Optical assessment of mammographic breast density by a 12‐wavelength vs a continuous‐spectrum optical spectroscopy device
Bibliographic record
Abstract
A 12-laser-wavelength, fixed source-detector position, cup-based optical breast spectroscopy (OBS) device was developed for use in large-scale, multicenter trials as a mammographic breast density (MBD) quantification and breast cancer (BC) risk prescreening tool. In this study, the device was evaluated in comparison with a spectrometer-based device used in previous studies. The devices were compared on their ability to predict mammographic percent density (MPD) and to identify women with high MBD from optical spectra. OBS measurements were made on 60 women, (age 29-73), using both devices. Recent mammograms were collected for all women and MPD quantified from the mammograms. Principal components (PCs) analysis was performed on both sets of OBS spectra, and multivariate logistic regression analysis of the resulting PC scores was used to identify women with high MBD. Both devices are able to identify high MBD with very high sensitivity and specificity. Partial least-squares regression of the spectra was used to predict MPD. Both devices show a strong correlation between OBS-predicted MPD and MPD read from mammograms, however, the correlation is stronger for the continuous-spectrum device (r = 0.74, P = .001) than for the 12-wavelength device (r = 0.62, P = .004). Improvements to the cup-based device to reduce detector saturation should improve the prediction of MPD from the spectra.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".