Yield of second-round MRI targeted ultrasound-guided fusion prostate biopsy after initial first-round targeted biopsy
Bibliographic record
Abstract
INTRODUCTION: We aimed to determine the yield of second-round magnetic resonance imaging-ultrasound (MRI-US) fusion biopsy and factors that may predict eventual clinically significant (CS) prostate cancer (PCa) diagnosis. METHODS: From 2013 to 2021, 85 men underwent second-round MRI-US fusion biopsy of 92 lesions (47.8% [44/92] peripheral zone and 52.2% [48/92] transition zone). Patient age, prostate-specific antigen (PSA), PSA density (PSAD), size/location of lesions, ADC value, Prostate Imaging-Reporting and Data System (PI-RADS), and PRECISE scores were recorded and compared to histopathological diagnosis (International Society of Urological Pathology [ISUP] grade-group 1 PCa, CS PCa=ISUP grade group ≥2 PCa) using logistic regression. RESULTS: Mean patient age, PSA, and PSAD were 64±7 years, 8.5±7.0 ng/ml, and 0.17±0.11, respectively. Results from first-round targeted biopsy were 63% (58/92) negative and 37% (34/92) clinically insignificant (grade group 1) PCa. Overall, second-round targeted biopsy identified 25% (23/92) CS PCa (grade group 2, n=19; grade group 3, n=4). Considering only lesions with initial negative targeted-biopsy results (n=58), 21% (12/58; grade group 2, n=8; grade group 3, n=4) CS PCa and 13 grade group 1 PCa were diagnosed at second-round biopsy. There was no difference in PSA (p=0.564), size (p=0.595), location (p=0.293), or PI-RADS score (p=0.342) of lesions by eventual CS PCa diagnosis. PSAD (0.2±1.4 vs. 0.16±0.10, p=0.167), ADC (0.748±0.199 vs. 0.833±0.257, p=0.151), and PRECISE score (p<0.01) showed a trend towards association or association with eventual CS PCa diagnosis. CONCLUSIONS: Repeat second-round targeted MRI-US fusion biopsy yielded CS PCa diagnosis in the targeted biopsy specimen in approximately 20% of patients in our study. PRECISE score may be a useful marker to help predict which patients require second-round biopsy.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".