{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T20:07:13Z","timestamp":1772136433512,"version":"3.50.1"},"reference-count":31,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T00:00:00Z","timestamp":1759881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-16679-x","type":"journal-article","created":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T11:05:24Z","timestamp":1759921524000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Assessing the risk of recurrence in early-stage breast cancer through H&amp;E stained whole slide images"],"prefix":"10.1038","volume":"15","author":[{"given":"Geongyu","family":"Lee","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joonho","family":"Lee","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tae-Yeong","family":"Kwak","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sun Woo","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Youngmee","family":"Kwon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chungyeul","family":"Kim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hyeyoon","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,8]]},"reference":[{"issue":"6","key":"16679_CR1","first-page":"524","volume":"72","author":"AN Giaquinto","year":"2022","unstructured":"Giaquinto, A. N. et al. Breast cancer statistics, 2022. CA Cancer J. Clin. 72(6), 524\u2013541 (2022).","journal-title":"CA Cancer J. Clin."},{"issue":"1","key":"16679_CR2","first-page":"17","volume":"73","author":"RL Siegel","year":"2023","unstructured":"Siegel, R. L., Miller, K. D., Wagle, N. S. & Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 73(1), 17\u201348 (2023).","journal-title":"CA Cancer J. Clin."},{"issue":"1","key":"16679_CR3","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1038\/s41523-023-00530-5","volume":"9","author":"FM Howard","year":"2023","unstructured":"Howard, F. M. et al. Integration of clinical features and deep learning on pathology for the prediction of breast cancer recurrence assays and risk of recurrence. NPJ Breast Cancer 9(1), 25 (2023).","journal-title":"NPJ Breast Cancer"},{"issue":"10","key":"16679_CR4","doi-asserted-by":"publisher","first-page":"5025","DOI":"10.1109\/JBHI.2022.3187765","volume":"26","author":"M Liu","year":"2022","unstructured":"Liu, M. et al. A deep learning method for breast cancer classification in the pathology images. IEEE J. Biomed. Health Inform. 26(10), 5025\u20135032 (2022).","journal-title":"IEEE J. Biomed. Health Inform."},{"issue":"4","key":"16679_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/bcr2607","volume":"12","author":"EA Rakha","year":"2010","unstructured":"Rakha, E. A. et al. Breast cancer prognostic classification in the molecular era: the role of histological grade. Breast Cancer Res. 12(4), 1\u201312 (2010).","journal-title":"Breast Cancer Res."},{"issue":"27","key":"16679_CR6","doi-asserted-by":"publisher","first-page":"2817","DOI":"10.1056\/NEJMoa041588","volume":"351","author":"S Paik","year":"2004","unstructured":"Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351(27), 2817\u20132826 (2004).","journal-title":"N. Engl. J. Med."},{"issue":"2","key":"16679_CR7","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1056\/NEJMoa1804710","volume":"379","author":"JA Sparano","year":"2018","unstructured":"Sparano, J. A. et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N. Engl. J. Med. 379(2), 111\u2013121 (2018).","journal-title":"N. Engl. J. Med."},{"key":"16679_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13058-020-01296-5","volume":"22","author":"L Yin","year":"2020","unstructured":"Yin, L., Duan, J.-J., Bian, X.-W. & Yu, S.-C. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 22, 1\u201313 (2020).","journal-title":"Breast Cancer Res."},{"issue":"12","key":"16679_CR9","doi-asserted-by":"publisher","first-page":"991","DOI":"10.1038\/s41416-019-0619-y","volume":"121","author":"A Darlix","year":"2019","unstructured":"Darlix, A. et al. Impact of breast cancer molecular subtypes on the incidence, kinetics and prognosis of central nervous system metastases in a large multicentre real-life cohort. Br. J. Cancer 121(12), 991\u20131000 (2019).","journal-title":"Br. J. Cancer"},{"key":"16679_CR10","doi-asserted-by":"crossref","first-page":"7","DOI":"10.21037\/atm.2019.11.30","volume":"8","author":"S Zhao","year":"2020","unstructured":"Zhao, S., Zuo, W.