MRI-Targeted Prostate Fusion Biopsy: What Are We Missing outside the Target? Implications for Treatment Planning
Bibliographic record
Abstract
Introduction: This study aimed to evaluate the added diagnostic value of systematic biopsies (SBx) after magnetic resonance imaging (MRI)-targeted biopsies (TBx) and the presence of prostate cancer (PCa) outside MRI targets, in a prospective, contemporary, multicentric series of fusion biopsy patients. Methods: We collected data on 962 consecutive patients who underwent fusion biopsy between 2022 and 2024. Prostate cancer was considered clinically significant (csPCa) in the case of grade ≥ 2. Median test and Fisher exact chi-square tests were used. To identify predictors of out-field positivity, univariate and multivariable logistic regression analyses were performed. Results: Prostate cancer and csPCa were detected by TBx only in 56% and 50%, respectively, and by SBx only in 55% and 45%, respectively (p < 0.001). Prostate cancer and csPCa were diagnosed by TBx in 100 (10%) and 82 (8%) SBx-negative cases and by SBx in 86 (9%) and 54 (6%) TBx-negative cases (p < 0.001). Tumors outside MRI targets were found in 213 (33%) cases in the same lobe and 208 (32%) in the contralateral lobe, most of them being csPCa. Predictors of out-field contralateral PCa were positive DRE (HR 1.50, p 0.03), PSA density ≥ 0.15 (HR 2.20, p < 0.001), and PI-RADS score 5 (HR 2.04, p 0.01). Conclusions: Both TBx and SBx identify a non-negligible proportion of csPCa when the other modality is negative. SBx after TBx should always be considered given the risk of missing other csPCa foci within the prostate, especially in patients with positive DRE, PSA density ≥ 0.15, and PIRADS 5 lesions.
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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 | 0.010 | 0.038 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".