{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T23:48:19Z","timestamp":1776728899363,"version":"3.51.2"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:00:00Z","timestamp":1772928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T00:00:00Z","timestamp":1776729600000},"content-version":"vor","delay-in-days":44,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Future Dreams Breast Cancer Charity"},{"name":"National Institute for Health and Care Research (NIHR) Cambridge Biomedical Research Centre","award":["NIHR203312*"],"award-info":[{"award-number":["NIHR203312*"]}]},{"name":"Cancer Research UK early detection program grant","award":["C543\/A26884"],"award-info":[{"award-number":["C543\/A26884"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Thirty percent of interval breast cancers, diagnosed between routine screening mammograms, have a poorer prognosis than screen-detected cancers. Deep learning algorithms can estimate short-term risk from negative mammograms to guide supplemental imaging or screening intervals, but comparative validation on complete national screening data is lacking. We retrospectively evaluated four risk algorithms (Mirai, iCAD, Transpara, and Google) using 112,621 negative mammograms from two UK NHS Breast Screening Programme sites with different mammography systems (Philips, GE) over one screening round (2014\u20132017) with five-year follow-up, including 1225 future cancers. There was a distinct ranking in discriminative ability; overall AUCs ranged 0.65\u20130.72, only one algorithm significantly differed between systems. For interval cancers, AUCs ranged 0.67\u20130.77. Within the highest 4.0% of risk scores, top algorithms identified ~20% of future cancers, including ~27% of interval cancers, doubling at the 14.0% threshold. These differences highlight the need for multi-algorithm prospective trials and potential fine-tuning to improve generalisation across unseen systems.<\/jats:p>","DOI":"10.1038\/s41746-026-02507-7","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T00:35:56Z","timestamp":1772930156000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Performance of breast cancer risk prediction algorithms across mammography systems in the UK screening programme"],"prefix":"10.1038","volume":"9","author":[{"given":"Joshua","family":"Rothwell","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nicholas","family":"Payne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fleur","family":"Kilburn-Toppin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joshua","family":"Kaggie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard","family":"Black","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sarah","family":"Hickman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bahman","family":"Kasmai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Arne","family":"Juette","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fiona","family":"Gilbert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,8]]},"reference":[{"key":"2507_CR1","doi-asserted-by":"publisher","first-page":"2205","DOI":"10.1038\/bjc.2013.177","volume":"108","author":"MG Marmot","year":"2013","unstructured":"Marmot, M. G. et al. The benefits and harms of breast cancer screening: an independent review. Br. J. Cancer 108, 2205\u20132240 (2013).","journal-title":"Br. J. Cancer"},{"key":"2507_CR2","doi-asserted-by":"publisher","first-page":"1326","DOI":"10.3390\/cancers16071326","volume":"16","author":"L Schumann","year":"2024","unstructured":"Schumann, L., Hadwiger, M., Eisemann, N. & Katalinic, A. Lead-time corrected effect on breast cancer survival in germany by mode of detection. Cancers 16, 1326 (2024).","journal-title":"Cancers"},{"key":"2507_CR3","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1038\/bjc.2013.778","volume":"110","author":"A Dibden","year":"2014","unstructured":"Dibden, A. et al. Reduction in interval cancer rates following the introduction of two-view mammography in the UK breast screening programme. Br. J. Cancer 110, 560\u2013564 (2014).","journal-title":"Br. J. Cancer"},{"key":"2507_CR4","doi-asserted-by":"publisher","first-page":"676","DOI":"10.2214\/AJR.14.13904","volume":"205","author":"LM Henderson","year":"2015","unstructured":"Henderson, L. M. et al. Breast Cancer Characteristics Associated With Digital Versus Film-Screen Mammography for Screen-Detected and Interval Cancers. Am. J. Roentgenol. 