Micro-ultrasound for the detection of clinically significant prostate cancer in biopsy-naive men with negative MRI
Bibliographic record
Abstract
INTRODUCTION: Despite a negative magnetic resonance imaging (MRI), some patients may still harbor clinically significant prostate cancer (csPCa, Gleason grade group ≥2). High-resolution micro-ultrasound (microUS) is a novel imaging technology that could visualize csPCa that is missed by MRI. METHODS: This retrospective review included 1011 consecutive patients biopsied between September 2021 and July 2023 in Alberta, Canada. Among them were 103 biopsy-naive patients with negative MRI (Prostate Imaging Reporting & Data System [PI-RADS] ≤2) undergoing microUS-informed prostate biopsy (n=56) scored using Prostate Risk Identification Using Micro-ultrasound (PRI-MUS) or standard transrectal ultrasound prostate biopsy (n=47). The primary outcome was detection rate of csPCa stratified by biopsy technique and PRI-MUS score. RESULTS: MicroUS biopsy identified csPCa in 14/56 (25%) compared to standard biopsy in 8/47 (17%) (p=0.33). Patients with lesions PRI-MUS ≥3 had csPCa detected at a higher rate compared to patients with PRI-MUS ≤2 (42% vs. 16%, p=0.03). The csPCa detection rate was significantly different comparing patients with prostate-specific antigen (PSA) density <0.15 and PRI-MUS ≤2 compared to patients with PSA density ≥0.15 and PRI-MUS ≥3 (14% vs. 60%, p=0.02). CONCLUSIONS: MicroUS may aid in the detection of csPCa for patients with negative MRI.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".