{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T13:44:24Z","timestamp":1782999864533,"version":"3.54.5"},"reference-count":58,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T00:00:00Z","timestamp":1776988800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100006364","name":"Institut National Du Cancer","doi-asserted-by":"publisher","award":["INCa_16049"],"award-info":[{"award-number":["INCa_16049"]}],"id":[{"id":"10.13039\/501100006364","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Biometrical J"],"published-print":{"date-parts":[[2026,6]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>High mammographic density is a well\u2010known risk factor for breast cancer and reduces the sensitivity of mammography\u2010based screening. While automated machine and deep learning\u2010based methods provide more consistent and precise measurements compared to subjective Breast Imaging Reporting and Data System (BI\u2010RADS) assessments, they often fail to account for the longitudinal evolution of density. Many of these methods assess mammographic density in a cross\u2010sectional manner, overlooking correlations in repeated measures, irregular visit intervals, missing data, and informative dropouts. Joint models address these limitations by simultaneously modeling the relationship between longitudinal biomarkers and time\u2010to\u2010event outcomes. We introduce the DeepJoint algorithm, an open\u2010source method combining deep learning\u2010based mammographic density estimation with joint modeling to assess its longitudinal relationship with breast cancer risk. Our approach adequately analyzes processed mammograms from various manufacturers, estimating both dense area and percent density, two established risk factors for breast cancer. We utilize a joint model to explore their association with breast cancer risk and provide individualized risk predictions. Bayesian inference and the consensus Monte Carlo algorithm make the approach reliable for large screening datasets. By integrating deep learning with joint modeling, our new method provides a robust, comprehensive framework for evaluating breast cancer risk based on longitudinal density profiles. The complete pipeline is publicly available, promoting broader application and comparison with other\u00a0methods.<\/jats:p>","DOI":"10.1002\/bimj.70135","type":"journal-article","created":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T05:41:52Z","timestamp":1777095712000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["The DeepJoint Algorithm: An Innovative Approach for Studying the Longitudinal Evolution of Quantitative Mammographic Density and Its Association With Screen\u2010Detected Breast Cancer Risk"],"prefix":"10.1002","volume":"68","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1516-7893","authenticated-orcid":false,"given":"Manel","family":"Rakez","sequence":"first","affiliation":[{"name":"BIOSTAT Team Bordeaux Population Health, U1219, ISPED Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julien","family":"Guillaumin","sequence":"additional","affiliation":[{"name":"Therapixel Nice France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aurelien","family":"Chick","sequence":"additional","affiliation":[{"name":"Therapixel Nice France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaelle","family":"Coureau","sequence":"additional","affiliation":[{"name":"EPICENE Team Bordeaux Population Health, U1219, ISPED Bordeaux France"},{"name":"Department of Public Health Bordeaux University Hospital Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Foucauld","family":"Chamming's","sequence":"additional","affiliation":[{"name":"Department of Radiology Institut Bergoni\u00e9, Comprehensive Cancer Centre Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pierre","family":"Fillard","sequence":"additional","affiliation":[{"name":"Therapixel Nice France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brice","family":"Amadeo","sequence":"additional","affiliation":[{"name":"EPICENE Team Bordeaux Population Health, U1219, ISPED Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Virginie","family":"Rondeau","sequence":"additional","affiliation":[{"name":"BIOSTAT Team Bordeaux Population Health, U1219, ISPED Bordeaux France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,4,24]]},"reference":[{"key":"e_1_2_10_2_1","unstructured":"Afonso P. M. D.Rizopoulos A. K.Palipana et\u00a0al.2023. \u201cEfficiently Analyzing Large Patient Registries With Bayesian Joint Models for Longitudinal and Time\u2010to\u2010Event Data.\u201d Preprint arXiv October 5.https:\/\/doi.org\/10.48550\/arXiv.2310.03351."},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.7065"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1093\/aje\/kwt260"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.semnuclmed.2022.02.003"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12232"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13058-018-0941-y"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa062790"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002335"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/39\/10\/008"},{"key":"e_1_2_10_11_1","unstructured":"Calster B. V. G. S.Collins A. J.Vickers et\u00a0al.2024. \u201cPerformance Evaluation of Predictive AI Models to Support Medical Decisions: Overview and Guidance.\u201d Preprint arXiv December 13.https:\/\/doi.org\/10.48550\/arXiv.2412.10288."},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-023-10561-w"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2016161286"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108919"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019190872"},{"key":"e_1_2_10_16_1","unstructured":"DICOM Standard Browser. \u201cVOI LUT Function Attribute.\u201dhttps:\/\/dicom.innolitics.com\/ciods\/digital\u2010mammography\u2010x\u2010ray\u2010image\/voi\u2010lut\/00281056."},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-018-4690-5"},{"key":"e_1_2_10_18_1","unstructured":"Falcon W. andThe PyTorch Lightning Team.2019. \u201cPyTorch Lightning.