The association of PSA levels and survival outcomes in patients with chemotherapy-naïve, castration-resistant prostate cancer (CRPC) who were treated with androgen receptor signaling axis targeting agent (ARAT): A Japanese cohort study.
Bibliographic record
Abstract
315 Background: PSA decline is used as one of the treatment outcome of androgen receptor signaling axis targeting agent (ARAT) in general. However, correlation between PSA decline and survival outcome is not discussed enough. In this study we evaluated how PSA decline influence the survival outcome of ARAT against chemo-naive castration resistant prostate cancer (CRPC). Methods: A total of 200 chemo-naïve CRPC cases treated with ARAT (abiraterone acetate or enzalutamide) were included in this study. We investigated the relationship between PSA response rate and survival outcome (PSA progression free survival (PSA-PFS), Failure free survival (FFS) and overall survival (OS)). Results: PSA response rate correlated with PSA-PFA, TFS and OS significantly (p<0.0001, <0.0001, 0.0009, respectively). And we categorized PSA decline in four groups, group 1: no PSA decline, group 2: 0-50%, group 3: 50%-90%, group 4: over 90%. Median PSA-PFS were 2M (group 1), 4M (group 2), 10M (group 3) and 16M (group 4) (p<0.0001). Median FFS were 3M (group 1), 6M (group 2), 12M (group 3) and 27M (group 4) (p<0.0001). Median OS were 28M (group 1), 36M (group 2), not reached (group 3 and 4) (p=0.0056). In terms of OS, there is a big different between PSA decline <50% and ≥50% in survival curve. And we compare the factors influencing PSA decline ≥50%. PSA and age at initiating ARAT are significant factors predicting PSA decline 50%. Lower PSA and lower age correlated PSA decline ≥50%. Conclusions: PSA decline strongly correlated with PSA-PFS, FFS and OS in this study. It would be a surrogate marker predicting survival outcomes of chemo-naïve CRPC cases treated with ARAT. Further investigation is warranted to confirm these results.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".