{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T15:07:00Z","timestamp":1789571220195,"version":"build-2803163510"},"reference-count":33,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Current clinical risk models were developed for longer-term breast cancer risk stratification and therefore have limited utility for predicting short-term (1-year) breast cancer risk. This study evaluates the performance of a mammogram-based artificial intelligence (MB-AI) risk model for predicting breast cancer diagnosis within 1 year in high-risk women.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective case-control study included 340 women who underwent supplemental MRI screening within 1 year following a negative mammogram, of whom 101 (29.7%) developed breast cancer and 239 (70.3%) remained cancer-free during the same interval. Artificial-intelligence\u2013assigned absolute 1-year risk scores, Tyrer\u2013Cuzick (TC) lifetime and 10-year scores, and Gail lifetime and 5-year scores were analyzed. Model discrimination was evaluated using receiver operating characteristic (ROC) curves. Sensitivity, specificity, and predictive values were estimated at optimal cutoffs. Subgroup analyses examined model performance across genetic, menopausal, reproductive, and tumor-based strata.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The MB-AI model demonstrated the highest overall discriminative ability (area under the curve [AUC] = 0.812), with strong performance in extremely dense breasts (AUC = 0.976). By comparison, TC lifetime (AUC = 0.625) and Gail lifetime (AUC = 0.668) models showed modest discrimination, while TC 10-year and Gail 5-year models did not discriminate (AUCs \u22640.49). Subgroup analyses showed consistent performance across clinical and pathological categories. The MB-AI model also showed an AUC comparable to that of previously developed AI models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A mammogram-based AI model demonstrated improved performance compared with traditional clinical risk models for predicting breast cancer diagnosis within 1 year in women undergoing supplemental screening. These findings support that AI-based approaches may reflect both short-term risk and early imaging features of occult disease and suggest further investigation of AI as a complement to existing risk stratification strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jbi\/wbag046","type":"journal-article","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T11:38:10Z","timestamp":1782992290000},"source":"Crossref","is-referenced-by-count":0,"title":["Mammogram-Based Artificial Intelligence Risk Assessment in High-Risk Women Undergoing Supplemental Screening with MRI"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8306-9836","authenticated-orcid":false,"given":"Samuel B","family":"Ogunlade","sequence":"first","affiliation":[{"name":"Division of Interventional Radiology, Department of Radiology , Mayo Clinic, Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lian","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anahita","family":"Tavana","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3115-0090","authenticated-orcid":false,"given":"Santo","family":"Maimone","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin A","family":"Robinson","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaitlin M","family":"Moran","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amie","family":"Leon","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrey P","family":"Morozov","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chidi T","family":"Nwachukwu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Rochester, MN ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haley P","family":"Letter","sequence":"additional","affiliation":[{"name":"Department of Radiology, Mayo Clinic , Jacksonville, FL ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"issue":"2","key":"2026091610513559900_wbag046-B1","doi-asserted-by":"publisher","first-page":"75","DOI":"10.3322\/canjclin.57.2.75","article-title":"American Cancer Society guidelines for breast screening with MRI as an adjunct to mammography","volume":"57","author":"Saslow","year":"2007","journal-title":"CA Cancer J Clin"},{"issue":"9","key":"2026091610513559900_wbag046-B2","doi-asserted-by":"publisher","first-page":"671","DOI":"10.7326\/0003-4819-148-9-200805060-00009","article-title":"Systematic review: Using magnetic resonance imaging to screen women at high risk for breast cancer","volume":"148","author":"Warner","year":"2008","journal-title":"Ann Intern Med"},{"issue":"2","key":"2026091610513559900_wbag046-B3","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1148\/radiol.2442051620","article-title":"The current status of breast MR imaging part I. Choice of technique, image interpretation, diagnostic accuracy, and transfer to clinical practice","volume":"244","author":"Kuhl","year":"2007","journal-title":"Radiology"},{"issue":"31","key":"2026091610513559900_wbag046-B4","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1200\/JCO.2014.56.6232","article-title":"Magnetic