Comparison of micro-ultrasound and multiparametric magnetic resonance imaging for prostate cancer: A multicenter, prospective analysis
Bibliographic record
Abstract
INTRODUCTION: High-resolution micro-ultrasound has the capability of imaging prostate cancer based on detecting alterations in ductal anatomy, analogous to multiparametric magnetic resonance imaging (mpMRI). This technology has the potential advantages of relatively low cost, simplicity, and accessibility compared to mpMRI. This multicenter, prospective registry aims to compare the sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) of mpMRI with high-resolution micro-ultrasound imaging for the detection of clinically significant prostate cancer. METHODS: We included 1040 subjects at 11 sites in seven countries who had prior mpMRI and underwent ExactVu micro-ultrasound-guided biopsy. Biopsies were taken from both mpMRI targets (Prostate Imaging-Reporting and Data System [PI-RADS] >3 and micro-ultrasound targets (Prostate Risk Identification using Micro-ultrasound [PRIMUS] >3). Systematic biopsies (up to 14 cores) were also performed. Various strategies were used for mpMRI target sampling, including cognitive fusion with micro-ultrasound, separate software-fusion systems, and software-fusion using the micro-ultrasound FusionVu system. Clinically significant cancer was those with Gleason grade group ≥2. RESULTS: Overall, 39.5% were positive for clinically significant prostate cancer. Micro-ultrasound and mpMRI sensitivity was 94% vs. 90%, respectively (p=0.03), and NPV was 85% vs. 77%, respectively. Specificities of micro-ultrasound and MRI were both 22%, with similar PPV (44% vs. 43%). This represents the initial experience with the technology at most of the participating sites and, therefore, incorporates a learning curve. Number of cores, diagnostic strategy, blinding to MRI results, and experience varied between sites. CONCLUSIONS: In this initial multicenter registry, micro-ultrasound had comparable or higher sensitivity for clinically significant prostate cancer compared to mpMRI, with similar specificity. Micro-ultrasound is a low-cost, single-session option for prostate screening and targeted biopsy. Further larger-scale studies are required for validation of these findings.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".