Serum neuroendocrine (NE) markers and clinical characteristics of treatment-emergent small cell neuroendocrine prostate cancer (t-SCNC) in men with metastatic castration resistant prostate cancer (mCRPC): Data from the West Coast Prostate Cancer Dream Team.
Bibliographic record
Abstract
278 Background: Detection of t-SCNC in mCRPC patients relies primarily on histopathologic evaluation (Histo) of a metastatic tumor biopsy (bx), likely leading to underdiagnosis. The clinical features of t-SCNC and the diagnostic utility of serum NE markers (neuron-specific enolase (NSE) and chromogranin (CGA)) were evaluated. Methods: Eligible patients (pts) underwent a metastatic bx at one of 5 centers. Histo was performed by 3 independent pathologists (JH, GT, LT). NE markers were evaluated in a central lab (lower limit = 1 ng/mL). Kruskal-Wallis and chi-square test were used to compare continuous and categorical variables, respectively. Receiver-operative-curve (ROC) analysis of serum NE markers was undertaken. Results: 160 consecutive pts with available Histo and NE markers were included. t-SCNC was found in 27 pts (17%). Detection of t-SCNC was observed in all bx sites, including liver (14%), lymph node (19%) and bone (14%). Clinical features are shown in the Table. By ROC analysis, if both serum NSE was > 6.05 ng/mL and chromogranin was > 3.1 ng/mL, the sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV) for the detection of t-SCNC were 95%, 50%, 98%, and 22%, respectively. Conclusions: Many of the classic features of de novo SCNC, including low PSA levels, do not reliably distinguish t-SCNC. In contrast, serum NE markers have diagnostic utility with high sensitivity and NPV, but low specificity and PPV. Heterogeneous NE differentiation may partially account for these findings. Clinical trial information: NCT02432001. [Table: see text]
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".