Non-ionizing near-infrared radiation transillumination spectroscopy for breast tissue density and assessment of breast cancer risk
Bibliographic record
Abstract
There is increasing attention to prevention as a means to reduce cancer incidence. Prevention interventions or therapies in turn rely on risk assessment programs to identify those women most likely to benefit from education and lifestyle changes. These programs are usually based either on interviews to identify ethnic, genetic, and lifestyle factors contributing to risk or on physical examination of the breast. For the latter it has been shown that the parenchymal density pattern observed in X-ray mammography can be used to assess an individual's risk. Extensive areas of dense, glandular tissue that are relatively radio-opaque are associated with higher breast cancer risk, with an odds ratio of 4 to 6 compared with women in whom the breast density is low owing to an abundance of adipose tissue. Near-infrared optical transillumination spectroscopy has been used previously to investigate the physiological properties of breast tissue. In this study, women were recruited who underwent recently X-ray mammography. The tissue density was assessed by a radiologist. The women then underwent optical transillumination spectroscopy, for which an instrument was developed that delivered visible and near-infrared light to the breast. After being transmitted through the breast craniocaudally in one of four quadrants, the spectrum from 625 to 1050 nm was measured. The spectra were used as input to a Principal Component Analysis (PCA) that used the corresponding mammographic density as the reference standard. The study group consisted of 92 women aged 39 to 72 years. Without further stratification for age, menopausal status, or measurement position, the PCA numerical model predicted the radiological assessment of tissue density in the mid 80% to low 90%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".