{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T02:58:26Z","timestamp":1780887506296,"version":"3.54.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":46,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"the Postdoctor Research Fund of West China Hospital, Sichuan University","award":["(Project No. 2025HXBH040)"],"award-info":[{"award-number":["(Project No. 2025HXBH040)"]}]},{"name":"Sichuan Science and Technology Program","award":["2026NSFSC1775"],"award-info":[{"award-number":["2026NSFSC1775"]}]},{"name":"Noncommunicable Chronic Diseases-National Science and Technology Major Project","award":["No. 2023ZD0502300 [2023ZD0502304]"],"award-info":[{"award-number":["No. 2023ZD0502300 [2023ZD0502304]"]}]},{"name":"National Guidance Fund on Developing Local Science and Technology for Sichuan Province","award":["No. 2023ZYD0167"],"award-info":[{"award-number":["No. 2023ZYD0167"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["(Project Nos. 82441007, 82120108014)"],"award-info":[{"award-number":["(Project Nos. 82441007, 82120108014)"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University","award":["(Project No. ZYGD23003)"],"award-info":[{"award-number":["(Project No. ZYGD23003)"]}]},{"name":"Chengdu Science and Technology Office, major technology application demonstration project","award":["(Project No. 2022-GH03-00017-HZ)"],"award-info":[{"award-number":["(Project No. 2022-GH03-00017-HZ)"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med"],"DOI":"10.1186\/s12916-026-04889-7","type":"journal-article","created":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T18:04:07Z","timestamp":1776967447000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multiparameter concept-based interpretable model for early breast cancer diagnosis and structured reporting: a multi-center, multi-reader, radiologist-in-the-loop study"],"prefix":"10.1186","volume":"24","author":[{"given":"Jiao","family":"Qu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengmei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengyuan","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenlei","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiaxin","family":"Zeng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyang","family":"Tu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiachen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Jing","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hubing","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shi","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Su","family":"Lui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,4,23]]},"reference":[{"issue":"2","key":"4889_CR1","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1148\/radiol.2442051620","volume":"244","author":"C Kuhl","year":"2007","unstructured":"Kuhl C. The current status of breast MR imaging. Part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice. Radiology. 2007;244(2):356\u201378.","journal-title":"Radiology"},{"issue":"3","key":"4889_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, Breast MRI. State of the Art. Radiology. 2019;292(3):520\u201336.","journal-title":"Radiology"},{"issue":"8","key":"4889_CR3","doi-asserted-by":"publisher","first-page":"1136","DOI":"10.1016\/S1470-2045(19)30275-X","volume":"20","author":"S Saadatmand","year":"2019","unstructured":"Saadatmand S, Geuzinge HA, Rutgers EJT, et al. MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial. Lancet Oncol. 2019;20(8):1136\u201347.","journal-title":"Lancet Oncol"},{"issue":"4","key":"4889_CR4","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1097\/RLI.0000000000000934","volume":"58","author":"E Verburg","year":"2023","unstructured":"Verburg E, van Gils CH, van der Velden BHM, et al. Validation of Combined Deep Learning Triaging and Computer-Aided Diagnosis in 2901 Breast MRI Examinations From the Second Screening Round of the Dense Tissue and Early Breast Neoplasm Screening Trial. Invest Radiol. 2023;58(4):293\u20138.","journal-title":"Invest Radiol"},{"issue":"1","key":"4889_CR5","doi-asserted-by":"publisher","first-page":"e241629","DOI":"10.1148\/radiol.241629","volume":"316","author":"F Oviedo","year":"2025","unstructured":"Oviedo F, Kazerouni AS, Liznerski P, et al. Cancer Detection in Breast MRI Screening via Explainable AI Anomaly Detection. Radiology. 