An intraoperative 3D ultrasound system for tumor margin determination in breast cancer surgery
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to analyze the clinical utility of a portable three-dimensional ultrasound (3DUS) system to be used for surgical guidance of lumpectomy surgeries. In 11%-60% of lumpectomy surgeries, a second surgery is required to fully resect the tumor. Previous studies have used 3DUS as a guidance tool with the hope of more accuracy in resecting the entire tumor during the first surgery. However, they utilized larger systems, which are not easily integrated into the operating room. METHODS: The portable 3DUS scanning system we developed consisted of a motorized "tilt" scanner coupled to a Terason t3000 portable ultrasound machine (Terason Ultrasound, Burlington, MA). The 3DUS system was evaluated by measuring agar "tumor" phantoms of known volumes and acquiring and segmenting images from nine patients undergoing lumpectomy. RESULTS: Experiments on simulated agar tumor phantoms have shown that our device could be used to measure objects with smooth, well-defined boundaries of known volume with an error of 3%. It was possible to view and segment estimated tumor margins from the clinical images in three dimensions. Correspondence between measurements obtained in the laboratory and the operating room varied with tumor geometry and the degree of spiculation in the ultrasound image. The measured values obtained by the system did not correspond closely with those obtained using histology. However, a more accurate histological measurement using 3D histology may provide a better basis for comparison. CONCLUSIONS: The results of imaging simulated agar tumor phantoms indicate the system's consistency in measuring objects of known volume and geometry. The system could be used for segmenting the approximate boundary of lumpectomy patients' breast tumors relative to inserted guide wires. The potential advantages of this system are a reduction in the number of re-excision surgeries required and a reduction in the operative time with the patient under anesthesia.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".