Subsequent prostate cancer detection in patients with prostatic intraepithelial neoplasia or atypical small acinar proliferation
Bibliographic record
Abstract
INTRODUCTION: To evaluate the predictors of prostate cancer in follow-up of patients diagnosed on initial biopsy with high-grade prostatic intraepithelial neoplasia (HGPIN) or atypical small acinar proliferation (ASAP). METHODS: We studied 201 patients with HGPIN and 22 patients with ASAP on initial prostatic biopsy who had subsequent prostatic biopsies. The mean time of follow-up was 17.3 months (range 1-62). The mean number of biopsy sessions was 2.5 (range 2-6), and the median number of biopsy cores was 10 (range 6-14). RESULTS: On subsequent biopsies, the rate of prostate cancer was 21.9% (44/201) in HGPIN patients. Of these, 32/201 patients (15.9%), 9/66 patients (13.6%) and 3/18 patients (16.6%) were found to have cancer on the first, second and third follow-up biopsy sessions, respectively. In ASAP patients, the cancer detection rate was 13/22 (59.1%), all of whom were found on the first follow-up biopsy. There was a statistically significant difference between the cancer detection rate in ASAP and HGPIN patients (p < 0.001). Multivariate analysis showed that the independent predictors of cancer were the number of cores in the initial biopsy, the number of cores (> 10) in the follow-up biopsy and a prostate specific antigen (PSA) density of >/= 0.15 (odds ratio 0.77, 3.46 and 2.7,8 respectively; p < 0.04). Conversely, in ASAP patients none of these variables were found to be associated with cancer diagnosis. CONCLUSION: ASAP is a strong predictive factor associated with cancer when compared with HGPIN. The factors predictive of cancer on follow-up biopsy of HGPIN are number of cores on initial biopsy, more than 10 cores in rebiopsy and elevated PSA density. As the cancer detection rate on repeated biopsy of HGPIN patients is the same as that of patients without HGPIN, perhaps the standard of repeat biopsy in all patients with HGPIN should be revisited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".