\/doi.org\/10.3389\/fonc.2022.946580","journal-title":"Front Oncol"},{"key":"11406_CR36","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1007\/s00432-022-04142-7","volume":"149","author":"HL Yin","year":"2023","unstructured":"Yin HL, Jiang Y, Xu Z, Jia HH, Lin GW (2023) Combined diagnosis of multiparametric MRI-based deep learning models facilitates differentiating triple-negative breast cancer from fibroadenoma magnetic resonance BI-RADS 4 lesions. J Cancer Res Clin Oncol 149:2575\u20132584. https:\/\/doi.org\/10.1007\/s00432-022-04142-7","journal-title":"J Cancer Res Clin Oncol"},{"key":"11406_CR37","doi-asserted-by":"publisher","first-page":"4857","DOI":"10.1007\/s00330-022-08553-5","volume":"32","author":"L Wang","year":"2022","unstructured":"Wang L, Chang L, Luo R et al (2022) An artificial intelligence system using maximum intensity projection MR images facilitates classification of non-mass enhancement breast lesions. Eur Radiol 32:4857\u20134867. https:\/\/doi.org\/10.1007\/s00330-022-08553-5","journal-title":"Eur Radiol"},{"key":"11406_CR38","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2021.805911","volume":"11","author":"H Yin","year":"2021","unstructured":"Yin H, Jiang Y, Xu Z, Huang W, Chen T, Lin G (2021) Apparent diffusion coefficient-based convolutional neural network model can be better than sole diffusion-weighted magnetic resonance imaging to improve the differentiation of invasive breast cancer from breast ductal carcinoma in situ. Front Oncol 11:805911. https:\/\/doi.org\/10.3389\/fonc.2021.805911","journal-title":"Front Oncol"},{"key":"11406_CR39","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1002\/jmri.26981","volume":"51","author":"J Zhou","year":"2020","unstructured":"Zhou J, Zhang Y, Chang KT et al (2020) Diagnosis of benign and malignant breast lesions on DCE-MRI by using radiomics and deep learning with consideration of peritumor tissue. J Magn Reson Imaging 51:798\u2013809. https:\/\/doi.org\/10.1002\/jmri.26981","journal-title":"J Magn Reson Imaging"},{"key":"11406_CR40","doi-asserted-by":"publisher","first-page":"e200159","DOI":"10.1148\/ryai.2021200159","volume":"3","author":"Q Hu","year":"2021","unstructured":"Hu Q, Whitney HM, Li H, Ji Y, Liu P, Giger ML (2021) Improved classification of benign and malignant breast lesions using deep feature maximum intensity projection MRI in breast cancer diagnosis using dynamic contrast-enhanced MRI. Radiol Artif Intell 3:e200159. https:\/\/doi.org\/10.1148\/ryai.2021200159","journal-title":"Radiol Artif Intell"},{"key":"11406_CR41","doi-asserted-by":"publisher","first-page":"014503","DOI":"10.1117\/1.JMI.5.1.014503","volume":"5","author":"N Antropova","year":"2018","unstructured":"Antropova N, Abe H, Giger ML (2018) Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks. J Med Imaging (Bellingham) 5:014503. https:\/\/doi.org\/10.1117\/1.JMI.5.1.014503","journal-title":"J Med Imaging (Bellingham)"},{"key":"11406_CR42","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1093\/jbi\/wbaa102","volume":"3","author":"S Eskreis-Winkler","year":"2021","unstructured":"Eskreis-Winkler S, Onishi N, Pinker K et al (2021) Using deep learning to improve nonsystematic viewing of breast cancer on MRI. J Breast Imaging 3:201\u2013207. https:\/\/doi.org\/10.1093\/jbi\/wbaa102","journal-title":"J Breast Imaging"},{"key":"11406_CR43","doi-asserted-by":"publisher","first-page":"4960","DOI":"10.1002\/mp.16296","volume":"50","author":"R Sun","year":"2023","unstructured":"Sun R, Zhang X, Xie Y, Nie S (2023) Weakly supervised breast lesion detection in DCE-MRI using self-transfer learning. Med Phys 50:4960\u20134972. https:\/\/doi.org\/10.1002\/mp.16296","journal-title":"Med Phys"},{"key":"11406_CR44","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1016\/j.patcog.2017.08.004","volume":"72","author":"R Rasti","year":"2017","unstructured":"Rasti R, Teshnehlab M, Phung SL (2017) Breast cancer diagnosis in DCE-MRI using mixture ensemble of convolutional neural networks. Pattern Recognit 72:381\u2013390. https:\/\/doi.org\/10.1016\/j.patcog.2017.08.004","journal-title":"Pattern Recognit"},{"key":"11406_CR45","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.3390\/cancers14082042","volume":"14","author":"X