-J., Shao, Z.-M. & Jiang, Y.-Z. Molecular subtypes and precision treatment of triple-negative breast cancer. Ann. Transl. Med. 8, 7 (2020).","journal-title":"Ann. Transl. Med."},{"issue":"1","key":"16679_CR11","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1038\/s41746-021-00469-6","volume":"4","author":"Y Mun","year":"2021","unstructured":"Mun, Y., Paik, I., Shin, S.-J., Kwak, T.-Y. & Chang, H. Yet another automated gleason grading system (yaaggs) by weakly supervised deep learning. Npj Digital Med. 4(1), 99 (2021).","journal-title":"Npj Digital Med."},{"issue":"12","key":"16679_CR12","doi-asserted-by":"publisher","first-page":"1860","DOI":"10.3390\/cancers11121860","volume":"11","author":"HS Ryu","year":"2019","unstructured":"Ryu, H. S. et al. Automated gleason scoring and tumor quantification in prostate core needle biopsy images using deep neural networks and its comparison with pathologist-based assessment. Cancers 11(12), 1860 (2019).","journal-title":"Cancers"},{"issue":"10","key":"16679_CR13","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1038\/s41379-022-01077-9","volume":"35","author":"M Jung","year":"2022","unstructured":"Jung, M. et al. Artificial intelligence system shows performance at the level of uropathologists for the detection and grading of prostate cancer in core needle biopsy: an independent external validation study. Mod. Pathol. 35(10), 1449\u20131457 (2022).","journal-title":"Mod. Pathol."},{"issue":"2","key":"16679_CR14","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1097\/PAP.0000000000000425","volume":"31","author":"S Satturwar","year":"2024","unstructured":"Satturwar, S. & Parwani, A. V. Artificial intelligence-enabled prostate cancer diagnosis and prognosis: current state and future implications. Adv. Anat. Pathol. 31(2), 136\u2013144 (2024).","journal-title":"Adv. Anat. Pathol."},{"key":"16679_CR15","unstructured":"BenTaieb, A., & Hamarneh, G. Deep learning models for digital pathology. arXiv preprint arXiv:1910.12329 (2019)."},{"key":"16679_CR16","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.ejca.2021.10.007","volume":"160","author":"L Schneider","year":"2022","unstructured":"Schneider, L. et al. Integration of deep learning-based image analysis and genomic data in cancer pathology: a systematic review. Eur. J. Cancer 160, 80\u201391 (2022).","journal-title":"Eur. J. Cancer"},{"issue":"5","key":"16679_CR17","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1038\/s41591-021-01343-4","volume":"27","author":"J Laak","year":"2021","unstructured":"Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27(5), 775\u2013784 (2021).","journal-title":"Nat. Med."},{"issue":"7","key":"16679_CR18","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1038\/s42256-020-0200-7","volume":"2","author":"Y Tolkach","year":"2020","unstructured":"Tolkach, Y., Dohmg\u00f6rgen, T., Toma, M. & Kristiansen, G. High-accuracy prostate cancer pathology using deep learning. Nat. Mach. Intell. 2(7), 411\u2013418 (2020).","journal-title":"Nat. Mach. Intell."},{"issue":"5","key":"16679_CR19","first-page":"713","volume":"17","author":"P-S Liao","year":"2001","unstructured":"Liao, P.-S. et al. A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17(5), 713\u2013727 (2001).","journal-title":"J. Inf. Sci. Eng."},{"issue":"1","key":"16679_CR20","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","volume":"9","author":"N Otsu","year":"1979","unstructured":"Otsu, N. A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62\u201366. https:\/\/doi.org\/10.1109\/TSMC.1979.4310076 (1979).","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"16679_CR21","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., & Xie, S. A convnet for the 2020s. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 11976\u201311986 (2022).","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"16679_CR22","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. Imagenet: a large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248\u2013255 (IEEE, 2009).","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"16679_CR23","unstructured":"Moon, J., Kim, J., Shin, Y., & Hwang, S. Confidence-aware learning for deep neural networks. In International Conference on Machine Learning 7034\u20137044 (PMLR, 2020)."