Optical biomarkers for breast cancer derived from dynamic diffuse optical tomography
Bibliographic record
Abstract
Diffuse optical tomography (DOT) is a noninvasive, nonionizing imaging modality that uses near-infrared light to visualize optically relevant chromophores. A recently developed dynamic DOT imaging system enables the study of hemodynamic effects in the breast during a breath-hold. Dynamic DOT imaging was performed in a total of 21 subjects (age 54±10 years) including 3 healthy subjects and 18 subjects with benign (n=8) and malignant (n=14) masses. Three-dimensional time-series images of the percentage change in oxygenated and deoxygenated hemoglobin concentrations ([HbO2] and [Hb]) from baseline are obtained over the course of a breath-hold. At a time point of 15 s following the end of the breath-hold, [Hb] in healthy breasts has returned to near-baseline values (1.6%±0.5%), while tumor-bearing breasts have increased levels of [Hb] (6.8%±3.6%, p<0.01). Further, healthy subjects have a higher correlation between the breasts over the course of the breath-hold as compared with the subjects with breast cancer (healthy: 0.96±0.02; benign: 0.89±0.02; malignant: 0.78±0.23, p<0.05). Therefore this study shows that dynamic features extracted from DOT measurements can differentiate healthy and diseased breast tissues. These features provide a physiologic method for identifying breast cancer without the need for ionizing radiation.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".