Cautery trajectory analysis for evaluation of resection margins in breast-conserving surgery
Bibliographic record
Abstract
After breast-conserving surgery, positive margins occur when breast cancer cells are found on the resection margin, leading to a higher chance of recurrence and the need for repeat surgery. The NaviKnife is an electromagnetic tracking-based surgical navigation system that helps to provide visual and spatial feedback to the surgeon. In this study, we conduct a gross evaluation of this navigation system with respect to resection margins. The trajectory of the surgical cautery relative to ultrasound-visible tumor will be visualized, and its distance and location from the tumor will be compared with pathology reports. Six breast-conserving surgery cases that resulted in positive margins were performed using the NaviKnife system. Trackers were placed on the surgical tools and their positions in three-dimensional space were recorded throughout the procedure. The closest distance between the cautery and the tumor throughout the procedure was measured. The trajectory of the cautery when it came closest to the tumor model was plotted in 3D Slicer and compared with pathology reports. In two of the six cases, the side at which the cautery came the closest to the tumor model coincided with the side at which positive margins were found from pathology reports. Our method shows that positive margins occur mainly in areas that are not visible from ultrasound imaging. Our system will need to be used in combination with intraoperative tissue characterization methods to effectively predict the occurrence and location of positive margins.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".