{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T14:09:13Z","timestamp":1789999753436,"version":"4.0.1"},"reference-count":49,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Breast density (BD) and background parenchymal enhancement (BPE) have long been considered related descriptors of breast tissue composition, yet growing evidence shows that they represent distinct and non-interchangeable physiological domains. Objective: This study aims to evaluate whether BD and BPE represent independent imaging biomarkers with distinct systemic associations, and to investigate their potential role in preventive medicine beyond breast cancer detection. Methods: Drawing on linear modeling, neural analyses, inverse prediction, and correlation studies conducted across five coordinated investigations, we evaluated the systemic associations of BD and BPE. We also performed model ablation analyses to confirm biomarker independence and assessed the impact of AI support on inter-reader agreement and workflow efficiency. Results: BD is strongly associated with bone mineral density, supporting its role as a structural marker linked to osteoporotic risk. Conversely, BPE correlates with systolic blood pressure (SBP), reflecting vascular tone and perfusion. Importantly, the systemic information carried by BD can be extracted not only from contrast enhanced mammography (CEM) but also from standard mammography. When CEM is available, BPE adds complementary insight into vascular physiology. Furthermore, AI support improved inter-reader agreement and workflow efficiency. Conclusions: These findings support a dual biomarker framework in which BD and BPE independently contribute to systemic risk stratification. This approach suggests potential for opportunistic osteoporosis risk assessment from routine screening examinations, though prospective validation is required before clinical implementation. This framework may shift breast imaging toward a broader, sustainable, and human-centered approach to preventive health.<\/jats:p>","DOI":"10.3390\/diagnostics16183020","type":"journal-article","created":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T12:50:03Z","timestamp":1789995003000},"page":"3020","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoupling Structural and Vascular Biomarkers in Contrast-Enhanced Mammography: A Methodological Synthesis and Perspective"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4507-3505","authenticated-orcid":false,"given":"Graziella","family":"Di Grezia","sequence":"first","affiliation":[{"name":"Department of Life Sciences, Health and Healthcare Professions, Link Campus University, Via del Casale di S. Pio V, 44, 00165 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1463-8940","authenticated-orcid":false,"given":"Antonio","family":"Nazzaro","sequence":"additional","affiliation":[{"name":"Independent Researcher, 83100 Avellino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"}]},{"given":"Teresa","family":"Iannaccone","sequence":"additional","affiliation":[{"name":"Independent Researcher, 83100 Avellino, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-5978-7862","authenticated-orcid":false,"given":"Liala Mirella","family":"Fattacciu","sequence":"additional","affiliation":[{"name":"Radiology Department of Surgery, Medicine and Pharmacy, University of Sassari, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"}]},{"given":"Salvatore","family":"Masala","sequence":"additional","affiliation":[{"name":"Radiology Department of Surgery, Medicine and Pharmacy, University of Sassari, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0910-8064","authenticated-orcid":false,"given":"Mariano","family":"Scaglione","sequence":"additional","affiliation":[{"name":"Radiology Department of Surgery, Medicine and Pharmacy, University of Sassari, 07100 Sassari, Italy"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1148\/radiol.2021201948","article-title":"Contrast-enhanced mammography: State of the art","volume":"299","author":"Jochelson","year":"2021","journal-title":"Radiology"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110513","DOI":"10.1016\/j.ejrad.2022.110513","article-title":"Contrast-enhanced mammography in breast cancer screening","volume":"156","author":"Coffey","year":"2022","journal-title":"Eur. J. Radiol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"340","DOI":"10.2214\/AJR.19.22353","article-title":"Factors associated with background parenchymal enhancement on contrast-enhanced mammography","volume":"216","author":"Karimi","year":"2021","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"45","DOI":"10.2214\/AJR.22.28769","article-title":"Association of clinical factors and degree of early background parenchymal enhancement on contrast-enhanced mammography","volume":"221","author":"Wang","year":"2023","journal-title":"AJR Am. J. Roentgenol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"983","DOI":"10.1148\/rg.2018180035","article-title":"Hormonal effects on breast density, fibroglandular tissue, and background parenchymal enhancement","volume":"38","author":"Heller","year":"2018","journal-title":"Radiographics"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8608","DOI":"10.1038\/s41598-020-65526-8","article-title":"Background parenchymal enhancement on contrast-enhanced spectral mammography: Influence of age, breast density, menstruation status, and menstrual cycle timing","volume":"10","author":"Zhao","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Nicosia, L., Mariano, L., Mallardi, C., Sorce, A., Frassoni, S., Bagnardi, V., Gialain, C., Pesapane, F., Sangalli, C., and Cassano, E. (2025). Influence of breast density and menopausal status on background parenchymal enhancement in contrast-enhanced mammography: Insights from a retrospective analysis. Cancers, 17.","DOI":"10.3390\/cancers17010011"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.breast.2022.09.007","article-title":"Mammographic breast density and the risk of breast cancer: A systematic review and meta-analysis","volume":"66","author":"Bodewes","year":"2022","journal-title":"Breast"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.mcna.2022.10.007","article-title":"Breast cancer: Risk assessment, screening, and primary prevention","volume":"107","author":"Michaels","year":"2023","journal-title":"Med. Clin. N. Am."