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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".