Artificial-Intelligence Powered Identification of High-Risk Breast Cancer Subgroups Using Mammography: A Multicenter Study Integrating Automated Brightest Density Measures with Deep Learning Metrics

Mammography plays a crucial role in breast cancer (BC) risk assessment. Recent breakthroughs show that deep learning (DL) in mammography is expanding from diagnosis to effective risk prediction. Moreover, the brightest mammographic breast density (MBD), termed “cirrocumulus,” signifies an authentic risk. Addressing the challenges in quantifying above recent measures, we present MIDAS: a DL-derived system for multi-level MBD and risk feature score (FS). Using >260,000 multicenter images from South Korea and the US, FS consistently outperforms conventional MBD metrics in risk stratification. Only within the high FS, cirrocumulus further enriches assessment, pinpointing “double-higher” subgroup. Their risk profiles are notable: women in double upper-tertile showed OR=10.20 for Koreans and 5.67 for US, and OR=7.09 for scree-detected cases (US only). We also reveals the “black-box” nature of FS that it predominantly captures complex patterns of higher-intensity MBD. Our research enhances the potential of digital mammography in identifying individuals at elevated BC risks. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The study was supported by the National Research Foundation of Korea(NRF) grant funded by the Ministry of Science and ICT, Korea (No. 2020R1A2C2101041). TLN is supported by a Cancer Council Victoria grant (AF7305). JLH is a Dame Kate Campbell Professorial Fellow at The University of Melbourne. JIK was supported by a grant from Research year of Inje University in 2018. This work is supported by the NHMRC Centre for Research Excellence in Breast Cancer Screening, Early Detection and Mortality Detection (APP2006899). This work was supported by the National Research Foundation of Korea (BK21 Center for Integrative Response to Health Disasters, Graduate School of Public Health, Seoul National University) (NO.419 999 0514025). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: IRB of Seoul National University (IRB No. SNU 23-03-002) gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The Korean data for mammographic images are not available for open access. The EMBED data requires a process for online data access * Abbreviations : BC : breast cancer MBD : mammographic breast density DL : deep learning FS : feature score FFDM : full-field digital mammography DICOM : digital imaging and communications in medicine XAI : explainable artificial intelligence DCIS : ductal carcinoma in situ