{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T23:59:11Z","timestamp":1793404751867,"version":"4.5.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:00:00Z","timestamp":1766966400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:00:00Z","timestamp":1769731200000},"content-version":"vor","delay-in-days":32,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Onc"],"DOI":"10.1007\/s12672-025-04359-1","type":"journal-article","created":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T23:03:22Z","timestamp":1767049402000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Graph neural network-based multi-label classification of breast cancer using multiparametric MRI and clinical features"],"prefix":"10.1007","volume":"17","author":[{"given":"Shahed","family":"Salehzehi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shabnam","family":"Jafarpoor Nesheli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alireza","family":"Eftekhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Shojaee Rocky","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vajihe","family":"Vejdani Noghreiyan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abolfazl","family":"Bazazpour","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeynab Hassani","family":"Arefi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed Masoud","family":"Rezaeijo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"4359_CR1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1056609","volume":"13","author":"G Olorunfemi","year":"2023","unstructured":"Olorunfemi G, et al. Population-based temporal trends and ethnic disparity in breast cancer mortality in South Africa (1999\u20132018): joinpoint and age\u2013period\u2013cohort regression analyses. Front Oncol. 2023;13:1056609.","journal-title":"Front Oncol"},{"key":"4359_CR2","doi-asserted-by":"publisher","DOI":"10.7759\/cureus.13854","author":"A Alaref","year":"2021","unstructured":"Alaref A, et al. Magnetic resonance imaging features in different types of invasive breast cancer: a systematic review of the literature. Cureus. 2021. https:\/\/doi.org\/10.7759\/cureus.13854.","journal-title":"Cureus"},{"issue":"23","key":"4359_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/jcm10235668","volume":"10","author":"M Houser","year":"2021","unstructured":"Houser M, et al. Current and future directions of breast MRI. J Clin Med. 2021;10(23):5668.","journal-title":"J Clin Med"},{"issue":"1","key":"4359_CR4","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1109\/TTS.2023.3234203","volume":"4","author":"T Dhar","year":"2023","unstructured":"Dhar T, et al. Challenges of deep learning in medical image analysis\u2014improving explainability and trust. IEEE Trans Technol Soc. 2023;4(1):68\u201375.","journal-title":"IEEE Trans Technol Soc"},{"key":"4359_CR5","doi-asserted-by":"publisher","first-page":"203636132513270","DOI":"10.1177\/20363613251327080","volume":"17","author":"WW Loughborough","year":"2025","unstructured":"Loughborough WW, et al. Comparison of MRI imaging features to differentiate degenerating fibroids from uterine leiomyosarcomas. Rare Tumors. 2025;17:20363613251327080.","journal-title":"Rare Tumors"},{"key":"4359_CR6","doi-asserted-by":"publisher","first-page":"64747","DOI":"10.1109\/ACCESS.2021.3075608","volume":"9","author":"CK Chong","year":"2021","unstructured":"Chong CK, Ho ETW. Synthesis of 3D MRI brain images with shape and texture generative adversarial deep neural networks. IEEE Access. 2021;9:64747\u201360.","journal-title":"IEEE Access"},{"issue":"2","key":"4359_CR7","doi-asserted-by":"publisher","first-page":"1125138","DOI":"10.21037\/qims-24-1543","volume":"15","author":"S Bijari","year":"2025","unstructured":"Bijari S, et al. Development and validation of a robust MRI-based nomogram incorporating radiomics and deep features for preoperative glioma grading: a multi-center study. Quant Imaging Med Surg. 2025;15(2):1125138\u20131121138.","journal-title":"Quant Imaging Med Surg"},{"key":"4359_CR8","unstructured":"Salmanpour MR, et al. Tensor deep versus radiomics features: lung cancer outcome prediction using hybrid machine learning systems. Soc Nucl Med. 2023."},{"key":"4359_CR9","unstructured":"Whybra P, et al. The Image Biomarker Standardization Initiative: Standardized convolutional filters for quantitative radiomics Authors and affiliations. 2023."},{"key":"4359_CR10","unstructured":"Salmanpour MR, et al. ViSERA: Visualized & standardized environment for radiomics analysis. Soc Nucl Med. 2023."},{"key":"4359_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107714","volume":"240","author":"MR Salmanpour","year":"2023","unstructured":"Salmanpour MR, et al. Fusion-based tensor radiomics using reproducible features: application to survival prediction in head and neck cancer. Comput Methods Programs Biomed. 2023;240:107714.","journal-title":"Comput Methods Programs Biomed"},{"issue":"2","key":"4359_CR12","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.231319","volume":"310","author":"P Whybra","year":"2024","unstructured":"Whybra P, et al. The image biomarker standardization initiative: standardized convolutional filters for reproducible radiomics and enhanced clinical insights. Radiology. 2024;310(2):e231319.","journal-title":"Radiology"},{"key":"4359_CR13","doi-asserted-by":"publisher","DOI":"10.21608\/jmals.2024.372332","author":"N Varma","year":"2024","unstructured":"Varma N, et al. Expression of estrogen, progesterone, and human epidermal growth factor receptors in breast cancer in GMERS medical college and hospital Gandhinagar, India. journal of medical and life science. 2024. https:\/\/doi.org\/10.21608\/jmals.2024.372332.","journal-title":"journal of medical and life science"},{"key":"4359_CR14","doi-asserted-by":"crossref","unstructured":"Netshifhire R, Visaya MV. Integrating multiscale mapper features with machine learning techniques to improve breast cancer classification accuracy. In: 2024 IEEE\/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). 