Real-world Clinical Outcomes and Prognostic Factors in Neuroendocrine Prostate Cancer
Bibliographic record
Abstract
BACKGROUND: Neuroendocrine prostate cancer (NEPC) encompasses pure NEPC and tumors with mixed adenocarcinoma and neuroendocrine histology. While NEPC is thought to confer a poor prognosis, outcome data are sparse, making risk stratification and treatment decisions difficult for clinicians. METHODS: This retrospective study identified patients with morphological and/or immunohistochemical NEPC features on pathological review of high-grade prostate cancer cases. Median overall survival (OS) was calculated by stage and castration sensitivity. Prognostic factors were assessed via multivariate analysis. OS and progression-free survival on first-line metastatic systemic treatment were also evaluated. RESULTS: Of 135 NEPC cases, 25.9% had NEPC documented in the original pathological report. Mixed pathology was found in 91.9% of cases. Median OS from NEPC diagnosis was 59.2, 42.3, 14.3, 17.6 and 9.6 months for localized, nonmetastatic castration-sensitive, nonmetastatic castration-resistant, metastatic castration-sensitive and metastatic castration-resistant prostate cancer, respectively. Anemia (hazard ratio [HR]: 1.66; 95% CI 1.05-2.16; P = .031) and elevated neutrophil-lymphocyte ratio (NLR) (HR: 1.51; 95% CI 1.01-2.52; P = .045), were associated with increased risk of death on multivariate analysis. 67 patients received first-line metastatic treatment beyond androgen deprivation, with a median progression-free survival of 5.2 months and OS of 15 months. Of these, 50.7% received more than 1 line of systemic treatment. CONCLUSION: We observed underdiagnosis of NEPC in pathology specimens. NEPC is associated with poorer prognosis than would be expected in pure adenocarcinoma populations, with rapid progression on first-line metastatic treatment and sharp drop-off between subsequent treatment lines. Anemia and elevated NLR were associated with poor survival.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".