Prognostic Factors for Overall Survival in Patients With Metastatic Renal Cell Carcinoma Treated With Vascular Endothelial Growth Factor–Targeted Agents: Results From a Large, Multicenter Study
Bibliographic record
Abstract
PURPOSE: There are no robust data on prognostic factors for overall survival (OS) in patients with metastatic renal cell carcinoma (RCC) treated with vascular endothelial growth factor (VEGF) -targeted therapy. METHODS: Baseline characteristics and outcomes on 645 patients with anti-VEGF therapy-naïve metastatic RCC were collected from three US and four Canadian cancer centers. Cox proportional hazards regression, followed by bootstrap validation, was used to identify independent prognostic factors for OS. RESULTS: The median OS for the whole cohort was 22 months (95% CI, 20.2 to 26.5 months), and the median follow-up was 24.5 months. Overall, 396, 200, and 49 patients were treated with sunitinib, sorafenib, and bevacizumab, respectively. Four of the five adverse prognostic factors according to the Memorial Sloan-Kettering Cancer Center (MSKCC) were independent predictors of short survival: hemoglobin less than the lower limit of normal (P < .0001), corrected calcium greater than the upper limit of normal (ULN; P = .0006), Karnofsky performance status less than 80% (P < .0001), and time from diagnosis to treatment of less than 1 year (P = .01). In addition, neutrophils greater than the ULN (P < .0001) and platelets greater than the ULN (P = .01) were independent adverse prognostic factors. Patients were segregated into three risk categories: the favorable-risk group (no prognostic factors; n = 133), in which median OS (mOS) was not reached and 2-year OS (2y OS) was 75%; the intermediate-risk group (one or two prognostic factors; n = 301), in which mOS was 27 months and 2y OS was 53%; and the poor-risk group (three to six prognostic factors; n = 152), in which mOS was 8.8 months and 2y OS was 7% (log-rank P < .0001). The C-index was 0.73. CONCLUSION: This model validates components of the MSKCC model with the addition of platelet and neutrophil counts and can be incorporated into patient care and into clinical trials that use VEGF-targeted agents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".