Perineural invasion on prostate biopsy does not predict adverse pathological outcome.
Bibliographic record
Abstract
INTRODUCTION: The clinical significance of perineural invasion (PNI) on prostate needle biopsy is controversial. The aim of this present study is to determine the role of PNI on prostate biopsy in predicting adverse findings at radical prostatectomy in a recent cohort of screen detected prostate cancer. MATERIALS AND METHODS: We analyzed 470 patients diagnosed with prostate cancer from a prospectively maintained database at Princess Margaret Hospital. Out of the 470 patients diagnosed with prostate cancer, 139 underwent radical prostatectomy. Pathological specimens were examined, and perineural invasion was identified as carcinoma tracking along or around a nerve in the perineural space. We investigated the predictive value of PNI on biopsy with PNI on radical prostatectomy as well as the ability of PNI on prostate biopsy to predict adverse findings at radical prostatectomy. RESULTS: Perineural invasion was present in 124 (26%) of biopsy specimens diagnosed with prostate cancer and 94 (68%) of those who chose radical prostatectomy. Perineural invasion on prostate needle biopsy was not predictive of radical prostatectomy Gleason score (p = .377), pathological stage (p = .852), extraprostatic extension (p = .258), surgical margin (p = .079), lymphovascular invasion (p = .499), and upgrading (p = .514) or downgrading (p = .208) at radical prostatectomy. The sensitivity, specificity, positive predictive value, and negative predictive value of PNI on biopsy for PNI on radical prostatectomy were 32%, 82%, 79%, and 37% respectively. The Cohen's Kappa correlation coefficient was .11. CONCLUSIONS: Perineural invasion on prostate needle biopsy is not predictive of radical prostatectomy outcome. Furthermore, perineural invasion on biopsy has limited predictive value for perineural invasion at radical prostatectomy.
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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 | 0.004 |
| 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) | 0.002 | 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".