Design and evaluation of a 3D transrectal ultrasound prostate biopsy system
Bibliographic record
Abstract
Biopsy of the prostate using ultrasound guidance is the clinical gold standard for diagnosis of prostate adenocarcinoma. The current prostate biopsy procedure is limited to using 2D transrectal ultrasound (TRUS) images to target biopsy sites and record biopsy core locations for postbiopsy confirmation. Localization of the 2D image in its actual 3D position is ambiguous and limits procedural accuracy and reproducibility. We have developed a 3D TRUS prostate biopsy system that provides 3D intrabiopsy information for needle guidance and biopsy location recording. The system conforms to the workflow and imaging technology of the current biopsy procedure, making it easier for clinical integration. In this paper, we describe the system design and validate the system accuracy by performing mock biopsies on US/CT multimodal patient-specific prostate phantoms. Our biopsy system generated 3D patient-specific models of the prostate with volume errors less than 3.5% and mean boundary errors of less than 1 mm. Using the 3D biopsy system, needles were guided to within 2.3 +/- 1.0 mm of 3D targets and with a high probability of biopsying clinically significant tumors. The positions of the actual biopsy sites were accurately localized to within 1.5 +/- 0.8 mm.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".