Optical breast spectroscopy as a pre-screening tool to identify women who benefit most from mammography
Bibliographic record
Abstract
There are two gaps in the present approach to breast cancer (BC) screening. First, access to mammography is often linked to socio-economic status, either of the individual or the country providing BC screening. Second, the BC incidence rate among women less than 40 years of age, commonly considered having high risk-benefit ratio for mammographic screening, is currently increasing the fastest of all age groups. Hence, both groups commonly access mammographic screening once they become symptomatic and thus are typically diagnosed with late-stage breast cancer, severely impacting long-term survival and often resulting in increased treatment costs. A safe and inexpensive pre-screening technology, which can identify women at risk of harboring early-stage BC or having very high mammographic breast density, and thus being at an elevated risk to develop BC in the future, can personalize a woman’s entry age into mammographic screening thus optimizing all women’s risk-benefit ratio related to their breast cancer screening. The Optical Breast Spectroscopy (OBS) device developed in our group is a portable device which quantifies the optical density of breast tissue employing up to 13 red/NIR wavelengths. Principal components analysis and tissue chromophore quantification allow identification of women with high mammographic density and hence elevated risk when combined with other risk factors such as BMI and menopausal status. Loss of left-right symmetry in the principal component scores or the tissue chromophores shows potential as an indicator of the presence of BC, although larger population studies are needed to validate the metrics. Longitudinal measurements improve the risk prediction.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".