Development of a 3D ultrasound-guided prostate biopsy system
Bibliographic record
Abstract
Biopsy of the prostate using ultrasound guidance is the clinical gold standard for diagnosis of prostate adenocarinoma. However, because early stage tumors are rarely visible under US, the procedure carries high false-negative rates and often patients require multiple biopsies before cancer is detected. To improve cancer detection, it is imperative that throughout the biopsy procedure, physicians know where they are within the prostate and where they have sampled during prior biopsies. The current biopsy procedure is limited to using only 2D ultrasound images to find and record target biopsy core sample sites. This information leaves ambiguity as the physician tries to interpret the 2D information and apply it to their 3D workspace. We have developed a 3D ultrasound-guided prostate biopsy system that provides 3D intra-biopsy information to physicians for needle guidance and biopsy location recording. The system is designed to conform to the workflow of the current prostate biopsy procedure, making it easier for clinical integration. In this paper, we describe the system design and validate its accuracy by performing an in vitro biopsy procedure on US/CT multi-modal patient-specific prostate phantoms. A clinical sextant biopsy was performed by a urologist on the phantoms and the 3D models of the prostates were generated with volume errors less than 4% and mean boundary errors of less than 1 mm. Using the 3D biopsy system, needles were guided to within 1.36 ± 0.83 mm of 3D targets and the position of the biopsy sites were accurately localized to 1.06 ± 0.89 mm for the two prostates.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 |
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".