Evaluation of an interactive ultrasound-based breast tumor contouring workflow
Bibliographic record
Abstract
PURPOSE: Computer-navigated breast tumor excision using tracked ultrasound is a technique for performing lumpectomies during early-stage breast cancer. An interactive method is used to contour tumors intra-operatively for excision. We evaluated this method’s effectiveness in contouring the entire tumor with minimal inclusion of healthy tissue in the excision volume. Additionally, we investigated the possibility of adding a safety margin to the contoured volume to ensure that the entire tumor is contained by the contour. METHODS: We conducted a study in which 10 participants contoured 5 tumors each using the intra-operative breast tumor contouring system. We analyzed their interactions with the system and their opinions of the contouring workflow. RESULTS: We found that only 0.19% of the tumor volume was not contained by the contour on average. The addition of a 0.4 mm safety margin to the final tumor contour guaranteed that the entire tumor would be contained. We also found a correlation between the amount of time spent on contour verification and excess healthy tissue included in the contour. Users’ perceptions of how well they excluded excess healthy tissue strongly correlated with reality. CONCLUSIONS: This workflow ensures that only a small amount of tumor volume is not contained by the contour and allows the radiologist confidence that they have contained the entire tumor in their contour. With the addition of a safety margin to the resulting tumor contour, the tumor can be completely contained.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".