2025;316(1):e241629.","journal-title":"Radiology"},{"issue":"664","key":"4889_CR6","doi-asserted-by":"publisher","first-page":"eabo4802","DOI":"10.1126\/scitranslmed.abo4802","volume":"14","author":"J Witowski","year":"2022","unstructured":"Witowski J, Heacock L, Reig B, et al. Improving breast cancer diagnostics with deep learning for MRI. Sci Transl Med. 2022;14(664):eabo4802.","journal-title":"Sci Transl Med"},{"issue":"6","key":"4889_CR7","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1097\/RLI.0000000000000544","volume":"54","author":"MU Dalmi\u015f","year":"2019","unstructured":"Dalmi\u015f MU, Gubern-M\u00e9rida A, Vreemann S, et al. Artificial Intelligence\u2013Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI. Invest Radiol. 2019;54(6):325\u201332.","journal-title":"Invest Radiol"},{"issue":"1","key":"4889_CR8","doi-asserted-by":"publisher","first-page":"3647","DOI":"10.1038\/s41467-025-58798-z","volume":"16","author":"L Luo","year":"2025","unstructured":"Luo L, Wu M, Li M, et al. A large model for non-invasive and personalized management of breast cancer from multiparametric MRI. Nat Commun. 2025;16(1):3647.","journal-title":"Nat Commun"},{"key":"4889_CR9","unstructured":"Bilal A, Ebert D, Lin B. LLMs for Explainable AI: A Comprehensive Survey. arXiv:250400125. 2025."},{"issue":"3","key":"4889_CR10","doi-asserted-by":"publisher","first-page":"e200088","DOI":"10.1148\/ryai.2020200088","volume":"2","author":"A Gastounioti","year":"2020","unstructured":"Gastounioti A, Kontos D. Is It Time to Get Rid of Black Boxes and Cultivate Trust in AI? Radiol Artif Intell. 2020;2(3):e200088.","journal-title":"Radiol Artif Intell"},{"key":"4889_CR11","unstructured":"D'Orsi CJ, Sickles EA, Mendelson EB, Morris EA. ACR BI-RADS Atlas, Breast Imaging Reporting and Data System. 5th ed. Reston, Va: American College of Radiology, 2013."},{"key":"4889_CR12","doi-asserted-by":"publisher","first-page":"101352","DOI":"10.1016\/j.preteyeres.2025.101352","volume":"106","author":"S An","year":"2025","unstructured":"An S, Teo K, McConnell MV, Marshall J, Galloway C, Squirrell D. AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed. Prog Retin Eye Res. 2025;106:101352.","journal-title":"Prog Retin Eye Res"},{"issue":"1","key":"4889_CR13","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1186\/s13058-023-01687-4","volume":"25","author":"R Adam","year":"2023","unstructured":"Adam R, Dell\u2019Aquila K, Hodges L, Maldjian T, Duong TQ. Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review. Breast Cancer Res. 2023;25(1):87.","journal-title":"Breast Cancer Res"},{"key":"4889_CR14","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2007.04612","author":"PW Koh","year":"2020","unstructured":"Koh PW, Nguyen T, Tang YS, et al. Concept Bottleneck Models. 2020. https:\/\/doi.org\/10.48550\/arXiv.2007.04612.","journal-title":"Concept Bottleneck Models"},{"issue":"1","key":"4889_CR15","doi-asserted-by":"publisher","first-page":"3504","DOI":"10.1038\/s41467-025-58801-7","volume":"16","author":"Y Wu","year":"2025","unstructured":"Wu Y, Liu Y, Yang Y, et al. A concept-based interpretable model for the diagnosis of choroid neoplasias using multimodal data. Nat Commun. 2025;16(1):3504.","journal-title":"Nat Commun"},{"issue":"4","key":"4889_CR16","doi-asserted-by":"publisher","first-page":"e230725","DOI":"10.1148\/radiol.230725","volume":"307","author":"LC Adams","year":"2023","unstructured":"Adams LC, Truhn D, Busch F, et al. Leveraging GPT-4 for Post Hoc Transformation of Free-text Radiology Reports into Structured Reporting: A Multilingual Feasibility Study. Radiology. 2023;307(4):e230725.","journal-title":"Radiology"},{"issue":"11","key":"4889_CR17","doi-asserted-by":"publisher","first-page":"e2343689","DOI":"10.1001\/jamanetworkopen.2023.43689","volume":"6","author":"M Benary","year":"2023","unstructured":"Benary M, Wang XD, Schmidt M, et al. Leveraging Large Language Models for Decision Support in Personalized Oncology. JAMA Netw Open. 2023;6(11):e2343689.","journal-title":"JAMA Netw Open"},{"issue":"3","key":"4889_CR18","doi-asserted-by":"publisher","first-page":"e233117","DOI":"10.1148\/radiol.233117","volume":"311","author":"R Bhayana","year":"2024","unstructured":"Bhayana R, Nanda B, Dehkharghanian T, et al. Large Language Models for Automated Synoptic Reports and Resectability Categorization in Pancreatic Cancer. Radiology. 