Role of Systematic Control Biopsies following Partial Gland Ablation with High-Intensity Focused Ultrasound for Clinically Significant Prostate Cancer
Bibliographic record
Abstract
PURPOSE: Partial gland ablation (PGA) using high-intensity focused ultrasound (HIFU) is currently under investigation for clinically significant prostate cancer (Cs-PCa). Our primary objective was to assess the role of systematic control biopsies following HIFU-PGA in a cohort of Cs-PCa patients. MATERIALS AND METHODS: We studied a single-center retrospective cohort of 77 men treated with HIFU-PGA between October 2015 and December 2019. Patients with unilateral Cs-PCa, defined as Gleason grade group (GGG) ≥2, with visible lesion on multiparametric magnetic resonance imaging (mpMRI) and prostate specific antigen (PSA) ≤15 ng/ml were included. All patients underwent mpMRI with systematic and targeted biopsies before and after HIFU-PGA. The primary outcome was the rate of Cs-PCa at control biopsy within 1 year of treatment. Logistic regression was performed to identify predictive factors of our primary outcome. RESULTS: Median age was 67 years (IQR 61-71), median PSA was 7 ng/ml (IQR 5.5-8.9). Pre-treatment biopsies revealed 48 (62.3%) GGG2 lesions, 24 (31.2%) GGG3 and 5 (6.5%) GGG4 lesions. Cs-PCa was found in 24 (31.2%) patients at systematic control biopsy post-HIFU; Cs-PCa was in the treated lobe for 18 (27%) patients. No variables were identified as significant predictors of Cs-PCa at control biopsy, including PSA kinetics and control mpMRI. Median followup time was 17 months (95% CI 15-21). Median time to any retreatment was 32 months (95% CI 23-42). CONCLUSIONS: Systematic control biopsy within a year after PGA for Cs-PCa can identify the presence of residual Cs-PCa in up to a third of patients. From our early experience, control biopsy should be systematically offered patients regardless of PSA kinetics or control mpMRI results.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".