Quantification of breast arterial calcification using full field digital mammography
Bibliographic record
Abstract
Breast arterial calcification is commonly detected on some mammograms. Previous studies indicate that breast arterial calcification is evidence of general atherosclerotic vascular disease and it may be a useful marker of coronary artery disease. It can potentially be a useful tool for assessment of coronary artery disease in women since mammography is widely used as a screening tool for early detection of breast cancer. However, there are currently no available techniques for quantification of calcium mass using mammography. The purpose of this study was to determine whether it is possible to quantify breast arterial calcium mass using standard digital mammography. An anthropomorphic breast phantom along with a vessel calcification phantom was imaged using a full field digital mammography system. Densitometry was used to quantify calcium mass. A calcium calibration measurement was performed at each phantom thickness and beam energy. The known (K) and measured (M) calcium mass on 5 and 9 cm thickness phantoms were related by M=0.964K -0.288 mg (r=0.997 and SEE=0.878 mg) and M=1.004K+0.324 mg (r=0.994 and SEE = 1.32 mg), respectively. The results indicate that accurate calcium mass measurements can be made without correction for scatter glare as long as careful calcium calibration is made for each breast thickness. The results also indicate that composition variations and differences of approximately 1 cm between calibration phantom and breast thickness introduce only minimal error in calcium measurement. The uncertainty in magnification is expected to cause up to 5% and 15% error in calcium mass for 5 and 9 cm breast thicknesses, respectively. In conclusion, a densitometry technique for quantification of breast arterial calcium mass was validated using standard full field digital mammography. The results demonstrated the feasibility and potential utility of the densitometry technique for accurate quantification of breast arterial calcium mass using standard digital mammography.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".