resonance imaging improves breast screening sensitivity in BRCA mutation carriers age \u2265 50 years: evidence from an individual patient data meta-analysis","volume":"33","author":"Phi","year":"2015","journal-title":"J Clin Oncol"},{"issue":"3 Pt A","key":"2026091610513559900_wbag046-B5","doi-asserted-by":"publisher","first-page":"408","DOI":"10.1016\/j.jacr.2017.11.034","article-title":"Breast cancer screening in women at higher-than-average risk: recommendations from the ACR","volume":"15","author":"Monticciolo","year":"2018","journal-title":"J Am Coll Radiol"},{"issue":"1","key":"2026091610513559900_wbag046-B6","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1186\/s13058-015-0653-5","article-title":"Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort","volume":"17","author":"Brentnall","year":"2015","journal-title":"Breast Cancer Res"},{"issue":"10","key":"2026091610513559900_wbag046-B7","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1093\/jnci\/djq088","article-title":"Assessing women at high risk of breast cancer: a review of risk assessment models","volume":"102","author":"Amir","year":"2010","journal-title":"J Natl Cancer Inst"},{"issue":"4","key":"2026091610513559900_wbag046-B8","doi-asserted-by":"publisher","first-page":"504","DOI":"10.1016\/S1470-2045(18)30902-1","article-title":"10-year performance of four models of breast cancer risk: a validation study","volume":"20","author":"Terry","year":"2019","journal-title":"Lancet Oncol"},{"issue":"28","key":"2026091610513559900_wbag046-B9","doi-asserted-by":"publisher","first-page":"3137","DOI":"10.1200\/JCO.2015.60.8869","article-title":"Breast density and benign breast disease: risk assessment to identify women at high risk of breast cancer","volume":"33","author":"Tice","year":"2015","journal-title":"J Clin Oncol"},{"issue":"1","key":"2026091610513559900_wbag046-B10","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1148\/radiol.2019182716","article-title":"A deep learning mammography-based model for improved breast cancer risk prediction","volume":"292","author":"Yala","year":"2019","journal-title":"Radiology"},{"issue":"12","key":"2026091610513559900_wbag046-B11","doi-asserted-by":"publisher","first-page":"2041","DOI":"10.3390\/diagnostics13122041","article-title":"Artificial intelligence applications in breast imaging: Current status and future directions","volume":"13","author":"Taylor","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"2026091610513559900_wbag046-B12","author":"Pike"},{"issue":"644","key":"2026091610513559900_wbag046-B13","doi-asserted-by":"publisher","first-page":"eabn3971","DOI":"10.1126\/scitranslmed.abn3971","article-title":"A risk model for digital breast tomosynthesis to predict breast cancer and guide clinical care","volume":"14","author":"Eriksson","year":"2022","journal-title":"Sci Transl Med"},{"key":"2026091610513559900_wbag046-B14","doi-asserted-by":"publisher","first-page":"100798","DOI":"10.1016\/j.lanepe.2023.100798","article-title":"European validation of an image-derived AI-based short-term risk model for individualized breast cancer screening-a nested case-control study","volume":"37","author":"Eriksson","year":"2023","journal-title":"Lancet Reg Health Eur"},{"issue":"11","key":"2026091610513559900_wbag046-B15","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1136\/jmg.40.11.807","article-title":"Evaluation of breast cancer risk assessment packages in the family history evaluation and screening programme","volume":"40","author":"Amir","year":"2003","journal-title":"J Med Genet"},{"issue":"24","key":"2026091610513559900_wbag046-B16","doi-asserted-by":"publisher","first-page":"1879","DOI":"10.1093\/jnci\/81.24.1879","article-title":"Projecting individualized probabilities of developing breast cancer for white females who are being examined annually","volume":"81","author":"Gail","year":"1989","journal-title":"J Natl Cancer Inst"},{"issue":"2","key":"2026091610513559900_wbag046-B17","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/s10549-005-5152-4","article-title":"Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population","volume":"94","author":"Tice","year":"2005","journal-title":"Breast Cancer Res Treat"},{"issue":"1","key":"2026091610513559900_wbag046-B18","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1214\/19-STS729","article-title":"Risk Models for Breast Cancer and Their Validation","volume":"35","author":"Brentnall","year":"2020","journal-title":"Stat Sci"},{"issue":"3","key":"2026091610513559900_wbag046-B19","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1056\/NEJMoa062790","article-title":"Mammographic density and the risk and detection of breast cancer","volume":"356","author":"Boyd","year":"2007","journal-title":"N Engl J Med"},{"issue":"1","key":"2026091610513559900_wbag046-B20","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1186\/s13058-016-0778-1","article-title":"Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status","volume":"18","author":"Malkov","year":"2016","journal-title":"Breast Cancer