\u201dhttps:\/\/doi.org\/10.5281\/zenodo.3828935."},{"key":"e_1_2_10_19_1","unstructured":"Gerds T. A. J. S.Ohlendorff P.Blanche et\u00a0al.2025. \u201criskRegression: Risk Regression Models and Prediction Scores for Survival Analysis With Competing Risks.\u201dhttps:\/\/cran.r\u2010project.org\/web\/packages\/riskRegression\/index.html."},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djq414"},{"key":"e_1_2_10_21_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-16141-2"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-05-0824"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacr.2018.05.004"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102138"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-70538-3_5"},{"key":"e_1_2_10_26_1","unstructured":"He F. T.Liu andD.Tao.2019. \u201cWhy ResNet Works? Residuals Generalize.\u201d Preprint arXiv April 2.https:\/\/doi.org\/10.48550\/arXiv.1904.01367."},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1002\/bimj.201700067"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13058-023-01667-8"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.220047"},{"key":"e_1_2_10_30_1","article-title":"Longitudinal Analysis of Change in Mammographic Density in Each Breast and Its Association With Breast Cancer Risk","author":"Jiang S.","year":"2023","journal-title":"JAMA Oncology"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.13611"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4736530"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-017-3871-7"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1111\/biom.12490"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12763"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2018180694"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10549-023-06966-4"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020201150"},{"key":"e_1_2_10_39_1","unstructured":"Muthukrishnan R. A.Heyler K.Katti et\u00a0al.2023. \u201cMammoFL: Mammographic Breast Density Estimation Using Federated Learning.\u201d Preprint arXiv December 14.https:\/\/doi.org\/10.48550\/arXiv.2206.05575."},{"key":"e_1_2_10_40_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2015.12783"},{"key":"e_1_2_10_41_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa052911"},{"key":"e_1_2_10_42_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2021.11.034"},{"key":"e_1_2_10_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-014-0390-3"},{"key":"e_1_2_10_44_1","unstructured":"Reinke A. M. D.Tizabi C. H.Sudre et\u00a0al.2023. \u201cCommon Limitations of Image Processing Metrics: A Picture Story.\u201d Preprint arXiv December 6.https:\/\/doi.org\/10.48550\/arXiv.2104.05642."},{"key":"e_1_2_10_45_1","unstructured":"Research and Markets.2023. \u201cUnited States Mammography and Breast Imaging Market Outlook Report 2022\u20102025 With Hologic GE HealthCare and Siemens Healthineers Dominating.\u201dhttps:\/\/www.globenewswire.com\/news\u2010release\/2023\/04\/05\/2641404\/28124\/en\/United\u2010States\u2010Mammography\u2010and\u2010Breast\u2010Imaging\u2010Market\u2010Outlook\u2010Report\u20102022\u20102025\u2010with\u2010Hologic\u2010GE\u2010HealthCare\u2010and\u2010Siemens\u2010Healthineers\u2010Dominating.html."},{"key":"e_1_2_10_46_1","doi-asserted-by":"publisher","DOI":"10.1201\/b12208"},{"key":"e_1_2_10_47_1","unstructured":"Rizopoulos D. P. M.Afonso andG.Papageorgiou.2025. \u201cJMbayes2: Extended Joint Models for Longitudinal and Time\u2010to\u2010Event Data.\u201dhttps:\/\/cran.r\u2010project.org\/web\/packages\/JMbayes2\/index.html."},{"key":"e_1_2_10_48_1","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v047.i04"},{"key":"e_1_2_10_49_1","doi-asserted-by":"crossref","unstructured":"Ronneberger O. P.Fischer andT.Brox.2015. \u201cU\u2010Net: Convolutional Networks for Biomedical Image Segmentation.\u201d Preprint arXiv May 18.https:\/\/doi.org\/10.48550\/arXiv.1505.04597.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_2_10_50_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12885-022-09484-6"},{"key":"e_1_2_10_51_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2021200097"},{"key":"e_1_2_10_52_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diii.2017.01.001"},{"key":"e_1_2_10_53_1","doi-asserted-by":"publisher","DOI":"10.1111\/rssb.12062"},{"key":"e_1_2_10_54_1","unstructured":"Therapixel.2023.Artificial Intelligence for Medical Imaging.Therapixel."},{"key":"e_1_2_10_55_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.220291"},{"key":"e_1_2_10_56_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-019-1466-7"},{"key":"e_1_2_10_57_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.8779"},{"key":"e_1_2_10_58_1","doi-asserted-by":"publisher","DOI":"10.1093\/jbi\/wbac047"},{"key":"e_1_2_10_59_1","unstructured":"Zivot E.Forthcoming. \u201cEstimating Functions of GWN Model Parameters.\u201d InIntroduction to Computational Finance and Financial Econometrics With R Section 8.6.CRC Press."}],"container-title":["Biometrical Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/bimj.70135","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/bimj.70135","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/bimj.70135","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T13:00:32Z","timestamp":1782997232000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/bimj.70135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,24]]},"references-count":58,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["10.1002\/bimj.70135"],"URL":"https:\/\/doi.org\/10.1002\/bimj.70135","archive":["Portico"],"relation":{},"ISSN":["0323-3847","1521-4036"],"issn-type":[{"value":"0323-3847","type":"print"},{"value":"1521-4036","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,24]]},"assertion":[{"value":"2024-11-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-01-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-04-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70135"}}