Breast arterial calcification as a cardiovascular risk biomarker: A multicenter analysis of Indian women
Bibliographic record
Abstract
Background: Breast arterial calcification (BAC) visible on routine screening mammography is emerging as a potential marker for cardiovascular disease (CVD) risk in women. This study pooled data from three institutional cohorts in India to evaluate the association between BAC, angiographically confirmed coronary artery disease (CAD), and survival outcomes. Materials and Methods: We retrospectively analyzed mammograms from three cohorts: a screening population (2020–2021, n = 335), a breast cancer cohort (2013–2017), and a mixed screening/diagnostic group (2019–2023). BAC was graded (0–3) as per the Canadian Society of Breast Imaging criteria. Primary endpoints included obstructive CAD (≥50% stenosis) and all-cause mortality. Multivariable logistic regression and Cox models were adjusted for traditional risk factors (age, diabetes, hypertension, and dyslipidemia). Results: BAC prevalence in the screening cohort was 11.3%. Obstructive CAD was significantly more frequent in BAC-positive women (83.9%) compared to BAC-negative (4.0%), with an adjusted odds ratio of 32.1 (95% confidence interval: 11.4–90.8; P < 0.001). In the breast cancer cohort, BAC-positive women had a significantly lower median survival (28.6 vs. 43.6 months; hazard ratio = 1.92, P = 0.02). These findings were consistent across all cohorts. Conclusions: BAC independently predicts obstructive CAD and poorer survival among Indian women, beyond traditional risk factors. Routine BAC reporting in mammography and integrated cardio-oncology referrals may enhance cost-effective CVD risk stratification in India.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".