Investigating the feasibility of a hand-held photoacoustic imaging probe for margin assessment during breast conserving surgery
Bibliographic record
Abstract
Approximately 19% of breast cancer patients undergoing breast conserving surgery (BCS) must return for a secondary surgery due to incomplete tumour removal. We propose a single sensor, low-frequency hand-held photoacoustic imaging (PAI) probe for detection of residual cancer tissue during BCS within the surgical cavity and on the excised specimen based on lipid content differences. The probe incorporated a single polyvinylidene fluoride acoustic sensor, a 1-to-4 optical fibre bundle and a polycarbonate axicon lens for light delivery. A phantom consisting of nylon strings was imaged to find an optimal scanning geometry and resolution of the probe. The effect of limited angular coverage was evaluated by comparing the PAI results of a phantom mimicking an ex-vivo breast cancer specimen obtained with the hand-held probe and near-full view PAI system. Translation of the probe with 4 mm steps and rotation over 6° steps resulted in lateral and axial resolution of 1.8 mm and 1 mm, respectively. Experiments with the prototype hand-held PAI probe at 930 nm resulted in excellent image contrast exclusively from lipids. Lipid-free gaps mimicking positive margins were clearly visible in the images. Compared to images from the near full-view PAI system, the hand-held PAI probe had a higher signal-to-noise ratio but suffered from more negativity image artefacts. Taken together, the results show that PAI with the hand-held probe has the potential for detection of residual breast cancer tissue during BCS.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".