205, 676\u2013684 (2015).","journal-title":"Am. J. Roentgenol."},{"key":"2507_CR5","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1093\/jnci\/djaa176","volume":"113","author":"K Kerlikowske","year":"2021","unstructured":"Kerlikowske, K. et al. Advanced Breast Cancer Definitions by Staging System Examined in the Breast Cancer Surveillance Consortium. JNCI J. Natl. Cancer Inst. 113, 909\u2013916 (2021).","journal-title":"JNCI J. Natl. Cancer Inst."},{"key":"2507_CR6","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1097\/CEJ.0b013e32833548ed","volume":"19","author":"S T\u00f6rnberg","year":"2010","unstructured":"T\u00f6rnberg, S. et al. A pooled analysis of interval cancer rates in six European countries. Eur. J. Cancer Prev. 19, 87\u201393 (2010).","journal-title":"Eur. J. Cancer Prev."},{"key":"2507_CR7","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1038\/bjc.2011.3","volume":"104","author":"RL Bennett","year":"2011","unstructured":"Bennett, R. L., Sellars, S. J. & Moss, S. M. Interval cancers in the NHS breast cancer screening programme in England, Wales and Northern Ireland. Br. J. Cancer 104, 571\u2013577 (2011).","journal-title":"Br. J. Cancer"},{"key":"2507_CR8","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1038\/s41591-021-01599-w","volume":"28","author":"A Yala","year":"2022","unstructured":"Yala, A. et al. Optimizing risk-based breast cancer screening policies with reinforcement learning. Nat. Med. 28, 136\u2013143 (2022).","journal-title":"Nat. Med."},{"key":"2507_CR9","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1093\/jnci\/djac008","volume":"114","author":"K Kerlikowske","year":"2022","unstructured":"Kerlikowske, K. et al. Cumulative Advanced Breast Cancer Risk Prediction Model Developed in a Screening Mammography Population. JNCI J. Natl. Cancer Inst. 114, 676\u2013685 (2022).","journal-title":"JNCI J. Natl. Cancer Inst."},{"key":"2507_CR10","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.233067","volume":"314","author":"SL Van Winkel","year":"2025","unstructured":"Van Winkel, S. L. et al. Using AI to Select Women with Intermediate Breast Cancer Risk for Breast Screening with MRI. Radiology 314, e233067 (2025).","journal-title":"Radiology"},{"key":"2507_CR11","doi-asserted-by":"publisher","first-page":"1935","DOI":"10.1016\/S0140-6736(25)00582-3","volume":"405","author":"FJ Gilbert","year":"2025","unstructured":"Gilbert, F. J. et al. Comparison of supplemental breast cancer imaging techniques\u2014interim results from the BRAID randomised controlled trial. Lancet 405, 1935\u20131944 (2025).","journal-title":"Lancet"},{"key":"2507_CR12","doi-asserted-by":"publisher","first-page":"5940","DOI":"10.1007\/s00330-021-07686-3","volume":"31","author":"K Lang","year":"2021","unstructured":"Lang, K., Hofvind, S., Rodriguez-Ruiz, A. & Andersson, I. Can artificial intelligence reduce the interval cancer rate in mammography screening? Eur. Radiol. 31, 5940\u20135947 (2021).","journal-title":"Eur. Radiol."},{"key":"2507_CR13","doi-asserted-by":"publisher","first-page":"100798","DOI":"10.1016\/j.lanepe.2023.100798","volume":"37","author":"M Eriksson","year":"2024","unstructured":"Eriksson, M. et al. European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening\u2014a nested case-control study. Lancet Reg. Health - Eur. 37, 100798 (2024).","journal-title":"Lancet Reg. Health - Eur."},{"key":"2507_CR14","doi-asserted-by":"publisher","first-page":"1732","DOI":"10.1200\/JCO.21.01337","volume":"40","author":"A Yala","year":"2022","unstructured":"Yala, A. et al. Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model. J. Clin. Oncol. 40, 1732\u20131740 (2022).","journal-title":"J. Clin. Oncol."},{"key":"2507_CR15","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.250391","volume":"317","author":"JWD Rothwell","year":"2025","unstructured":"Rothwell, J. W. D. et al. Evaluation of a Mammography-based Deep Learning Model for Breast Cancer Risk Prediction in a Triennial Screening Program. Radiology 317, e250391 (2025).","journal-title":"Radiology"},{"key":"2507_CR16","doi-asserted-by":"publisher","first-page":"1343627","DOI":"10.3389\/fonc.2024.1343627","volume":"14","author":"S Hussain","year":"2024","unstructured":"Hussain, S. et al. Breast cancer risk prediction using machine learning: a systematic review. Front. Oncol. 14, 1343627 (2024).","journal-title":"Front. Oncol."},{"key":"2507_CR17","doi-asserted-by":"publisher","unstructured":"Lehman, C. et al. Deep Learning vs Traditional Breast Cancer Risk Models to Support Risk-Based Mammography Screening. J. Natl. Cancer Inst. https:\/\/doi.org\/10.1093\/jnci\/djac142 (2022).","DOI":"10.1093\/jnci\/djac142"},{"key":"2507_CR18","doi-asserted-by":"publisher","first-page":"e2431715","DOI":"10.1001\/jamanetworkopen.2024.31715","volume":"7","author":"H Hill","year":"2024","unstructured":"Hill, H., Roadevin, C., Duffy, S., Mandrik, O. & Brentnall, A. Cost-Effectiveness of AI for Risk-Stratified Breast Cancer Screening. JAMA Netw. Open 7, e2431715 (2024).","journal-title":"JAMA Netw. Open"},{"key":"2507_CR19","doi-asserted-by":"publisher","first-page":"pkae103","DOI":"10.1093\/jncics\/pkae103","volume":"8","author":"JMJ Isautier","year":"2024","unstructured":"Isautier, J. M. J. et al. Clinical guidelines for the management of mammographic density: a systematic review of breast screening guidelines worldwide. JNCI Cancer Spectr. 8, pkae103 (2024).","journal-title":"JNCI Cancer Spectr."},{"key":"2507_CR20","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1056\/NEJMoa1903986","volume":"381","author":"MF Bakker","year":"2019","unstructured":"Bakker, M. F. et al. Supplemental MRI Screening for Women with Extremely Dense Breast Tissue. N. Engl. J. Med. 381, 2091\u20132102 (2019).","journal-title":"N. Engl. J. Med."},{"key":"2507_CR21","doi-asserted-by":"publisher","unstructured":"Salim, M. et al. AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial. Nat. Med. https:\/\/doi.org\/10.1038\/s41591-024-03093-5 (2024).","DOI":"10.1038\/s41591-024-03093-5"},{"key":"2507_CR22","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1007\/s00330-014-3487-0","volume":"25","author":"S Halligan","year":"2015","unstructured":"Halligan, S., Altman, D. G. & Mallett, S. Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: A discussion and proposal for an alternative approach. Eur. Radiol. 25, 932\u2013939 (2015).","journal-title":"Eur. Radiol."},{"key":"2507_CR23","doi-asserted-by":"publisher","first-page":"4803","DOI":"10.3390\/cancers14194803","volume":"14","author":"A Gastounioti","year":"2022","unstructured":"Gastounioti, A. et al. External Validation of a Mammography-Derived AI-Based Risk Model in a U.S. Breast Cancer Screening Cohort of White and Black Women. Cancers 14, 4803 (2022).","journal-title":"Cancers"},{"key":"2507_CR24","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.230227","volume":"308","author":"AD Lauritzen","year":"2023","unstructured":"Lauritzen, A. D. et al. Assessing Breast Cancer Risk by Combining AI for Lesion Detection and Mammographic Texture. Radiology 308, e230227 (2023).","journal-title":"Radiology"},{"key":"2507_CR25","doi-asserted-by":"publisher","first-page":"673","DOI":"10.7326\/M14-1465","volume":"162","author":"K Kerlikowske","year":"2015","unstructured":"Kerlikowske, K. et al. Identifying women with dense breasts at high risk for interval cancer: a cohort study. Ann. Intern. Med. 162, 673\u2013681 (2015).","journal-title":"Ann. Intern. Med."