Prostatic needle biopsies following primary high intensity focused ultrasound (HIFU) therapy for prostatic adenocarcinoma: histopathological features in tumour and non-tumour tissue
Bibliographic record
Abstract
AIMS: High intensity focused ultrasound (HIFU) is currently offered as primary treatment for patients with clinically localised prostate cancer. Data on histopathological features of post-treatment biopsies are limited. METHODS: Pretreatment biopsies were identified in 45 men (age range 41-85) who received primary HIFU therapy. Post-HIFU biopsies were performed in 30 of these patients (67%) at mean 14.1 months (95% CI 11.7 to 16.5) follow-up, 22 due to rising PSA and eight as part of routine follow-up. Biopsies were examined for presence, distribution and extent of adenocarcinoma, Gleason scores, use of standard immunohistochemistry and ablative tissue changes were attributable to HIFU. RESULTS: In post-HIFU biopsies performed for biochemical failure, 17/22 (77%) contained adenocarcinoma; 4/22 (18%) had higher post-HIFU Gleason score; 3/22 (14%) had newly recognised bilateral involvement; and 4/22 (18%) had higher percentage tissue involvement compared with pre-HIFU biopsies. Of cases without rising post-HIFU PSA, 2/8 (25%) routine follow-up biopsies contained adenocarcinoma. Stromal fibrosis was the commonest finding in non-tumour post-HIFU biopsy tissue (17/30, 57%) with coagulative necrosis occurring in fewer cases (4/30, 13%) and over a shorter follow-up interval than cases showing fibrosis (8.5 (0.2-16.8) vs 15.3 (11.5-19.1) months). Treatment effects in tumour cells precluding the assignment of Gleason scores or use of immunohistochemistry in post-HIFU biopsies were not identified. CONCLUSION: Post-HIFU biopsies are positive in more than 75% of patients with elevated or rising PSA. Stromal fibrosis is common but the tissue effects of this modality do not appear to impair pathologists' ability to detect and grade adenocarcinoma in follow-up biopsies.
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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 | 0.001 |
| 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 | 0.000 | 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".