A prospective comparison of MRI‐US fused targeted biopsy versus systematic ultrasound‐guided biopsy for detecting clinically significant prostate cancer in patients on active surveillance
Bibliographic record
Abstract
BACKGROUND: In active surveillance (AS) patients: (i) To compare the ability of a multiparametric MRI (mpMRI)-ultrasound biopsy system to detect clinically significant (CS) prostate cancer with systematic 12-core biopsy (R-TRUSBx), and (ii) To assess the predictive value of mpMRI with biopsy as the reference standard. METHODS: Seventy-two men on AS prospectively underwent 3T mpMRI . MRI-ultrasound fusion biopsy (UroNavBx) and R-TRUSBx was performed. CS cancer was defined using two thresholds: 1) GS ≥ 7 (CS7) and 2) GS = 6 with >50% involvement (GS6). CS cancer detection rates and predictive values were determined. RESULTS: CS7 cancers were found in 19/72 (26%), 7 (37%) identified by UroNavBx alone, 2 (11%) by R-TRUSBx alone (P = 0.182). UroNav targeted biopsy was 6.3× more likely to yield a core positive for CS7 cancer compared with R-TRUSBx (25% of 141 versus 4% of 874, P < 0.001). Upgrading of GS occurred in 15/72 patients (21%), 13 (87%) detected by UroNavBx and 10 (67%) by R-TRUSBx. The NPV of mpMRI for CS7 cancer was 100%. MRI suspicion level significantly predicted CS cancer on multivariate analysis (OR 3.6, P < 0.001). CONCLUSION: UroNavBx detected CS cancer with far fewer cores compared with R-TRUSBx, and mpMRI had a perfect negative predictive value in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".