},{"key":"2507_CR26","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.222733","volume":"307","author":"VA Arasu","year":"2023","unstructured":"Arasu, V. A. et al. Comparison of Mammography AI Algorithms with a Clinical Risk Model for 5-year Breast Cancer Risk Prediction: An Observational Study. Radiology 307, e222733 (2023).","journal-title":"Radiology"},{"key":"2507_CR27","unstructured":"NHS England. Protocols for the surveillance of women at higher risk of developing breast cancer. GOV.UK https:\/\/www.gov.uk\/government\/publications\/breast-screening-higher-risk-women-surveillance-protocols\/protocols-for-surveillance-of-women-at-higher-risk-of-developing-breast-cancer (2025)."},{"key":"2507_CR28","unstructured":"iCAD, Inc. ProFound AI Breast Cancer Health Suite. https:\/\/www.icadmed.com\/breast-health\/."},{"key":"2507_CR29","unstructured":"Choosing Transpara. ScreenPoint Medical BV https:\/\/screenpoint-medical.com\/choosing-transpara\/."},{"key":"2507_CR30","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.aba4373","volume":"13","author":"A Yala","year":"2021","unstructured":"Yala, A. et al. Toward robust mammography-based models for breast cancer risk. Sci. Transl. Med. 13, eaba4373 (2021).","journal-title":"Sci. Transl. Med."},{"key":"2507_CR31","unstructured":"Yala, A. yala\/OncoServe_Public. (2025)."},{"key":"2507_CR32","doi-asserted-by":"crossref","unstructured":"McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, (2020).","DOI":"10.1038\/d41586-019-03822-8"},{"key":"2507_CR33","unstructured":"NHS England. NHS Breast screening programme screening standards valid for data collected from 1 April 2021. GOV.UK https:\/\/www.gov.uk\/government\/publications\/breast-screening-consolidated-programme-standards\/nhs-breast-screening-programme-screening-standards-valid-for-data-collected-from-1-april-2021 (2024)."},{"key":"2507_CR34","doi-asserted-by":"publisher","first-page":"208","DOI":"10.2214\/AJR.15.15987","volume":"208","author":"P Grabler","year":"2017","unstructured":"Grabler, P., Sighoko, D., Wang, L., Allgood, K. & Ansell, D. Recall and Cancer Detection Rates for Screening Mammography: Finding the Sweet Spot. Am. J. Roentgenol. 208, 208\u2013213 (2017).","journal-title":"Am. J. Roentgenol."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02507-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02507-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02507-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T23:27:54Z","timestamp":1776727674000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-026-02507-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,8]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["2507"],"URL":"https:\/\/doi.org\/10.1038\/s41746-026-02507-7","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,8]]},"assertion":[{"value":"7 October 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"J.R., N.R.P., S.E.H. and F.J.G. research agreements with Merantix, ScreenPoint Medical, Lunit, iCAD, Google, Therapixel and Volpara. S.E.H. Radiology Artificial Intelligence Trainee Editorial Board member. J.R. and F.KT. were supported by a Future Dreams breast cancer charity grant awarded to F.J.G. F.KT. consulting for Genesis Care. J.D.K. research support from the NIHR Cambridge Biomedical Research Centre and the Wellcome Trust; grants from Cancer Research UK, AstraZeneca, and GE HealthCare. F.J.G., recipient of the Cancer Research UK Early Detection Programme grant; consulting for Alphabet and Kheiron; honoraria for lectures from GE HealthCare; participation on an advisory board for Bayer; past president (2020\u20132022) of the European Society of Breast Imaging; current Clinical Radiology AI Lead Advisor at the Royal College of Radiologists; contrast media for unrelated trial from Bayer. Y.H., R.T.B., B.K. and A.J. have no competing interests to declare.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"330"}}