Res"},{"issue":"9","key":"2026091610513559900_wbag046-B21","doi-asserted-by":"publisher","first-page":"916","DOI":"10.1093\/jnci\/djy222","article-title":"Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists","volume":"111","author":"Rodr\u00edguez-Ruiz","year":"2019","journal-title":"J Natl Cancer Inst"},{"issue":"3","key":"2026091610513559900_wbag046-B22","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1007\/s10549-017-4198-4","article-title":"Breast cancer detection and tumor characteristics in BRCA1 and BRCA2 mutation carriers","volume":"163","author":"Krammer","year":"2017","journal-title":"Breast Cancer Res Treat"},{"issue":"4","key":"2026091610513559900_wbag046-B23","doi-asserted-by":"publisher","first-page":"920","DOI":"10.2214\/AJR.16.16957","article-title":"Association of BRCA Mutation Types, Imaging Features, and Pathologic Findings in Patients With Breast Cancer With BRCA1 and BRCA2 Mutations","volume":"209","author":"Ha","year":"2017","journal-title":"AJR Am J Roentgenol"},{"issue":"5","key":"2026091610513559900_wbag046-B24","doi-asserted-by":"publisher","first-page":"635","DOI":"10.1016\/j.acra.2012.12.016","article-title":"Mammographic parenchymal patterns as an imaging marker of endogenous hormonal exposure: a preliminary study in a high-risk population","volume":"20","author":"Daye","year":"2013","journal-title":"Acad Radiol"},{"issue":"5","key":"2026091610513559900_wbag046-B25","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1093\/oxfordjournals.aje.a113462","article-title":"Evidence for a crossover in breast cancer risk factors","volume":"116","author":"Janerich","year":"1982","journal-title":"Am J Epidemiol"},{"key":"2026091610513559900_wbag046-B26","doi-asserted-by":"publisher","first-page":"17588359251369973","DOI":"10.1177\/17588359251369973","article-title":"Pregnancy and breast cancer in young women: current updates and future directions","volume":"17","author":"Zhang","year":"2025","journal-title":"Ther Adv Med Oncol"},{"issue":"3","key":"2026091610513559900_wbag046-B27","doi-asserted-by":"publisher","first-page":"769","DOI":"10.1002\/ijc.32330","article-title":"Significant inter- and intra-laboratory variation in grading of invasive breast cancer: A nationwide study of 33,043 patients in the Netherlands","volume":"146","author":"van Dooijeweert","year":"2020","journal-title":"Int J Cancer"},{"issue":"1","key":"2026091610513559900_wbag046-B28","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1186\/s13058-016-0705-5","article-title":"The distribution of ductal carcinoma in situ (DCIS) grade in 4232 women and its impact on overdiagnosis in breast cancer screening","volume":"18","author":"van Luijt","year":"2016","journal-title":"Breast Cancer Res"},{"issue":"1","key":"2026091610513559900_wbag046-B29","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s13058-015-0605-0","article-title":"Lobular breast cancer series: imaging","volume":"17","author":"Johnson","year":"2015","journal-title":"Breast Cancer Res"},{"issue":"9","key":"2026091610513559900_wbag046-B30","doi-asserted-by":"publisher","first-page":"e2018179","DOI":"10.1001\/jamanetworkopen.2020.18179","article-title":"Incidence, Characteristics, and Outcomes of Interval Breast Cancers Compared With Screening-Detected Breast Cancers","volume":"3","author":"Niraula","year":"2020","journal-title":"JAMA Netw Open"},{"issue":"7","key":"2026091610513559900_wbag046-B31","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1016\/j.clbc.2017.04.001","article-title":"Interval Breast Cancer Versus Screen-Detected Cancer: Comparison of Clinicopathologic Characteristics in a Single-Center Analysis","volume":"17","author":"Bellio","year":"2017","journal-title":"Clin Breast Cancer"},{"issue":"1","key":"2026091610513559900_wbag046-B32","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1038\/s41523-025-00831-x","article-title":"Predicting short- to long-term breast cancer risk from longitudinal mammographic screening history","volume":"11","author":"Wang","year":"2025","journal-title":"NPJ Breast Cancer"},{"issue":"16","key":"2026091610513559900_wbag046-B33","doi-asserted-by":"publisher","first-page":"1732","DOI":"10.1200\/JCO.21.01337","article-title":"Multi-Institutional Validation of a Mammography-Based Breast Cancer Risk Model","volume":"40","author":"Yala","year":"2022","journal-title":"J Clin Oncol"}],"container-title":["Journal of Breast Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jbi\/advance-article-pdf\/doi\/10.1093\/jbi\/wbag046\/71123965\/wbag046.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jbi\/advance-article-pdf\/doi\/10.1093\/jbi\/wbag046\/71123965\/wbag046.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T14:51:41Z","timestamp":1789570301000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jbi\/advance-article\/doi\/10.1093\/jbi\/wbag046\/8802219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,16]]},"references-count":33,"URL":"https:\/\/doi.org\/10.1093\/jbi\/wbag046","relation":{},"ISSN":["2631-6110","2631-6129"],"issn-type":[{"value":"2631-6110","type":"print"},{"value":"2631-6129","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,16]]},"article-number":"wbag046"}}