Prostate cancer after initial high-grade prostatic intraepithelial neoplasia and benign prostate biopsy.
Bibliographic record
Abstract
INTRODUCTION: Limited data exist on long term pathological outcomes in patients with initial prostate biopsies showing either high-grade intraepithelial neoplasia (HGPIN) or benign findings, who are subsequently diagnosed with prostate cancer. MATERIALS AND METHODS: Preoperative characteristics of patients showing either HGPIN or benign initial prostate biopsies were investigated and compared in patients with and without a subsequent diagnosis of prostate cancer. We also compared the biopsy and prostatectomy findings in patients with prostate cancer in both groups. RESULTS: We evaluated 161 and 85 patients with initial HGPIN and benign prostate biopsies, respectively, who underwent a subsequent biopsy. After a median follow up of 11 years, prostate cancer was detected in 26.7% patients after HGPIN and in 22.3% patients after initial benign biopsy. Ninety-eight percent of positive biopsies after initial HGPIN demonstrated either Gleason score (GS) 3 + 3 (86%) or GS 3 + 4 (12%). In the benign group, 100% of patients demonstrated prostate cancer on biopsy with either GS 3 + 3 (58%) or GS 3 + 4 (42%). Of 35 patients who underwent prostatectomy (22 after initial HGPIN biopsy and 13 after initial benign biopsy), all had node negative, organ-confined disease; 86% and 54% patients had GS6 disease, with = 5% tumor volume found in 91% and 62% of the HGPIN and benign group, respectively. CONCLUSIONS: Patients with initial HGPIN or benign biopsies preceding a diagnosis of prostate cancer usually show favourable pathology on positive biopsy and prostatectomy, most commonly exhibiting low volume and low grade disease. These findings may help clinicians risk-stratify patients who may benefit from conservative management options.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".