Development of risk groups in metastatic castration-resistant prostate cancer (mCRPC) to facilitate the identification of active chemotherapy regimens
Bibliographic record
Abstract
5137 Background: Our aim was to develop and validate clinically applicable predictive factors for a >30% PSA decline 3 months following chemotherapy initiation, and to assess the performance of a risk-group based classification in predicting PSA declines and overall survival (OS) in men receiving chemotherapy for mCRPC. Methods: In TAX327, 1006 men with mCRPC were randomized to receive docetaxel (D) in two schedules, or mitoxantrone (M), each with prednisone: 989 provided data on PSA decline at 3 months. Predictive factors for a >30% 3-month decline in PSA levels were identified using multivariate logistic regression in D treated men (n = 656) and validated in M treated men (n = 333). Risk factors were combined to form risk groups to predict PSA declines, OS, tumor, and pain responses. Prostate Cancer Working Group (PCWG2) disease states were evaluated for these outcomes in docetaxel treated men. Results: In multivariate analysis, four independent risk factors predicted for absence of >30% decline in 3-month PSA: significant baseline pain (OR 0.63 p = 0.02), visceral metastases (OR 0.66, p = 0.03), anemia (hemoglobin <13 g/dl, OR 0.72 p = 0.07), and bone scan progression at baseline (OR 0.60 p = 0.009). Predictive accuracy was moderate with a concordance index (c-index) of 0.61. Risk groups (good, intermediate, poor) were developed with median OS of 25.7 (95% CI 23.3–28.6), 18.7 (17.3–19.7), and 12.8 (11.5–14.6) months, respectively (p < 0.0001), and >30% PSA decline in 78, 66, and 58 percent of men (p < 0.001). In the validation M cohort, similar trends for PSA declines and OS were noted across risk groups (OS 22.5, 16.0, 11.8 mo, p < 0.001). PCWG2 subtypes (node only, bone metastatic, and visceral disease), were also highly prognostic and predictive but did not predict OS as well as the TAX327 risk groups (c-index 0.56). Conclusions: Risk groups have been identified and internally validated that predict PSA decline and OS in men with mCRPC. This classification may facilitate evaluation of new regimens of systemic therapy that warrant definitive testing in comparison to docetaxel and prednisone in phase III trials. Prospective validation of these risk groups is needed. [Table: see text]
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".