Development of a high-throughput wide-field imaging robotic system for assessing margin status based on cancer biomarkers derived from Raman spectroscopy with preliminary validation in lumpectomy specimens from breast-conserving surgery
Bibliographic record
Abstract
Breast cancer affects 1 in 8 women, with 60% of Canadian patients opting for breast-conserving surgery. Current pathology assessments can take days, revealing positive margins in 5-57% of cases, necessitating additional surgeries. This project aims to develop an imaging technology to predict the margin status of lumpectomy specimens within 20 minutes in the operating room (OR), assisting surgeons in determining the need for further tissue excision and potentially reducing reoperation rates. Our group previously developed a wide-field imaging system achieving more than 90% accuracy in distinguishing cancerous and normal breast tissue from sliced lumpectomy specimens. This new robotic system integrates a motorized stage and probe holder for 3-directional scanning and imaging of whole lumpectomy specimens. Initial steps involve a proof-of-principle study using biological phantoms that mimic breast cancer tissue characteristics. We will then present the first in-human data acquired in the OR on lumpectomy specimens and compare the predictions with pathological findings.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".