Jing","year":"2022","unstructured":"Jing X, Dorrius MD, Wielema M, Sijens PE, Oudkerk M, van Ooijen P (2022) Breast tumor identification in ultrafast MRI using temporal and spatial information. Cancers (Basel) 14:2042. https:\/\/doi.org\/10.3390\/cancers14082042","journal-title":"Cancers (Basel)"},{"key":"11406_CR46","doi-asserted-by":"publisher","first-page":"8706","DOI":"10.1007\/s00330-022-08863-8","volume":"32","author":"X Jing","year":"2022","unstructured":"Jing X, Wielema M, Cornelissen LJ et al (2022) Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time. Eur Radiol 32:8706\u20138715. https:\/\/doi.org\/10.1007\/s00330-022-08863-8","journal-title":"Eur Radiol"},{"key":"11406_CR47","doi-asserted-by":"publisher","first-page":"014502","DOI":"10.1117\/1.JMI.5.1.014502","volume":"5","author":"MU Dalm\u0131\u015f","year":"2018","unstructured":"Dalm\u0131\u015f MU, Vreemann S, Kooi T, Mann RM, Karssemeijer N, Gubern-M\u00e9rida A (2018) Fully automated detection of breast cancer in screening MRI using convolutional neural networks. J Med Imaging (Bellingham) 5:014502. https:\/\/doi.org\/10.1117\/1.JMI.5.1.014502","journal-title":"J Med Imaging (Bellingham)"},{"key":"11406_CR48","doi-asserted-by":"publisher","first-page":"5495","DOI":"10.1109\/JBHI.2023.3311021","volume":"27","author":"M Fan","year":"2023","unstructured":"Fan M, Huang G, Lou J, Gao X, Zeng T, Li L (2023) Cross-parametric generative adversarial network-based magnetic resonance image feature synthesis for breast lesion classification. IEEE J Biomed Health Inform 27:5495\u20135505. https:\/\/doi.org\/10.1109\/JBHI.2023.3311021","journal-title":"IEEE J Biomed Health Inform"},{"key":"11406_CR49","doi-asserted-by":"publisher","DOI":"10.7717\/peerj-cs.1460","volume":"9","author":"W Tang","year":"2023","unstructured":"Tang W, Zhang M, Xu C et al (2023) Diagnostic efficiency of multi-modal MRI based deep learning with Sobel operator in differentiating benign and malignant breast mass lesions-a retrospective study. PeerJ Comput Sci 9:e1460. https:\/\/doi.org\/10.7717\/peerj-cs.1460","journal-title":"PeerJ Comput Sci"},{"key":"11406_CR50","doi-asserted-by":"publisher","first-page":"1582","DOI":"10.3390\/diagnostics13091582","volume":"13","author":"W Wang","year":"2023","unstructured":"Wang W, Wang Y (2023) Deep learning-based modified YOLACT algorithm on magnetic resonance imaging images for screening common and difficult samples of breast cancer. Diagnostics 13:1582. https:\/\/doi.org\/10.3390\/diagnostics13091582","journal-title":"Diagnostics"},{"key":"11406_CR51","doi-asserted-by":"publisher","first-page":"3578","DOI":"10.3934\/mbe.2021180","volume":"18","author":"H Yu","year":"2021","unstructured":"Yu H, Lu W, Sun Q et al (2021) Design and analysis of a robust breast cancer diagnostic system based on multimode MR images. Math Biosci Eng 18:3578\u20133597. https:\/\/doi.org\/10.3934\/mbe.2021180","journal-title":"Math Biosci Eng"},{"key":"11406_CR52","doi-asserted-by":"publisher","first-page":"S161","DOI":"10.1016\/j.acra.2022.12.038","volume":"30","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Liu YL, Nie K et al (2023) Deep learning-based automatic diagnosis of breast cancer on MRI using mask R-CNN for detection followed by ResNet50 for classification. Acad Radiol 30:S161\u2013S171. https:\/\/doi.org\/10.1016\/j.acra.2022.12.038","journal-title":"Acad Radiol"},{"key":"11406_CR53","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1016\/j.mri.2020.03.001","volume":"69","author":"H Feng","year":"2020","unstructured":"Feng H, Cao J, Wang H et al (2020) A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI. Magn Reson Imaging 69:40\u201348. https:\/\/doi.org\/10.1016\/j.mri.2020.03.001","journal-title":"Magn Reson Imaging"},{"key":"11406_CR54","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1097\/RLI.0000000000000976","volume":"58","author":"A Bhowmik","year":"2023","unstructured":"Bhowmik A, Monga N, Belen K et al (2023) Automated triage of screening breast MRI examinations in high-risk women using an ensemble deep learning model. Invest Radiol 58:710\u2013719. https:\/\/doi.org\/10.1097\/RLI.0000000000000976","journal-title":"Invest