2024. IEEE.","DOI":"10.1109\/BDCAT63179.2024.00051"},{"key":"4359_CR15","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3558752","author":"V Ponzi","year":"2025","unstructured":"Ponzi V, Napoli C. Graph Neural Networks: architectures, applications, and future directions. IEEE Access. 2025. https:\/\/doi.org\/10.1109\/ACCESS.2025.3558752.","journal-title":"IEEE Access"},{"key":"4359_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3444042","author":"A Singh","year":"2024","unstructured":"Singh A, et al. Dynamic filter application in graph convolutional networks for enhanced spectral feature analysis and class discrimination in medical imaging. IEEE Access. 2024. https:\/\/doi.org\/10.1109\/ACCESS.2024.3444042.","journal-title":"IEEE Access"},{"issue":"24","key":"4359_CR17","doi-asserted-by":"publisher","first-page":"5858","DOI":"10.3390\/cancers15245858","volume":"15","author":"G Gogoshin","year":"2023","unstructured":"Gogoshin G, Rodin AS. Graph neural networks in cancer and oncology research: emerging and future trends. Cancers. 2023;15(24):5858.","journal-title":"Cancers"},{"issue":"23","key":"4359_CR18","doi-asserted-by":"publisher","DOI":"10.3390\/rs12234003","volume":"12","author":"Y Li","year":"2020","unstructured":"Li Y, et al. Multi-label remote sensing image scene classification by combining a convolutional neural network and a graph neural network. Remote Sens. 2020;12(23):4003.","journal-title":"Remote Sens"},{"issue":"2","key":"4359_CR19","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1002\/mp.12079","volume":"44","author":"MU Dalm\u0131\u015f","year":"2017","unstructured":"Dalm\u0131\u015f MU, et al. Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med Phys. 2017;44(2):533\u201346.","journal-title":"Med Phys"},{"issue":"4","key":"4359_CR20","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1016\/j.diii.2019.02.008","volume":"100","author":"P Herent","year":"2019","unstructured":"Herent P, et al. Detection and characterization of MRI breast lesions using deep learning. Diagn Interv Imaging. 2019;100(4):219\u201325.","journal-title":"Diagn Interv Imaging"},{"issue":"10","key":"4359_CR21","doi-asserted-by":"publisher","first-page":"105006","DOI":"10.1088\/1361-6560\/ab7e7f","volume":"65","author":"X Ma","year":"2020","unstructured":"Ma X, et al. Automated fibroglandular tissue segmentation in breast MRI using generative adversarial networks. Phys Med Biol. 2020;65(10):105006.","journal-title":"Phys Med Biol"},{"key":"4359_CR22","doi-asserted-by":"crossref","unstructured":"Lv T, Pan X, Li L. DCE-MRI based breast intratumor heterogeneity analysis via dual attention deep clustering network and its application in molecular typing. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2020.","DOI":"10.1109\/BIBM49941.2020.9313272"},{"issue":"1","key":"4359_CR23","first-page":"2413706","volume":"2020","author":"H Jiao","year":"2020","unstructured":"Jiao H, et al. Deep convolutional neural networks-based automatic breast segmentation and mass detection in DCE\u2010MRI. Comput Math Methods Med. 2020;2020(1):2413706.","journal-title":"Comput Math Methods Med"},{"key":"4359_CR24","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ymeth.2021.01.004","volume":"192","author":"R Ramirez","year":"2021","unstructured":"Ramirez R, et al. Prediction and interpretation of cancer survival using graph convolution neural networks. Methods. 2021;192:120\u201330.","journal-title":"Methods"},{"key":"4359_CR25","doi-asserted-by":"crossref","unstructured":"Lv T, Pan X. Temporal-spatial graph attention networks for DCE-MRI breast tumor segmentation. In: BMVC. 2021.","DOI":"10.5244\/C.35.62"},{"issue":"11","key":"4359_CR26","doi-asserted-by":"publisher","first-page":"5495","DOI":"10.1109\/JBHI.2023.3311021","volume":"27","author":"M Fan","year":"2023","unstructured":"Fan M, et al. Cross-parametric generative adversarial network-based magnetic resonance image feature synthesis for breast lesion classification. IEEE J Biomed Health Inform. 2023;27(11):5495\u2013505.","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7","key":"4359_CR27","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15071922","volume":"15","author":"S Ghose","year":"2023","unstructured":"Ghose S, et al. Predicting breast cancer events in ductal carcinoma in situ (DCIS) using generative adversarial network augmented deep learning model. Cancers. 2023;15(7):1922.","journal-title":"Cancers"},{"issue":"20","key":"4359_CR28","doi-asserted-by":"publisher","first-page":"18039","DOI":"10.1007\/s00432-023-05464-w","volume":"149","author":"SS Chowa","year":"2023","unstructured":"Chowa SS, et al. Graph neural network-based breast cancer diagnosis using ultrasound images with optimized graph construction integrating the medically significant features. J Cancer Res Clin Oncol. 2023;149(20):18039\u201364.","journal-title":"J Cancer Res Clin Oncol"},{"key":"4359_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107360","volume":"236","author":"P Xuan","year":"2022","unstructured":"Xuan P, et al. Dynamic graph convolutional autoencoder with node-attribute-wise attention for kidney and tumor segmentation from CT volumes. Knowl Based Syst. 2022;236:107360.","journal-title":"Knowl Based Syst"}],"container-title":["Discover Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12672-025-04359-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12672-025-04359-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12672-025-04359-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T14:49:18Z","timestamp":1769784558000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12672-025-04359-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,29]]},"references-count":29,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["4359"],"URL":"https:\/\/doi.org\/10.1007\/s12672-025-04359-1","relation":{},"ISSN":["2730-6011"],"issn-type":[{"value":"2730-6011","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,29]]},"assertion":[{"value":"14 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","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":"194"}}