The Diagnostic Yield of MRI–Transrectal US Fusion Prostate Biopsy in Patients With Suspected Prostate Cancer
Bibliographic record
Abstract
Introduction: This study aimed to determine the positive predictive value (PPV) of magnetic resonance imaging–transrectal ultrasound (MRI-TRUS) machine fusion prostate biopsies, and to identify factors associated with a positive biopsy. Methods: With ethics approval, we retrospectively evaluated all MRI-TRUS machine fusion prostate biopsies at our institution from September 2022 to April 2025. True positive clinically significant prostate cancers (csPCa) were defined as Gleason ≥7. PPVs were calculated overall and for PI-RADS 3, 4 and 5 categories. A generalized linear mixed model (GLMM) was created evaluating the following factors as fixed effects: PI-RADS category; prostate-specific antigen (PSA) density (<0.10, 0.10-0.15, ≥0.15 ng/mL 2 ); lesion size (<7, 7-15, ≥15 mm); lesion location (peripheral vs transition zone); ultrasound correlate (present/absent); prostate size (<60 vs ≥60 mL); interval from MRI to biopsy (<6 months or not); and biopsy operator (2 radiologists). Referring urologist (n = 19) and reporting radiologist (n = 8) were included as random effects. Results: 372 patients (mean age, 67 ± 7 years) with 529 lesions underwent biopsy. The overall PPV was 314/529 (59.4%). For PI-RADS 3 to 5, PPVs were 32/72 (44.4%), 123/243 (50.6%), and 159/214 (74.3%), respectively. In GLMM analysis, PI-RADS 5 versus 3 (OR 3.6, 95% CI, 1.7-7.4), PSA density ≥0.15 ng/mL 2 (OR 2.2, 95% CI, 1.2-3.8), and presence of an ultrasound correlate (OR 2.7, 95% CI, 1.7-4.2) were associated with true positive biopsies. Small lesion size <7 mm was associated with a false positive biopsy (OR 0.4, 95% CI, 0.2-0.8). Conclusion: The yield of fusion prostate biopsies at our institution is high. PI-RADS 5, PSA density ≥0.15 ng/mL 2 , and an ultrasound correlate at biopsy were associated with csPCa, whereas sub-7 mm lesions were negatively associated with csPCa.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".