Perineural Invasion and Risk of Lethal Prostate Cancer
Bibliographic record
Abstract
Abstract Background: Prostate cancer has a propensity to invade and grow along nerves, a phenomenon called perineural invasion (PNI). Recent studies suggest that the presence of PNI in prostate cancer has been associated with cancer aggressiveness. Methods: We investigated the association between PNI and lethal prostate cancer in untreated and treated prostate cancer cohorts: the Swedish Watchful Waiting Cohort of 615 men who underwent watchful waiting, and the U.S. Health Professionals Follow-Up Study of 849 men treated with radical prostatectomy. One pathologist performed a standardized histopathologic review assessing PNI and Gleason grade. Patients were followed from diagnosis until metastasis or death. Results: The prevalence of PNI was 7% and 44% in the untreated and treated cohorts, respectively. PNI was more common in high Gleason grade tumors in both cohorts. PNI was associated with enhanced tumor angiogenesis, but not tumor proliferation or apoptosis. In the Swedish study, PNI was associated with lethal prostate cancer [OR 7.4; 95% confidence interval (CI), 3.6–16.6; P < 0.001]. A positive, although not statistically significant, association persisted after adjustment for age, Gleason grade, and tumor volume (OR 1.9; 95% CI, 0.8–5.1; P = 0.17). In the U.S. study, PNI predicted lethal prostate cancer independent of clinical factors (HR 1.8; 95% CI, 1.0, 3.3; P =0.04). Conclusions: These data support the hypothesis that perineural invasion creates a microenvironment that promotes cancer aggressiveness. Impact: Our findings suggest that PNI should be a standardized component of histopathologic review, and highlights a mechanism underlying prostate cancer metastasis. Cancer Epidemiol Biomarkers Prev; 26(5); 719–26. ©2017 AACR.
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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 | 0.001 |
| 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".