Investigating the feasibility of a hand-held photoacoustic imaging probe for margin assessment during breast conserving surgery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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. Our previous work demonstrated that the lower lipid content, characteristic of tumour tissue, was observed as regions of hypo-intense photoacoustic (PA) contrast. The goal of this work was to evaluate feasibility of a low-frequency, hand-held PA imaging probe for surgical margin assessment based on lipid content differences. Here, we describe (i) the design of a prototype hand-held PA imaging probe, (ii) the effect of limited-bandwidth on image contrast, (iii) accuracy towards hypo-intense contrast detection, (iv) the limited-view characteristics of the single sensor design, and (iv) early imaging results of an ex-vivo breast cancer specimen. The probe incorporates a single polyvinylidene fluoride acoustic sensor, a 1-to-4 optical fibre bundle and a polycarbonate axicon lens for light delivery. Imaging results on phantoms designed to mimic positive margins demonstrated the ability to detect gaps in optical absorption as small as 1 mm in width. Compared to images from a near full-view PAI system, the hand-held PAI probe had higher signal to noise ratio but suffered from negativity image artifacts. Lumpectomy specimen imaging showed that strong signals can be obtained from the fatty tissue. Taken together, the results show this imaging approach with a hand-held probe has potential for detection of residual breast cancer tissue during BCS; however, more work is needed to reduce the size of the probe to fit within the surgical cavity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it