Lipid-weighted intraoperative photoacoustic tomography of breast tumors: Volumetric comparison to preoperative MRI
Bibliographic record
Abstract
With a lifetime risk of 1 in 8, breast cancer continues to be a major concern for women and their physicians. The optimal treatment of the disease depends on the stage of the cancer at diagnosis, which is typically assessed using medical imaging. However, currently employed imaging systems for breast tumor measurement rarely agree perfectly. Our group developed an Intraoperative Photoacoustic Screening (iPAS) soft tissue scanner featuring high bulk tissue sensitivity, a clinically compatible scan-time of 6 min, imaging depths greater than 2 cm and the capability to visualize whole breast tumors based on their lipid, rather than hemoglobin, profile. Here, we report on the first clinical experience with breast cancer patients by comparing tumor-measurement using iPAS, preoperative dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) and gold-standard pathology. Tumor size was measured volumetrically for iPAS and DCE-MRI, and separately using maximum diameters for pathology, DCE-MRI and iPAS. Comparisons were performed using Pearson's correlation coefficients, and the non-parametric Wilcoxon signed-rank test. Twelve consecutive patients were included in the study, contingent on pathologically documented invasive carcinoma. iPAS volumetric tumor size was positively correlated to DCE-MRI (Pearson's r = 0.78, p = 0.003) and not significantly different (Wilcoxon, p = 0.97). In comparison to pathology, tumor diameters given by iPAS were positively correlated (Pearson's r = 0.87, p = 0.0002) and significantly different (Wilcoxon, p = 0.0015). The results indicated that volumetric-measurement of invasive breast tumors with iPAS is similar to that of DCE-MRI. On the other hand, tumor diameter measurements were less reliable. Beyond enhancing surgical specimen examination, an extension of this technology to diagnostic imaging promises a new perspective on tumor assessment, potentially improving our current understanding and treatment of breast cancer.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".