Correlation between cumulative mammographic density and age‐specific incidence of breast cancer: A biethnic study in Israel
Bibliographic record
Abstract
Women with the most extensive breast density, have a 4- to 6-fold higher cancer risk than women with the lowest density. This cross-sectional study evaluated associations of cumulative mammographic density in two distinct ethnic groups with the respective age-specific breast cancer incidences in the population. The study compared four cohorts of 200 women each aged 35 to 49 and 50 to 74, representing Jewish and Arab ethnicity. Breast density measures were calculated from screening mammograms, using a thresholding software (Cumulus). Breast cancer specific incidence values were obtained from the National Cancer Registry. The percent mammographic density was lower for women aged 50 to 74 than 35 to 49 years, both for Jews: 11.7 vs 23.1 and for Arabs: 11.6 vs 18.3. In contrast, the cumulative density increased with age, from 37.30 to 181.24 in Jews, compared to 21.26 to 108.03 in Arabs. Similar trends in breast cancer incidence rates per 100 000 in the Israeli population were apparent, with an increase from 92.95 to 381.91 in Jews, compared to 48.6 to 244.44 in Arabs. Comparing cumulative density of the cohort with respective age-specific breast cancer incidence in the population yielded a highly significant correlation: Jews; r = .97, P < .0001 and Arabs: r = .86, P = .007. A strong association was found between the log of cumulative density and the log of cancer incidence, as well. Our study identified correlations between cumulative mammographic density and breast cancer incidence in two distinct populations. The findings should prompt research to enhance our understanding of the pathogenesis of breast cancer, and lead to novel insights into measures of prevention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".