Development of a scanning photoacoustic tomography system for tumor margin assessment in breast conserving surgery
Bibliographic record
Abstract
Photoacoustic tomography (PAT) has excellent sensitivity for hemoglobin and lipids, which make up much of human breast tissue. Our group has focused on intraoperative PAT applied to tissues obtained during breast-conserving surgery (BCS). In BCS, the tumor is excised with a margin of healthy tissue to ensure tumor removal. Margin detection can be difficult and re-excision surgeries are required in 10 to 25% of cases. Our first-generation intraoperative PAT system was capable of 3D imaging specimens up to 11 cm in diameter and several centimeters thick. The system used a semi-circular ring of low frequency transducers, resulting in a 2.5 mm spatial resolution. The current objective is to improve spatial resolution using higher frequency transducers. An array was constructed with 41 circular transducers positioned on two concentric circular rungs with a single point of focus. An optical window at the center allowed illumination. The array was tested with imaging phantoms consisting of written words on a clear plastic bag, 108 µm polyester monofilament arranged as parallel lines with spacing varying from 1 mm to 8 mm, and finally with porcine tissues. The array was positioned above and perpendicular to the imaging area and raster scanned. Signal averaging was implemented, and images were reconstructed with universal back projection. Image analysis demonstrated a 400 μm spatial resolution, but with low penetration depth and low sensitivity. Results suggest the transducers could improve spatial resolution of the first-generation intraoperative PAT system by 6-fold.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".