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1080\/13697137.2018.1424816","article-title":"Role of breast density measurement in screening for breast cancer","volume":"21","author":"McLean","year":"2018","journal-title":"Climacteric"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2768","DOI":"10.1002\/cncr.34252","article-title":"Bone mineral density and risk of breast cancer: A cohort study and Mendelian randomization analysis","volume":"128","author":"Zhang","year":"2022","journal-title":"Cancer"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"e250059","DOI":"10.1148\/radiol.250059","article-title":"Understanding background parenchymal enhancement at contrast-enhanced mammography","volume":"314","author":"Slanetz","year":"2025","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"e241158","DOI":"10.1148\/radiol.241158","article-title":"Hormonal regulation of background parenchymal enhancement at contrast-enhanced mammography","volume":"314","author":"Nissan","year":"2025","journal-title":"Radiology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1234","DOI":"10.1016\/j.acra.2019.10.034","article-title":"Background parenchymal enhancement at contrast-enhanced spectral mammography (CESM) as a breast cancer risk factor","volume":"27","author":"Sorin","year":"2020","journal-title":"Acad. Radiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"111903","DOI":"10.1016\/j.ejrad.2024.111903","article-title":"Background parenchymal enhancement in CEM and MRI: Is there always a high agreement?","volume":"183","author":"Ferrara","year":"2024","journal-title":"Eur. J. Radiol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1016\/j.jacr.2022.02.025","article-title":"Breast density knowledge in a screening mammography population exposed to density notification","volume":"19","author":"Smith","year":"2022","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Di Grezia, G., Nazzaro, A., Schiavone, L., Cisternino, E., Galiano, A., Gatta, G., Cuccurullo, V., and Scaglione, M. (2025). From variability to standardization: The impact of breast density on background parenchymal enhancement in contrast-enhanced mammography and the need for a structured reporting system. Cancers, 17.","DOI":"10.1101\/2025.04.15.25325851"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Di Grezia, G., Nazzaro, A., Cisternino, E., Galiano, A., Marinelli, L., Mercogliano, S., Cuccurullo, V., and Gatta, G. (2025). Beyond cancer detection: An AI framework for multidimensional risk profiling on contrast-enhanced mammography. Diagnostics, 15.","DOI":"10.3390\/diagnostics15212788"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Di Grezia, G., Iannaccone, T., and Nazzaro, A. (2026). Bifurcated networks for breast density & cancer risk: A technical framework. Diagnostics, 16.","DOI":"10.3390\/diagnostics16050770"},{"key":"ref_20","first-page":"e15734056444815","article-title":"AI-assisted decision-support framework for breast density and background parenchymal enhancement assessment in contrast-enhanced mammography","volume":"22","author":"Nazzaro","year":"2026","journal-title":"Curr. Med. Imaging"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1007\/s11547-024-01860-5","article-title":"Background parenchymal enhancement on contrast-enhanced mammography: Associations with breast density and patient\u2019s characteristics","volume":"129","author":"Magni","year":"2024","journal-title":"Radiol. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1002\/jmri.29058","article-title":"Automated breast density assessment in MRI using deep learning and radiomics: Strategies for reducing inter-observer variability","volume":"60","author":"Jing","year":"2024","journal-title":"J. Magn. Reson. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mariotti, F., Cacioppa, L.M., Rossini, N., Bruno, A., Francavilla, G., Felicioli, A., Macchini, M., Coppola, A., Cellina, M., and Floridi, C. (2026). Human-AI interaction in interventional radiology: A narrative review of current applications, challenges, and future directions. J. Imaging, 12.","DOI":"10.3390\/jimaging12060274"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.clbc.2025.07.009","article-title":"Exploring imaging biomarkers to improve equity in supplemental and high-risk breast cancer screening between Black and White women: A perspective on background parenchymal enhancement and breast density","volume":"26","author":"Mahmoud","year":"2026","journal-title":"Clin. Breast Cancer"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e2431715","DOI":"10.1001\/jamanetworkopen.2024.31715","article-title":"Cost-effectiveness of AI for risk-stratified breast cancer screening","volume":"7","author":"Hill","year":"2024","journal-title":"JAMA Netw. Open"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"5586","DOI":"10.1109\/TKDE.2021.3070203","article-title":"A survey on multi-task learning","volume":"34","author":"Zhang","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_27","unstructured":"Ruder, S. (2017). An overview of multi-task learning in deep neural networks. arXiv."},{"key":"ref_28","first-page":"3614","article-title":"Multi-task learning for dense prediction tasks: A survey","volume":"44","author":"Vandenhende","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1007\/s10147-024-02602-3","article-title":"Research on biomarkers using innovative artificial intelligence systems in breast cancer","volume":"29","author":"Kurozumi","year":"2024","journal-title":"Int. J. Clin. Oncol."