Determination of breast density by bioimpedance.
Bibliographic record
Abstract
56 Background: Mammographic breast density is a significant risk factor for breast cancer. Women with extremely dense breasts are at 4-to-6 times the risk of developing breast cancer than women with primarily fatty breast tissue. Electrical Breast Densitometry (EBD) is a new technique that assesses breast density. EBD is non-ionizing, fast, has low cost per test ($20-$30) and may help in breast cancer risk assessment in the primary care setting. Methods: This study evaluated the feasibility of the EBD in an IRB-approved pilot study of 20 patients. The study used a custom-made self-adhesive electrode (SenoSENSE Medical Systems, Toronto, Canada) interfaced to an off-the-shelf impedance meter (Bodystat 1500, Bodystat, Isle of Man, UK) with a customized cable. On the same day as the subject’s scheduled mammogram, impedance measurements were acquired for each breast. Mammogram densities were scored by a trained radiologist using standard BiRADS breast density categories 1 to 4. Results: A high correlation coefficient was observed (Pearson correlation coefficient >0.80) between breast density determined by the EBD and the BiRADS breast density score. In addition a statistically significant difference was observed between dense categories (BiRADS 3,4) and fatty categories (BiRADS 1,2) (p<0.01), as well as between extremely dense breasts (BiRADS 4) and all other categories (p<0.01). Very high correlation (Pearson correlation coefficient >0.95) was observed between EBD measurements on the left and right breasts. Previous studies have reported a left/right correlation of 0.89 for blinded mammography readers. Conclusions: These results suggests that the EBD measure may be less variable than mammographic estimates of density. The results of the study suggest that Electrical Breast Densitometry is a promising technique for the assessment of breast density and the ability to aid in evaluation of breast cancer risk. It can be reasonably deployed at primary care facilities.
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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 | 0.001 | 0.001 |
| 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) | 0.004 | 0.002 |
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".