Translation of a portable diffuse optical breast scanner probe for clinical application: a preliminary study
Bibliographic record
Abstract
Abstract Most breast cancer lesions absorb higher levels of near-infrared (NIR) radiation compared to healthy breast tissue due to its increased vascularity. Oxy-hemoglobin (HbO 2 ) and deoxy-hemoglobin (Hb) primarily found in cancerous vascular lesions, absorbs higher levels of radiation in the 650 nm to 850 nm wavelength range than the surrounding fatty tissue and water in the human breast. NIR diffuse optical spectroscopy (DOS) provides real-time functional and compositional information based on the optical properties of biological tissues, which cannot be accomplished by other portable breast imaging modalities. Here we present the first set of clinical trials using a non-invasive, hand-held diffuse optical breast scanner (DOB-Scan probe 3 ) to capture in vivo cross-sectional images of the breast. The scanner uses four NIR illuminating sources with different wavelengths, 690 nm, 750 nm, 800 nm, and 850 nm, to determine the concentrations of the four main constituents of breast tissue, oxy-hemoglobin (HbO 2 ), deoxy-hemoglobin (Hb), water (H 2 O), and fat. In this paper, we briefly explain the hardware design and image reconstruction algorithm of the DOB-Scan probe, the data collection process, and the imaging results of four different participants, selected from twenty, all who are diagnosed with breast cancer. For each patient, images were scanned from two locations, the first over the cancerous lesion and the second over the same region on the contralateral healthy breast, as a means of establishing controls for comparison. During each scan, four cross-sectional images of the breast, corresponding to four different NIR wavelengths, are reconstructed and displayed on a user interface for reference. Clinical results confirm that the absorption coefficients of cancerous lesions are significantly higher than the normal surrounding tissue. We propose to deploy the probe to effectively identify cancerous breast tissue at an early stage in a primary care setting, which could increase the efficiency of screening programs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".