Radiol"},{"key":"11406_CR55","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/2413706","volume":"2020","author":"H Jiao","year":"2020","unstructured":"Jiao H, Jiang X, Pang Z, Lin X, Huang Y, Li L (2020) Deep convolutional neural networks-based automatic breast segmentation and mass detection in DCE-MRI. Comput Math Methods Med 2020:2413706. https:\/\/doi.org\/10.1155\/2020\/2413706","journal-title":"Comput Math Methods Med"},{"key":"11406_CR56","doi-asserted-by":"publisher","first-page":"917","DOI":"10.1016\/j.mri.2006.03.004","volume":"24","author":"R Warren","year":"2006","unstructured":"Warren R, Hayes C, Pointon L et al (2006) A test of performance of breast MRI interpretation in a multicentre screening study. Magn Reson Imaging 24:917\u2013929. https:\/\/doi.org\/10.1016\/j.mri.2006.03.004","journal-title":"Magn Reson Imaging"},{"key":"11406_CR57","doi-asserted-by":"publisher","first-page":"1144","DOI":"10.1002\/jmri.26721","volume":"50","author":"J Zhou","year":"2019","unstructured":"Zhou J, Luo LY, Dou Q et al (2019) Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. J Magn Reson Imaging 50:1144\u20131151. https:\/\/doi.org\/10.1002\/jmri.26721","journal-title":"J Magn Reson Imaging"},{"key":"11406_CR58","doi-asserted-by":"publisher","first-page":"e0224365","DOI":"10.1371\/journal.pone.0224365","volume":"14","author":"A Vabalas","year":"2019","unstructured":"Vabalas A, Gowen E, Poliakoff E, Casson AJ (2019) Machine learning algorithm validation with a limited sample size. PLoS One 14:e0224365. https:\/\/doi.org\/10.1371\/journal.pone.0224365","journal-title":"PLoS One"},{"key":"11406_CR59","doi-asserted-by":"publisher","first-page":"971","DOI":"10.2214\/AJR.10.5081","volume":"196","author":"D Narayanan","year":"2011","unstructured":"Narayanan D, Madsen K, Kalinyak J, Berg W (2011) Interpretation of positron emission mammography and MRI by experienced breast imaging radiologists: Performance and observer reproducibility. AJR Am J Roentgenol 196:971\u2013981. https:\/\/doi.org\/10.2214\/AJR.10.5081","journal-title":"AJR Am J Roentgenol"},{"key":"11406_CR60","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1148\/radiol.2461061298","volume":"246","author":"NH Peters","year":"2008","unstructured":"Peters NH, Borel Rinkes IH, Zuithoff NP, Mali WP, Moons KG, Peeters PH (2008) Meta-analysis of MR imaging in the diagnosis of breast lesions. Radiology 246:116\u2013124. https:\/\/doi.org\/10.1148\/radiol.2461061298","journal-title":"Radiology"},{"key":"11406_CR61","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1259\/0007-1285-69-827-1009","volume":"69","author":"S Mussurakis","year":"1996","unstructured":"Mussurakis S, Buckley D, Coady A, Turnbull L, Horsman A (1996) Observer variability in the interpretation of contrast enhanced MRI of the breast. Br J Radiol 69:1009\u20131016. https:\/\/doi.org\/10.1259\/0007-1285-69-827-1009","journal-title":"Br J Radiol"},{"key":"11406_CR62","doi-asserted-by":"publisher","first-page":"103319","DOI":"10.1016\/j.bspc.2021.10331","volume":"72","author":"YA Wu","year":"2022","unstructured":"Wu YA, Wu J, Dou YT, Rubert N, Wang Y, Deng J (2022) A deep learning fusion model with evidence-based confidence level analysis for differentiation of malignant and benign breast tumors using dynamic contrast enhanced MRI. Biomed Signal Process Control 72:103319. https:\/\/doi.org\/10.1016\/j.bspc.2021.10331","journal-title":"Biomed Signal Process Control"},{"key":"11406_CR63","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2022.943415","volume":"12","author":"Y Chen","year":"2022","unstructured":"Chen Y, Wang L, Luo R et al (2022) A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons. Front Oncol 12:943415. https:\/\/doi.org\/10.3389\/fonc.2022.943415","journal-title":"Front Oncol"},{"key":"11406_CR64","doi-asserted-by":"publisher","first-page":"169","DOI":"10.3390\/jimaging9090169","volume":"9","author":"JNC Raimundo","year":"2023","unstructured":"Raimundo JNC, Fontes JPP, Gonzaga Mendes Magalh\u00e3es L, Guevara Lopez MA (2023) An innovative faster R-CNN-based framework for breast cancer detection in MRI. J Imaging 9:169. https:\/\/doi.org\/10.3390\/jimaging9090169","journal-title":"J