Prostate-specific Antigen Response as a Prognostic Factor for Overall Survival in Patients with Prostate Cancer Treated with Androgen Receptor Pathway Inhibitors: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: For patients with advanced prostate cancer (PC) treated with androgen deprivation therapy (ADT) plus an androgen receptor pathway inhibitor (ARPI), the decline in prostate-specific antigen (PSA) is a potential biomarker for treatment response. We synthesised data regarding the association of the PSA response with overall survival (OS). METHODS: The MEDLINE, Embase, Web of Science, and Google Scholar databases were searched up to November 2024 to identify studies evaluating the association between the PSA response and OS among patients treated with ADT + ARPI. Hazard ratios (HRs) were pooled in random-effects meta-analyses. KEY FINDINGS AND LIMITATIONS: We identified 14 studies comprising a total of 8883 patients. Among four studies in metastatic hormone-sensitive PC (n = 2197), achievement of an undetectable PSA level was associated with better OS (HR 0.33, 95% confidence interval [CI] 0.23-0.49). In two studies in nonmetastatic castration-resistant PC (n = 1507), a PSA decline to <0.2 ng/ml (HR 0.28, 95% CI 0.21-0.36), a PSA reduction of ≥90% (HR 0.39, 95% CI 0.28-0.52), and a PSA reduction of ≥50% (HR 0.34, 95% CI 0.16-0.69) were associated with better OS. Among four studies in metastatic castration-resistant PC (n = 3728), PSA reductions of ≥90% (HR 0.22, 95% CI 0.14-0.34) and ≥50% (HR 0.29, 95% CI 0.20-0.41) were associated with better OS. The main limitations include heterogeneity in study designs and use of ADT before baseline PSA measurement in mHSPC studies. CONCLUSIONS AND CLINICAL IMPLICATIONS: The PSA response following ADT + ARPI therapy is significantly associated with OS across all metastatic and castration-resistant PC states and could serve as a clinically useful early signal of efficacy. It remains to be proven whether the PSA response is a surrogate for OS or should guide changes in clinical care.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".