Volumetric breast density characteristics as determined from digital mammograms
Bibliographic record
Abstract
In this paper we present the results of an automated and entirely reproducible algorithm that estimates the breast volume, dense tissue volume and the volumetric breast density from digital mammograms. The algorithm was applied to 55, 087 digital images (CC view only) from 15 351 individual women, acquired between 2008 and 2011 at the Sunnybrook Health Sciences Centre in Toronto, Canada. The algorithm is based on a prior calibration of the digital image signal versus tissue thickness and composition, and the thickness of the compressed breast is estimated using an empirical model that corrects the thickness readout of the mammography system as a function of compression force. The mean volumetric density and breast volumes for our study group were 30% and 687 cm(3), respectively. The left and right volumetric density and breast volume were strongly correlated, with a Pearson correlation of 0.92 and 0.91, respectively. The volumetric density decreased from 45% to 25% as age increased from 35 to 75 years, with an increase to 30% at 80 years. For a given woman, the volumetric density decreased at an average rate of -2 density percentage points per year while the breast volume increased by 2% per year.
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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 | 0.000 | 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".