Assessing breast tissue density by transillumination breast spectroscopy (TIBS): an intermediate indicator of cancer risk
Bibliographic record
Abstract
Risk assessment by parenchymal density pattern, a strong physical indicator of future breast cancer risk, is available with the onset of mammographic screening programmes. However, due to the use of ionizing radiation, mammography is not recommended for use in younger women, thereby rendering risk assessment unattainable at an earlier age. Visible and near infrared light was used on 292 women with radiologically normal mammograms to determine whether transillumination breast spectroscopy (TIBS) can identify women with a high parenchymal density pattern as an intermediate indicator of breast cancer risk. Principal component analysis (PCA) was used to reduce the spectral data and generate density scores for each woman. To assess the accuracy of TIBS, logistic regression was used to calculate crude and adjusted odds ratios (OR) and 95% confidence intervals (CI) for each score. Receiver operator characteristic (ROC) curves and area under the curve (AUC) were also calculated for the crude and adjusted logistic models. Optical information relating to tissue chromophores, such as water, lipid and haemoglobin content, was sufficient to identify women with high parenchymal density. The resulting AUC for the final and most parsimonious multivariate logistic model was 0.922 (95% CI 0.878-0.967). TIBS provides information correlating to high parenchymal density and is a promising tool for risk assessment, particularly for younger women.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".