Assessment of magnetic resonance imaging (MRI)-fusion prostate biopsy with concurrent standard systematic ultrasound-guided biopsy among men requiring repeat biopsy
Bibliographic record
Abstract
INTRODUCTION: The role of magnetic resonance imaging (MRI)-fusion biopsy (FB) remains unclear in men with prior negative prostate biopsies. This study aimed to compare the diagnostic accuracy of FB with concurrent systematic biopsy (SB) in patients requiring repeat prostate biopsies. METHODS: Patients with previous negative prostate biopsies requiring repeat biopsies were included. Those without suspicious lesions (≥Prostate Imaging-Reporting and Data System [PI-RADS] 3) on MRI were excluded. All patients underwent FB followed by SB. The primary outcome was the sensitivity for clinically significant prostate cancer (Gleason score ≥7). The secondary objective was identification of potential predictive factors of biopsy performance. RESULTS: A total of 53 patients were included; 41 (77%) patients were found to have clinically significant prostate cancer. FB had a higher detection rate of significant cancer compared to SB (85% vs. 76%, respectively, p=0.20) and lower diagnosis of indolent (Gleason score 3+3=6) cancer (10% vs. 27%, respectively, p=0.05). FB alone missed six (15%) clinically significant cancers, compared to 10 (24%) with SB. SB performance was significantly impaired in patients with anterior lesions and high prostate volumes (p<0.05). There was high degree of pathological discordance between the two approaches, with concordance seen in only 34% of patients. CONCLUSIONS: In patients with prior negative biopsies and ongoing suspicion for prostate cancer, a combined approach of FB with SB is needed for optimal detection and risk classification of clinically significant disease. Anterior tumors and large prostates were significant predictors of poor SB performance and an MRI-fusion alone approach in these settings could be considered.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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".