Optical transillumination spectroscopy to quantify parenchymal tissue density: an indicator for breast cancer risk
Bibliographic record
Abstract
Mammographic screening for early detection of breast cancer has proven valuable in improving breast cancer survival. However, breast cancer incidence is still increasing, and thus preventative oncology needs to receive more attention, with the goal of identifying women with increased risk of developing breast cancer in the future and offering them risk reduction interventions. Mammogram derived parenchymal density pattern has been shown by various authors to provide a high odds ratio for breast cancer. Near-infrared optical transillumination spectroscopy was employed to determine physiological properties of the breast tissue to quantify differences in women with low or high breast cancer risk. Specifically in this study, women who had a recent mammogram underwent examination of their breast tissue by optical transillumination spectroscopy. Areas of adipose and glandular tissues which give rise to mammographic density patterns also have characteristic optical transillumination spectra. Correlation between optical transillumination spectroscopy and mammographic density pattern was established using partial least squares analysis. Results show that predicted tissue density based on optical transillumination spectroscopy correlates with mammographic observed tissue density, with a Spearman Rank correlation coefficient of 0.72. This suggests that optical transillumination spectroscopy may be a promising tool to quantify and monitor changes in breast cancer risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".