Prognostic stratification of post-docetaxel metastatic castration resistant prostate cancer (mCRPC) from a phase III randomized trial.
Bibliographic record
Abstract
4644 Background: A prognostic model for mCRPC post docetaxel is necessary to guide therapy. We retrospectively analyzed a phase III trial enrolling progressive mCRPC following docetaxel to construct a prognostic model. Additionally, we studied the impact of neutrophil-lymphocyte ratio (NLR), a potential marker for inflammatory and immune state. Methods: A phase III trial (SUN-1120) comparing prednisone combined with sunitinib (N=584) or placebo (N=289) for mCRPC following docetaxel-based chemotherapy was evaluated. The treatment arms were combined for analysis, since no statistical difference was observed in the primary endpoint of overall survival (OS). A logarithmic transformation was applied to non-normal factors. The Kaplan-Meier method was used for OS estimation. To identify an optimal prognostic model for survival, we used a Cox proportional hazards regression methods with forward stepwise selection, stratifying for ECOG PS, progression type (PSA or radiographic) and treatment group. A risk score was calculated and patients were categorized into risk groups to assess model performance. Results: Data from patients without missing data (n=806) were used to construct an optimal model. The factors used in the model that remained individually significant in multivariate analysis were: log-LDH (HR 2.77 [95% CI=2.23, 3.44], p<0.001), hemoglobin (0.81 [0.76, 0.87], p<0.001), log-NLR (1.63 [1.38, 1.92], p<0.001), >1 organ involved (1.53 [1.24, 1.88], p<0.001), log-alkaline phosphatase (1.14 [1.01, 1.30], p=0.041) and log-PSA (1.07 [1.00, 1.13], p=0.036). No clear cutpoints were identified; thus, these prognostic factors were used to group patients into 3 equally sized risk categories. Low, medium and high risk patients (n=268-270 per group) had median (95% CI) OS estimates of 23.7 (21.4-not reached), 13.5 (11.6-15.8) and 7.3 (6.3-8.4) months, respectively. Conclusions: A prognostic risk model with readily available variables significantly discriminated between outcomes in post-docetaxel mCRPC and may provide valuable information in future studies. High NLR was associated with an independent poor prognostic impact, and warrants prospective validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".