Photoacoustic imaging of breast microcalcifications: A validation study with 3-dimensional ex vivo data
Bibliographic record
Abstract
Breast microcalcification has been served as an important early-indicator of breast cancer. In the conventional screening procedure for the breast cancer, X-ray mammography is first conducted and the malignancy of the suspicious patient is confirmed by conducting needle biopsy with real-time imaging-assisted guidance, i.e., stereotactic and US imaging. However, these biopsy guidance methods suffer from large amount of radiative exposure and limited sensitivity on microcalcifications without a mass, respectively. In this paper, we verify the capability of photoacoustic imaging (PAI) for detection of the breast microcalcifications by comparing their locations in a 3-D PA image with those in the corresponding X-ray mammography. For this, cross-sectional PA/US images of breast ex vivo specimens were sequentially acquired with 7.2-MHz linear array transducer and the procedure was repeated by moving the transducer along the elevation direction at an increment of 0.3 mm. A Surelite Nd:YAG OPO system (Continuum Inc., USA) was used for the laser excitation at the rate of 10 Hz with a bifurcated optical fiber bundle for laser delivery. PA signals generated in the specimen were captured with SonixTouch research package (Ultrasonix Corp., Canada). With a volume-rendered PA/US image, it is shown that the locations of the microcalcifications in the X-ray mammography meshed well with those in PA images. From the experimental results, therefore, it is demonstrates that PAI can be an effective alternative for noninvasive, real-time biopsy guidance for breast cancer screening.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".