Detection of clinically significant prostate cancer by micro-ultrasound-informed systematic biopsy during MRI/micro-ultrasound fusion biopsy
Bibliographic record
Abstract
INTRODUCTION: High-resolution micro-ultrasound (microUS) is a novel imaging technique that may visualize clinically significant prostate cancer (csPCa), including those missed by magnetic resonance imaging (MRI ), in real time during prostate biopsy. METHODS: From September 2021 to January 2022, 75 consecutive biopsy-naive men were entered into an observational cohort. All men underwent an MRI /microUS fusion prostate biopsy, completed by a single surgeon using the ExactVU device. At time of biopsy, each biopsy core was given a Prostate Risk Identification using MicroUS (PRI-MUS) score. Anonymized data were entered into a RED Cap database. Cancer detection stratified by Prostate Imaging-Reporting & Data System (PI-RADS ) and PRI-MUS score, and imaging modality was captured. Our primary outcome was the detection rate of csPCa in microUS-informed systematic biopsy cores, taken outside MRI-visible lesions, during MRI /microUS fusion prostate biopsy. RESULTS: A median of three MRI-targeted and 12 microUS-informed systematic cores were taken per patient. MRI /microUS biopsy detected PCa in 84%, with csPCa detected in 52%. Of the 900 microUS-informed systematic cores, 105 cores were PRI-MUS ≥3 and 795 cores were PRI-MUS ≤2. csPCa was detected in 35% of the PRI-MUS ≥3 cores compared to 10% of the PRI-MUS ≤2 cores (p<0.0001). Detection of csPCa varied by core type: 8% of patients were diagnosed by MRI-targeted cores only, 38% were diagnosed by microUS-informed systematic cores only, and 54% were diagnosed by both. CONCLUSIONS: MicroUS-informed systematic biopsy may be a useful adjunct to MRI, with PRI-MUS ≥3 systematic cores having a 3.5-fold increased risk of csPCa compared to PRI-MUS ≤2 cores.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".