Optical tomography as adjunct to x-ray mammography: methods and results
Bibliographic record
Abstract
Recent years have seen significant efforts deployed to apply optical imaging techniques in clinical indications. Optical mammography as an adjunct to X-ray mammography is one such application. 3D optical mammography relies on the sensitivity of near-infrared light to endogenous breast chromophores in order to generate in vivo functional views of the breast. This work presents prospective tissue characterization results from a multi-site clinical study targeting optical tomography as an adjunct to conventional mammography. A 2nd -generation multi-wavelength time-domain acquisition system was used to scan a wide population of women presenting normal or suspicious X-ray mammograms. Application specific algorithms based on a diffusive model of light transport were used to quantify the breast's optical properties and derive 3D images of physiological indices. Using histopathological findings as a gold standard, results confirm that optically derived parameters provide statistically significant discrimination between malignant and benign tissue in wide population of subjects. The methodology developed for case reviews, lesion delineation and characterization allows for better translation of the optical data to the more traditional x-ray paradigm while maintaining efficacy. They also point to the need for guidelines that facilitate correlation of optical data if those results are to be confirmed in a clinical setting.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".