Central Nervous System Toxicity in Prostate Cancer Patients Treated with Androgen Receptor Signaling Inhibitors: A Systematic Review, Meta-analysis, and Network Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Androgen-receptor signaling inhibitors (ARSIs) significantly improve survival in systemic therapy for advanced/metastatic prostate cancer (PCa) patients; however possible central nervous system (CNS) toxicity is an unaddressed concern. We aimed to assess and compare the incidence of CNS-related adverse events (AEs) secondary to the treatment of PCa patients with different ARSIs. MATERIALS: In August 2023, a comprehensive seach was conducted in three databases for randomized controlled trials (RCTs) of PCa patients receiving ARSIs plus ADT. The primary endpoints included mental impairment, cognitive impairment, seizure, fatigue, and falls. RESULTS: Twenty-six RCTs, comprising 20,328 patients, were included in meta-analyses and network meta-analyses (NMAs). ARSIs increased the risk of mental impairment (RR: 1.72; 95% CI, 1.09-2.71), cognitive impairment (RR: 2.25; 95% CI, 1.78-2.86), seizure (RR: 2.20, 95% CI, 1.09-4.45), fatigue (RR: 1.31, 95% CI, 1.20-1.43), and falls (RR: 2.07, 95% CI, 1.60-2.67) compared to standard of care (SOC). Based on NMAs, Enzalutamide showed a significant increase in risk for all assessed CNS-related AEs, while Abiraterone demonstrated significant risk increases in cognitive impairment, fatigue, and falls. Conversely, Darolutamide did not exhibit significant increases in risk for any CNS-related AEs, except for fatigue. CONCLUSIONS: The addition of ARSIs to ADT increased all examined CNS-related AEs compared to SOC. Each ARSI is associated with a distinct profile of CNS-related AEs. Careful patient selection and monitoring for CNS sequelae is necessary to achieve the best quality of life in patients on ARSI + ADT for PCa.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.043 |
| Bibliometrics | 0.005 | 0.006 |
| 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".