Initial experience and cancer detection rates of office-based transperineal magnetic resonance imaging-ultrasound fusion prostate biopsy under local anesthesia
Bibliographic record
Abstract
INTRODUCTION: We aimed to demonstrate feasibility and cancer detection rates of office-based ultrasound-guided transperineal magnetic resonance imaging-ultrasound (MRI-US) fusion (TFB) prostate biopsy under local anesthesia. METHODS: With institutional review board approval, records of men undergoing TFB in the office setting under local anesthesia were reviewed. Baseline patient characteristics, MRI findings, cancer detection rates, and complications were recorded. The PrecisionPoint Transperineal Access System (Perineologic, Cumberland, MD, U.S.), along with UroNav 3.0 image-fusion system (Invivo International, Best, The Netherlands) were used for all procedures. Following biopsy, men were surveyed to assess patient experience. RESULTS: Between January 2019 and February 2020, 200 TFBs were performed, of which 141 (71%) were positive for prostate cancer, with 117 (83%) Gleason grade group 2 or higher. A total of 259 of 265 MRI lesions were biopsied, with 127 (49%) positive overall. Prostate Imaging-Reporting and Data System (PI-RADS) 4-5 lesions were positive for prostate cancer in 59% of cases. The mean procedural time was 20 minutes, with a patient enter-to-exit room time of 54 minutes. There were no septic complications, no patients required post-procedure hospital admission, and all procedures were successfully completed. Seventy-five percent of patients surveyed reported complete resolution of pain at three days following the procedure. CONCLUSIONS: Office-based TFB represents a viable approach to prostate cancer detection following prostate MRI. Larger-scale assessment is needed to categorize cancer detection rates more accurately by PI-RADs subset, patient selection factors, complication rate, and cost relative to TFB 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 | 0.007 |
| 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 | 0.001 | 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".