Imaging"},{"key":"11406_CR65","doi-asserted-by":"publisher","first-page":"5897","DOI":"10.1002\/mp.15156","volume":"48","author":"F Ayatollahi","year":"2021","unstructured":"Ayatollahi F, Shokouhi SB, Mann RM, Teuwen J (2021) Automatic breast lesion detection in ultrafast DCE-MRI using deep learning. Med Phys 48:5897\u20135907. https:\/\/doi.org\/10.1002\/mp.15156","journal-title":"Med Phys"},{"key":"11406_CR66","doi-asserted-by":"publisher","first-page":"330","DOI":"10.3390\/diagnostics10050330","volume":"10","author":"M Adachi","year":"2020","unstructured":"Adachi M, Fujioka T, Mori M et al (2020) Detection and diagnosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images. Diagnostics (Basel) 10:330. https:\/\/doi.org\/10.3390\/diagnostics10050330","journal-title":"Diagnostics (Basel)"},{"key":"11406_CR67","doi-asserted-by":"publisher","first-page":"1067","DOI":"10.3390\/diagnostics13061067","volume":"13","author":"L Wang","year":"2023","unstructured":"Wang L, Zhang M, He G, Shen D, Meng M (2023) Classification of breast lesions on DCE-MRI data using a fine-tuned MobileNet. Diagnostics (Basel) 13:1067. https:\/\/doi.org\/10.3390\/diagnostics13061067","journal-title":"Diagnostics"},{"key":"11406_CR68","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1097\/RLI.0000000000000544","volume":"54","author":"M Dalmis","year":"2019","unstructured":"Dalmis M, Gubern-Merida A, Vreemann S et al (2019) Artificial intelligence-based classification of breast lesions imaged with a multiparametric breast MRI protocol with ultrafast DCE-MRI, T2, and DWI. Invest Radiol 54:325\u2013332. https:\/\/doi.org\/10.1097\/RLI.0000000000000544","journal-title":"Invest Radiol"},{"key":"11406_CR69","doi-asserted-by":"publisher","first-page":"3003","DOI":"10.3390\/electronics11193003","volume":"11","author":"H Gui","year":"2022","unstructured":"Gui H, Su T, Pang Z et al (2022) Diagnosis of breast cancer with strongly supervised deep learning neural network. Electronics 11:3003. https:\/\/doi.org\/10.3390\/electronics11193003","journal-title":"Electronics"},{"key":"11406_CR70","doi-asserted-by":"publisher","first-page":"011002","DOI":"10.1117\/1.JMI.6.1.011002","volume":"6","author":"N Antropova","year":"2019","unstructured":"Antropova N, Huynh B, Li H, Giger ML (2019) Breast lesion classification based on dynamic contrast-enhanced magnetic resonance images sequences with long short-term memory networks. J Med Imaging (Bellingham) 6:011002. https:\/\/doi.org\/10.1117\/1.JMI.6.1.011002","journal-title":"J Med Imaging"},{"key":"11406_CR71","doi-asserted-by":"publisher","first-page":"2084","DOI":"10.1007\/s00330-023-10184-3","volume":"34","author":"X Jing","year":"2023","unstructured":"Jing X, Dorrius MD, Zheng S et al (2023) Localization of contrast-enhanced breast lesions in ultrafast screening MRI using deep convolutional neural networks. Eur Radiol 34:2084\u20132092. https:\/\/doi.org\/10.1007\/s00330-023-10184-3","journal-title":"Eur Radiol"},{"key":"11406_CR72","doi-asserted-by":"publisher","unstructured":"Girish G, Spandana P, Vasu B (2023) Breast cancer detection using deep learning. Preprint at https:\/\/doi.org\/10.48550\/arXiv.2304.10386","DOI":"10.48550\/arXiv.2304.10386"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11406-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-025-11406-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-025-11406-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,3]],"date-time":"2025-07-03T12:16:42Z","timestamp":1751545002000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-025-11406-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,5]]},"references-count":72,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["11406"],"URL":"https:\/\/doi.org\/10.1007\/s00330-025-11406-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,5]]},"assertion":[{"value":"17 October 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 December 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 February 2025","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 Fiona J. Gilbert.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Not applicable.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}