Micro-ultrasound transperineal prostate biopsy as an alternative to MRI-US fusion transrectal biopsy
Bibliographic record
Abstract
INTRODUCTION: ExactVu micro-ultrasound generates high-resolution images and promises to improve prostate biopsy performance, while transperineal prostate biopsy (TPB) has gained popularity due to its sterile technique. The aim of this study was to compare TPB using ExactVu to transrectal biopsy (TRB). METHODS: A retrospective analysis of patients who underwent TPB (n=306) using ExactVu or TRB (n=392) from 2019-2023 was performed. Clinical parameters were compared between the groups using Chi-squared test. Putative predictors of cancer on biopsy and upgrading on radical prostatectomy were investigated using logistic regression. RESULTS: More transperineal than transrectal biopsy patients had a Prostate Imaging-Reporting and Data System (PI-RADS) 5 lesion (40% vs. 28%, p=0.001) and were biopsynaive (53% vs. 39%, p<0.001). In patients with no previous diagnosis of prostate cancer, the clinically significant prostate cancer detection rate was higher in the TPB group (53% vs. 42%, p=0.01). Transperineal patients required fewer cores to obtain equal cancer detection rates (11±5 vs. 15±4 cores, p<0.01). Upgrading from grade group 1 to grade group ≥2 on radical prostatectomy was more common with TRB (9.1% vs. 2.1%, p=0.04). Urinary retention rate did not differ by biopsy type and two transrectal but no transperineal patients developed urosepsis. CONCLUSIONS: TPB required fewer cores to obtain a similar clinically significant prostate cancer detection rate when compared to TRB. TPB had fewer complications and a low upgrade rate. This suggests that cognitive fusion TPB using ExactVu is an excellent alternative to software fusion TRB.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".