Repeat Prostate Biopsy Accuracy: Simulator-based Comparison of Two- and Three-dimensional Transrectal US Modalities
Bibliographic record
Abstract
PURPOSE: To compare the accuracy of biopsy with two-dimensional (2D) transrectal ultrasonography (US) with that of biopsy with conventional three-dimensional (3D) transrectal US and biopsy with guided 3D transrectal US in the guidance of repeat prostate biopsy procedures in a prostate biopsy simulator. MATERIALS AND METHODS: The institutional review board approved this retrospective study. Five residents and five experts performed repeat biopsies with a biopsy simulator that contained the transrectal US prostate images of 10 patients who had undergone biopsy. Simulated repeat biopsies were performed with 2D transrectal US, conventional 3D transrectal US, and guided 3D transrectal US (an extension of 3D transrectal US that enables active display of biopsy targets). The modalities were compared on the basis of time per biopsy and how accurately simulated repeat biopsies could be guided to specific targets. The probability for successful biopsy of a repeat target was calculated for each modality. RESULTS: Guided 3D transrectal US was significantly (P < .01) more accurate for simulated biopsy of repeat targets than was 2D or 3D transrectal US, with a biopsy accuracy of 0.86 mm +/- 0.47 (standard deviation), 3.68 mm +/- 2.60, and 3.60 mm +/- 2.57, respectively. Experts had a 70% probability of sampling a prior biopsy target volume of 0.5 cm(3) with 2D transrectal US; however, the probability approached 100% with guided 3D transrectal US. Biopsy accuracy was not significantly different between experts and residents for any modality; however, experts were significantly (P < .05) faster than residents with each modality. CONCLUSION: Repeat biopsy of the prostate with 2D transrectal US has limited accuracy. Compared with 2D transrectal US, the biopsy accuracy of both experts and residents improved with guided 3D transrectal US but did not improve with conventional 3D transrectal US.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".