},{"key":"16679_CR24","unstructured":"Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning 1597\u20131607 (PMLR, 2020)."},{"key":"16679_CR25","doi-asserted-by":"publisher","first-page":"138403","DOI":"10.1109\/ACCESS.2021.3118694","volume":"9","author":"S Lee","year":"2021","unstructured":"Lee, S., Lee, Y., Lee, G. & Hwang, S. Supervised contrastive embedding for medical image segmentation. IEEE Access 9, 138403\u2013138414 (2021).","journal-title":"IEEE Access"},{"key":"16679_CR26","first-page":"452","volume":"30","author":"Y Geifman","year":"2017","unstructured":"Geifman, Y. & El-Yaniv, R. Selective classification for deep neural networks. Adv. Neural Inf. Process. Syst. 30, 452 (2017).","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"12","key":"16679_CR27","doi-asserted-by":"publisher","first-page":"3868","DOI":"10.1109\/TMI.2020.3006437","volume":"39","author":"A Mehrtash","year":"2020","unstructured":"Mehrtash, A., Wells, W. M., Tempany, C. M., Abolmaesumi, P. & Kapur, T. Confidence calibration and predictive uncertainty estimation for deep medical image segmentation. IEEE Trans. Med. Imaging 39(12), 3868\u20133878 (2020).","journal-title":"IEEE Trans. Med. Imaging"},{"key":"16679_CR28","doi-asserted-by":"publisher","first-page":"596364","DOI":"10.3389\/fonc.2021.596364","volume":"11","author":"J-Y Kim","year":"2021","unstructured":"Kim, J.-Y. et al. Deep learning-based prediction model for breast cancer recurrence using adjuvant breast cancer cohort in tertiary cancer center registry. Front. Oncol. 11, 596364 (2021).","journal-title":"Front. Oncol."},{"key":"16679_CR29","doi-asserted-by":"publisher","first-page":"1007","DOI":"10.1007\/s12282-020-01100-4","volume":"27","author":"A Baltres","year":"2020","unstructured":"Baltres, A. et al. Prediction of oncotype dx recurrence score using deep multi-layer perceptrons in estrogen receptor-positive, her2-negative breast cancer. Breast Cancer 27, 1007\u20131016 (2020).","journal-title":"Breast Cancer"},{"issue":"3","key":"16679_CR30","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s10549-022-06763-5","volume":"196","author":"AD Williams","year":"2022","unstructured":"Williams, A. D. et al. Use of a supervised machine learning model to predict oncotype dx risk category in node-positive patients older than 50 years of age. Breast Cancer Res. Treat. 196(3), 565\u2013570 (2022).","journal-title":"Breast Cancer Res. Treat."},{"key":"16679_CR31","first-page":"1","volume":"2022","author":"KR Pawloski","year":"2022","unstructured":"Pawloski, K. R. et al. Supervised machine learning model to predict oncotype dx risk category in patients over age 50. Breast Cancer Res. Treatment 2022, 1\u20138 (2022).","journal-title":"Breast Cancer Res. Treatment"}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16679-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16679-x","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16679-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T11:05:29Z","timestamp":1759921529000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-16679-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,8]]},"references-count":31,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["16679"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-16679-x","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-6421825\/v1","asserted-by":"object"}]},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,8]]},"assertion":[{"value":"10 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"This study was reviewed and approved by the Institutional Review Board (IRB) of Korea University Medical Center, Seoul, Korea (IRB No. 2020-GRO-146). Informed consent was waived due to its retrospective nature by the IRB of Korea University Medical Center. The study was conducted in accordance with the Declaration of Helsinki.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}}],"article-number":"35069"}}