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"662","DOI":"10.1016\/j.jacr.2019.11.019","article-title":"Human-centered design thinking in radiology","volume":"17","author":"Vagal","year":"2020","journal-title":"J. Am. Coll. Radiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e175","DOI":"10.1016\/S2589-7500(24)00267-X","article-title":"Screening performance and characteristics of breast cancer detected in the Mammography Screening with Artificial Intelligence trial (MASAI): A randomised, controlled, parallel-group, non-inferiority, single-blinded, screening accuracy study","volume":"7","author":"Josefsson","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_32","unstructured":"Lekadir, K., Osuala, R., Gallin, C., Lazrak, N., Kushibar, K., Tsakou, G., Auss\u00f3, S., Alberich, L.C., Marias, K., and Tsiknakis, M. (2021). FUTURE-AI: Guiding principles and consensus recommendations for trustworthy AI in medical imaging. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","article-title":"Artificial intelligence for multimodal data integration in oncology","volume":"40","author":"Lipkova","year":"2022","journal-title":"Cancer Cell"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"102132","DOI":"10.1016\/j.xcrm.2025.102132","article-title":"AI-powered integration of multimodal imaging in precision medicine for neuropsychiatric disorders","volume":"6","author":"Huang","year":"2025","journal-title":"Cell Rep. Med."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mota, A.M. (2025). AI-based characterization of breast cancer in mammography and tomosynthesis: A review of radiomics and deep learning for subtyping, staging, and prognosis. Cancers, 17.","DOI":"10.3390\/cancers17203387"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"100864","DOI":"10.1016\/j.landig.2025.02.004","article-title":"Medical digital twins: Enabling precision medicine and medical artificial intelligence","volume":"7","author":"Testa","year":"2025","journal-title":"Lancet Digit. Health"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"20220890","DOI":"10.1259\/bjr.20220890","article-title":"Federated learning for medical imaging radiology","volume":"96","author":"Rehman","year":"2023","journal-title":"Br. J. Radiol."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Di Grezia, G., Iannaccone, T., Nazzaro, A., Piscopo, L., Masala, S.A., and Scaglione, M. (2026). Artificial intelligence in contrast-enhanced mammography: Current evidence, clinical integration, and future perspectives\u2014A narrative review. Eurasian J. Med. Oncol., online first.","DOI":"10.36922\/EJMO026250294"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Zoccali, C., Stel, V.S., Mallamaci, F., Tripepi, G., Kanbay, M., Jadoul, M., and Jager, K.J. (2026). Cardiovascular risk assessment tools in CKD. Clin. J. Am. Soc. Nephrol., online ahead of print.","DOI":"10.2215\/CJN.0000001025"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e70174","DOI":"10.1002\/cncr.70174","article-title":"Racial differences in quantitative background parenchymal enhancement on breast magnetic resonance imaging","volume":"131","author":"Mahmoud","year":"2025","journal-title":"Cancer"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"e230034","DOI":"10.1148\/rg.230034","article-title":"Triple-negative breast cancer: Histopathologic features, genomics, and treatment","volume":"43","author":"Adrada","year":"2023","journal-title":"Radiographics"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4514","DOI":"10.1158\/0008-5472.CAN-03-3853","article-title":"Integrin alpha5beta1 promotes survival of growth-arrested breast cancer cells: An in vitro paradigm for breast cancer dormancy in bone marrow","volume":"64","author":"Korah","year":"2004","journal-title":"Cancer Res."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1032","DOI":"10.4065\/83.9.1032","article-title":"Bisphosphonates: Mechanism of action and role in clinical practice","volume":"83","author":"Drake","year":"2008","journal-title":"Mayo Clin. Proc."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/s10549-013-2431-3","article-title":"Bone mineral density and risk of breast cancer in postmenopausal women","volume":"138","author":"Qu","year":"2013","journal-title":"Breast Cancer Res. Treat."},{"key":"ref_45","first-page":"26","article-title":"Opportunistic osteoporosis screening on FDG PET\/CT scans in breast carcinoma: A comparison with DXA","volume":"14","author":"Gupta","year":"2026","journal-title":"Asia Ocean. J. Nucl. Med. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1016\/j.bone.2016.02.011","article-title":"Digital X-ray radiogrammetry in the study of osteoporotic fractures: Comparison to dual energy X-ray absorptiometry and FRAX","volume":"86","author":"Lui","year":"2016","journal-title":"Bone"},{"key":"ref_47","first-page":"17455057261459264","article-title":"Efficacy and safety of estrogen therapy for prevention of osteoporosis in postmenopausal women","volume":"22","author":"Stanczyk","year":"2026","journal-title":"Womens Health"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Liu, C., Yang, B., Zhang, L., Liu, C., and Zheng, W. (2025). Breast Ultrasound Image Detection Based on Dual-Branch Faster R-CNN. Electronics, 14.","DOI":"10.3390\/electronics14214247"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"e75937","DOI":"10.1002\/adfm.75937","article-title":"Multifunctional Responsive Nanoplatform as Immunotherapeutic Regulator to Target Triple-Negative Breast Cancers via Mechanotransduction","volume":"36","author":"Li","year":"2026","journal-title":"Adv. Funct. Mater."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/3020\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T13:18:42Z","timestamp":1789996722000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/16\/18\/3020"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,17]]},"references-count":49,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["diagnostics16183020"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics16183020","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,17]]}}}