Impact of disease volume on survival efficacy of triplet therapy for metastatic hormone-sensitive prostate cancer: a systematic review, meta-analysis, and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Triplet therapy, androgen receptor signaling inhibitors (ARSIs) plus docetaxel plus androgen-deprivation therapy (ADT), is a novel guideline-recommended treatment for metastatic hormone-sensitive prostate cancer (mHSPC). However, the optimal selection of the patient most likely to benefit from triplet therapy remains unclear. METHODS: We performed a systematic review, meta-analysis, and network meta-analysis to assess the oncologic benefit of triplet therapy in mHSPC patients stratified by disease volume and compare them with doublet treatment regimens. Three databases and meeting abstracts were queried in March 2023 for randomized controlled trials (RCTs) evaluating patients treated with systemic therapy for mHSPC stratified by disease volume. Primary interests of measure were overall survival (OS). We followed the PRISMA guideline and AMSTAR2 checklist. RESULTS: Overall, eight RCTs were included for meta-analyses and network meta-analyses (NMAs). Triplet therapy outperformed docetaxel plus ADT in terms of OS in both patients with high-(pooled HR: 0.73, 95%CI 0.64-0.84) and low-volume mHSPC (pooled HR: 0.71, 95%CI 0.52-0.97). There was no statistically significant difference between patients with low- vs. high-volume in terms of OS benefit from adding ARSI to docetaxel plus ADT (p = 0.9). Analysis of treatment rankings showed that darolutamide plus docetaxel plus ADT (90%) had the highest likelihood of improved OS in patients with high-volume disease, while enzalutamide plus ADT (84%) had the highest in with low-volume disease. CONCLUSIONS: Triplet therapy improves OS in mHSPC patients compared to docetaxel-based doublet therapy, irrespective of disease volume. However, based on treatment ranking, triplet therapy should preferably be considered for patients with high-volume mHSPC while those with low-volume are likely to be adequately treated with ARSI + ADT.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.054 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".