Assessment of histopathological features of needle biopsy in recurrent prostate cancer following salvage high-intensity focused ultrasound
Bibliographic record
Abstract
INTRODUCTION: Local recurrence of prostate cancer (PCa) following radiotherapy may be treated with curative intent using salvage high-intensity focused ultrasound (s-HIFU). The interpretation of needle core biopsy specimens following s-HIFU is a daunting task, even for experienced pathologists. We describe various histopathological features encountered in biopsy specimens following whole-gland s-HIFU in one of the largest descriptive studies to date. METHODS: Fifty-five patients with biopsy-proven localized radio-recurrent PCa underwent s-HIFU and transrectal ultrasound (TRUS)-guided prostatic needle biopsies at 180 days post-treatment. All biopsies were reviewed by two genitourinary pathologists. RESULTS: PCa was detected in 11 (24%) biopsies. Radiation therapy-associated changes were identified in all cases. Additional findings included extensive coagulative stromal necrosis (100%), smudgy chromatin of cancer nuclei (82%), and markedly enlarged bizarre nuclei in the residual cancer (55%). Gleason grade assignment was possible in 10 (91%) of these biopsies and concordance of Gleason grading between pre- and post-therapy specimens was observed in six (60%) cases. CONCLUSIONS: The histological interpretation of needle biopsies following salvage HIFU is challenging and requires an understanding of the histopathological changes associated with this procedure in both tumoural and non-tumoural prostatic tissue. Accurate interpretation of the morphological changes following s-HIFU is instrumental for optimization of clinical decision-making and treatment planning in recurrent PCa.
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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 | 0.002 |
| 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".