2024;311(3):e233117.","journal-title":"Radiology"},{"issue":"5","key":"4889_CR19","doi-asserted-by":"publisher","first-page":"1296","DOI":"10.1002\/jmri.29807","volume":"62","author":"S Schiaffino","year":"2025","unstructured":"Schiaffino S, Zhang T, Mann RM, Pinker K. The Role of Large Language Models (LLMs) in Breast Imaging Today and in the Near Future. J Magn Reson Imaging. 2025;62(5):1296\u2013304.","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"4889_CR20","doi-asserted-by":"publisher","first-page":"e232133","DOI":"10.1148\/radiol.232133","volume":"311","author":"A Cozzi","year":"2024","unstructured":"Cozzi A, Pinker K, Hidber A, et al. BI-RADS Category Assignments by GPT-3.5, GPT-4, and Google Bard: A Multilanguage Study. Radiology. 2024;311(1):e232133.","journal-title":"Radiology"},{"issue":"3","key":"4889_CR21","doi-asserted-by":"publisher","first-page":"e232255","DOI":"10.1148\/radiol.232255","volume":"310","author":"SH Wu","year":"2024","unstructured":"Wu SH, Tong WJ, Li MD, et al. Collaborative Enhancement of Consistency and Accuracy in US Diagnosis of Thyroid Nodules Using Large Language Models. Radiology. 2024;310(3):e232255.","journal-title":"Radiology"},{"issue":"5","key":"4889_CR22","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1007\/s00330-024-11107-6","volume":"35","author":"F Busch","year":"2025","unstructured":"Busch F, Hoffmann L, Dos Santos DP, et al. Large language models for structured reporting in radiology: past, present, and future. Eur Radiol. 2025;35(5):2589\u2013602.","journal-title":"Eur Radiol"},{"issue":"1","key":"4889_CR23","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1038\/s41746-025-01522-4","volume":"8","author":"A Das","year":"2025","unstructured":"Das A, Talati IA, Chaves JMZ, Rubin D, Banerjee I. Weakly supervised language models for automated extraction of critical findings from radiology reports. NPJ Digit Med. 2025;8(1):257.","journal-title":"NPJ Digit Med"},{"key":"4889_CR24","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, et al. TRIPOD\u2009+\u2009AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.","journal-title":"BMJ"},{"issue":"22","key":"4889_CR25","doi-asserted-by":"publisher","first-page":"5430","DOI":"10.3390\/cancers15225430","volume":"15","author":"C Pescia","year":"2023","unstructured":"Pescia C, Guerini-Rocco E, Viale G, Fusco N. Advances in Early Breast Cancer Risk Profiling: From Histopathology to Molecular Technologies. Cancers (Basel). 2023;15(22):5430.","journal-title":"Cancers (Basel)"},{"issue":"1","key":"4889_CR26","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1148\/radiol.2018171987","volume":"288","author":"GR Kim","year":"2018","unstructured":"Kim GR, Choi JS, Han B-K, et al. Preoperative Axillary US in Early-Stage Breast Cancer: Potential to Prevent Unnecessary Axillary Lymph Node Dissection. Radiology. 2018;288(1):55\u201363.","journal-title":"Radiology"},{"issue":"2","key":"4889_CR27","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. Radiomic versus Convolutional Neural Networks Analysis for Classification of Contrast-enhancing Lesions at Multiparametric Breast MRI. Radiology. 2019;290(2):290\u20137.","journal-title":"Radiology"},{"issue":"1","key":"4889_CR28","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","volume":"143","author":"JA Hanley","year":"1982","unstructured":"Hanley JA, McNeil BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology. 1982;143(1):29\u201336.","journal-title":"Radiology"},{"issue":"8","key":"4889_CR29","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","volume":"18","author":"A Hosny","year":"2018","unstructured":"Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500\u201310.","journal-title":"Nat Rev Cancer"},{"issue":"3","key":"4889_CR30","doi-asserted-by":"publisher","first-page":"798","DOI":"10.1002\/jmri.26981","volume":"51","author":"J Zhou","year":"2019","unstructured":"Zhou J, Zhang Y, Chang KT, et al. 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. 2019;51(3):798\u2013809.","journal-title":"J Magn Reson Imaging"},{"issue":"1","key":"4889_CR31","doi-asserted-by":"publisher","first-page":"e222463","DOI":"10.1148\/radiol.222463","volume":"308","author":"SY Choi","year":"2023","unstructured":"Choi SY, Min JH, Kim JH, et al. Interobserver Variability and Diagnostic Performance in Predicting Malignancy of Pancreatic Intraductal Papillary Mucinous Neoplasm with MRI. Radiology. 