A multi‐institutional randomized controlled trial comparing first‐generation transrectal high‐resolution micro‐ultrasound with conventional frequency transrectal ultrasound for prostate biopsy
Bibliographic record
Abstract
Abstract Objectives To study high‐frequency 29 MHz transrectal side‐fire micro‐ultrasound (micro‐US) for the detection of clinically significant prostate cancer (csPCa) on prostate biopsy, and validate an image interpretation protocol for micro‐US imaging of the prostate. Materials and methods A prospective randomized clinical trial was performed where 1676 men with indications for prostate biopsy and without known prostate cancer were randomized 1:1 to micro‐US vs conventional end‐fire ultrasound (conv‐US) transrectal‐guided prostate biopsy across five sites in North America. The trial was split into two phases, before and after training on a micro‐US image interpretation protocol that was developed during the trial using data from the pre‐training micro‐US arm. Investigators received a standardized training program mid‐trial, and the post‐training micro‐US data were used to examine the training effect. Results Detection of csPCa (the primary outcome) was no better with the first‐generation micro‐US system than with conv‐US in the overall population (34.6% vs 36.6%, respectively, P = .21). Data from the first portion of the trial were, however, used to develop an image interpretation protocol termed PRI‐MUS in order to address the lack of understanding of the appearance of cancer under micro‐US. Micro‐US sensitivity in the post‐training group improved to 60.8% from 24.6% (P < .01), while specificity decreased (from 84.2% to 63.2%). Detection of csPCa in the micro‐US arm increased by 7% after training (32% to 39%, P < .03), but training instituted mid‐trial did not affect the overall results of the comparison between arms. Conclusion Micro‐US provided no clear benefit over conv‐US for the detection of csPCa at biopsy. However, it became evident during the trial that training and increasing experience with this novel technology improved the performance of this first‐generation system.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".