Micro-ultrasound Versus Magnetic Resonance Imaging in Prostate Cancer Active Surveillance
Bibliographic record
Abstract
Accurate assessment of tumor grade is critical for active surveillance (AS) in prostate cancer. We compared magnetic resonance imaging (MRI) and micro-ultrasound scoring (Prostate Imaging-Reporting and Data System [PI-RADS] v2.1 vs Prostate Risk Identification using Micro-ultrasound [PRI-MUS]) in 128 men on AS. The primary outcome was upgrading to Gleason grade group (GG) ≥2. There was no difference in GG ≥2 detection between the imaging techniques (PRI-MUS score ≥3: 33/34, 98%; PI-RADS score ≥3: 29/34, 85%; p = 0.22). The sensitivity, specificity, and positive and negative predictive values for GG ≥2 detection were 97%, 32%, 34%, and 97% with PRI-MUS ≥3, and 85%, 53%, 40%, and 91% with PI-RADS ≥3, respectively. Upgrading to GG ≥2 was more likely for PRI-MUS ≥3 than for PRI-MUS ≤2 scores (odds ratio 15.5, 95% confidence interval 2.0–118.5). A limitation is the lack of blinding to the MRI results. In conclusion, detection of upgrading to GG ≥2 during AS appears similar when using micro-ultrasound or MRI to inform prostate biopsy. Patient summary We looked at a novel imaging technology, micro-ultrasound, in patients undergoing biopsy during active surveillance for prostate cancer. We found that micro-ultrasound can detect prostate cancer that may require treatment at a similar rate to that with magnetic resonance imaging (MRI) scans.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".