2023;308(1):e222463.","journal-title":"Radiology"},{"issue":"2","key":"4889_CR32","doi-asserted-by":"publisher","first-page":"596","DOI":"10.1007\/s00259-021-05492-z","volume":"49","author":"V Romeo","year":"2021","unstructured":"Romeo V, Clauser P, Rasul S, et al. AI-enhanced simultaneous multiparametric 18F-FDG PET\/MRI for accurate breast cancer diagnosis. Eur J Nucl Med Mol Imaging. 2021;49(2):596\u2013608.","journal-title":"Eur J Nucl Med Mol Imaging"},{"key":"4889_CR33","first-page":"2579","volume":"9","author":"L van der Maaten","year":"2008","unstructured":"van der Maaten L, Hinton G. Visualizing Data using t-SNE. J Mach Learn Res. 2008;9:2579\u2013605.","journal-title":"J Mach Learn Res"},{"key":"4889_CR34","doi-asserted-by":"crossref","unstructured":"Liu Y, Zhang T, Gu S, Hybrid Concept Bottleneck M. 2025 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2025.","DOI":"10.1109\/CVPR52734.2025.01879"},{"key":"4889_CR35","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s41073-018-0049-z","volume":"3","author":"B McGillivray","year":"2018","unstructured":"McGillivray B, De Ranieri E. Uptake and outcome of manuscripts in Nature journals by review model and author characteristics. Res Integr Peer Rev. 2018;3:5.","journal-title":"Res Integr Peer Rev"},{"key":"4889_CR36","unstructured":"Cram\u00e9r\u2019s V and Measures of Association for Nominal Variables. R Handbook. https:\/\/rcompanion.org\/handbook\/H_10.html"},{"key":"4889_CR37","unstructured":"WHO Classification of Tumours. https:\/\/publicationsiarcwhoint\/Book-And-Report-Series\/Who-Classification-Of-Tumours\/Breast-Tumours-2019"},{"issue":"3","key":"4889_CR38","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1016\/j.acra.2024.10.014","volume":"32","author":"L Hirsch","year":"2025","unstructured":"Hirsch L, Huang Y, Makse HA, et al. Early Detection of Breast Cancer in MRI Using AI. Acad Radiol. 2025;32(3):1218\u201325.","journal-title":"Acad Radiol"},{"issue":"1","key":"4889_CR39","doi-asserted-by":"publisher","first-page":"159","DOI":"10.2307\/2529310","volume":"33","author":"JR Landis","year":"1977","unstructured":"Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics. 1977;33(1):159\u201374.","journal-title":"Biometrics"},{"issue":"1","key":"4889_CR40","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1038\/s41746-021-00438-z","volume":"4","author":"R Aggarwal","year":"2021","unstructured":"Aggarwal R, Sounderajah V, Martin G, et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. npj Digit Med. 2021;4(1):65.","journal-title":"npj Digit Med"},{"issue":"8","key":"4889_CR41","doi-asserted-by":"publisher","first-page":"4474","DOI":"10.1007\/s00330-025-11406-6","volume":"35","author":"KA Abdullah","year":"2025","unstructured":"Abdullah KA, Marziali S, Nanaa M, Escudero Sanchez L, Payne NR, Gilbert FJ. Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis. Eur Radiol. 2025;35(8):4474\u201389.","journal-title":"Eur Radiol"},{"key":"4889_CR42","doi-asserted-by":"publisher","first-page":"943415","DOI":"10.3389\/fonc.2022.943415","volume":"12","author":"Y Chen","year":"2022","unstructured":"Chen Y, Wang L, Luo R, et al. A deep learning model based on dynamic contrast-enhanced magnetic resonance imaging enables accurate prediction of benign and malignant breast lessons. Front Oncol. 2022;12:943415.","journal-title":"Front Oncol"},{"issue":"6","key":"4889_CR43","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1038\/s41571-024-00891-1","volume":"21","author":"E Hoffmann","year":"2024","unstructured":"Hoffmann E, Masthoff M, Kunz WG, et al. Multiparametric MRI for characterization of the tumour microenvironment. Nat Rev Clin Oncol. 2024;21(6):428\u201348.","journal-title":"Nat Rev Clin Oncol"},{"issue":"1","key":"4889_CR44","doi-asserted-by":"publisher","first-page":"10536","DOI":"10.1038\/s41598-020-67441-4","volume":"10","author":"Q Hu","year":"2020","unstructured":"Hu Q, Whitney HM, Giger ML. A deep learning methodology for improved breast cancer diagnosis using multiparametric MRI. Sci Rep. 2020;10(1):10536.","journal-title":"Sci Rep"},{"issue":"46","key":"4889_CR45","doi-asserted-by":"publisher","first-page":"eadq0856","DOI":"10.1126\/sciadv.adq0856","volume":"10","author":"FM Howard","year":"2024","unstructured":"Howard FM, Hieromnimon HM, Ramesh S, et al. Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features. Sci Adv. 2024;10(46):eadq0856.","journal-title":"Sci Adv"},{"issue":"3","key":"4889_CR46","doi-asserted-by":"publisher","first-page":"e231593","DOI":"10.1148\/radiol.231593","volume":"310","author":"R Doshi","year":"2024","unstructured":"Doshi R, Amin KS, Khosla P, Bajaj S, Chheang S, Forman HP. Quantitative Evaluation of Large Language Models to Streamline Radiology Report Impressions: A Multimodal Retrospective Analysis. Radiology. 2024;310(3):e231593.","journal-title":"Radiology"},{"issue":"2","key":"4889_CR47","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1148\/radiol.14140206","volume":"274","author":"OR Brook","year":"2015","unstructured":"Brook OR, Brook A, Vollmer CM, Kent TS, Sanchez N, Pedrosa I. Structured reporting of multiphasic CT for pancreatic cancer: potential effect on staging and surgical planning. Radiology. 2015;274(2):464\u201372.","journal-title":"Radiology"},{"issue":"13","key":"4889_CR48","doi-asserted-by":"publisher","first-page":"1286","DOI":"10.1001\/jama.2020.14930","volume":"324","author":"A Brady","year":"2020","unstructured":"Brady A, Brink J, Slavotinek J. Radiology and Value-Based Health Care. JAMA. 2020;324(13):1286\u20137.","journal-title":"JAMA"},{"issue":"12","key":"4889_CR49","doi-asserted-by":"publisher","first-page":"3968","DOI":"10.1038\/s41591-025-04008-8","volume":"31","author":"R Handler","year":"2025","unstructured":"Handler R, Sharma S, Hernandez-Boussard T. The fragile intelligence of GPT-5 in medicine. Nat Med. 2025;31(12):3968\u201370.","journal-title":"Nat Med"},{"issue":"2","key":"4889_CR50","doi-asserted-by":"publisher","first-page":"e243774","DOI":"10.1148\/radiol.243774","volume":"316","author":"S Zheng","year":"2025","unstructured":"Zheng S, Zhao N, Wang J, et al. Comparison of a Specialized Large Language Model with GPT-4o for CT and MRI Radiology Report Summarization. Radiology. 2025;316(2):e243774.","journal-title":"Radiology"},{"issue":"6","key":"4889_CR51","doi-asserted-by":"publisher","first-page":"919","DOI":"10.3390\/diagnostics11060919","volume":"11","author":"I Daimiel Naranjo","year":"2021","unstructured":"Daimiel Naranjo I, Gibbs P, Reiner JS, et al. Radiomics and Machine Learning with Multiparametric Breast MRI for Improved Diagnostic Accuracy in Breast Cancer Diagnosis. Diagnostics (Basel). 2021;11(6):919.","journal-title":"Diagnostics (Basel)"},{"issue":"3","key":"4889_CR52","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. 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. 2021;3(3):e200159.","journal-title":"Radiol Artif Intell"},{"issue":"10445","key":"4889_CR53","doi-asserted-by":"publisher","first-page":"2734","DOI":"10.1016\/S0140-6736(24)00425-2","volume":"403","author":"S Delaloge","year":"2024","unstructured":"Delaloge S, Khan SA, Wesseling J, Whelan T. Ductal carcinoma in situ of the breast: finding the balance between overtreatment and undertreatment. Lancet. 2024;403(10445):2734\u201346.","journal-title":"Lancet"},{"issue":"12","key":"4889_CR54","first-page":"9","volume":"35","author":"The Society of Breast Cancer China","year":"2025","unstructured":"The Society of Breast Cancer China A-CA. Guidelines for breast cancer diagnosis and treatment by China Anticancer Association (2026 edition). CHINA Oncol. 2025;35(12):9\u2013104. Breast Oncology Group of the Oncology Branch of the Chinese Medical Association.","journal-title":"CHINA Oncol"}],"container-title":["BMC Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12916-026-04889-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12916-026-04889-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12916-026-04889-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T02:02:42Z","timestamp":1780884162000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12916-026-04889-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,23]]},"references-count":54,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["4889"],"URL":"https:\/\/doi.org\/10.1186\/s12916-026-04889-7","relation":{},"ISSN":["1741-7015"],"issn-type":[{"value":"1741-7015","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,23]]},"assertion":[{"value":"28 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 April 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2026","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 approved by the Ethics Committee on Biomedical Research, West China Hospital of Sichuan University (approval No. 2025\u2009\u2212\u2009949). Informed consent was waived given the retrospective design of the study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"345"}}