Can neutrophil-to-lymphocyte ratio be used to identify patients with metastatic renal cell carcinoma who may gain greater benefit from cytoreductive nephrectomy?
Bibliographic record
Abstract
490 Background: Two randomized trials demonstrated survival benefits associated with cytoreductive nephrectomy (CN) for metastatic renal cell carcinoma (mRCC) in the interferon era. However, the role of CN in the era of targeted therapies is yet to be defined. In recent years, neutrophil-to-lymphocyte ratio (NLR) has emerged as a clinically useful prognostic marker in several cancers including mRCC. In this multi-center retrospective study, we aim to assess the impact of CN in mRCC and the value of NLR in risk stratification and patient selection. Methods: Patients with de novo mRCC diagnosed between 2006 and 2012 from three large Australian hospitals were identified using an electronic database. Data regarding clinicopathological features, MSKCC risk group, NLR at the time of metastatic diagnosis, treatments received and survival were collected. NLR ≥5 was used as the cutoff for statistical analyses. Survival analyses were performed using the Kaplan-Meier method and compared using the log-rank test. Multivariate analyses used the Cox proportional hazards method. Results: Our study identified 91 de novo mRCC patients. CN was performed in 46 (51%) patients. Patients who underwent CN were more likely to be younger (median age 59.0 vs. 64.6, p=0.019), and to have received systemic therapy post CN (91% vs. 76%, p=0.043). Median overall survival (mOS) was significantly improved in patients who underwent CN (23.0 mo vs. 10.9 mo, p=0.039). Patients with NLR <5 also had superior mOS (16.7 mo vs. 6.2 mo; HR 0.53; 95% CI; 0.24-0.84; p=0.013). While CN was associated with substantially improved survival in patients with both NLR <5 (mOS 31.1 mo vs. 7.0 mo; HR 0.41; 95% CI, 0.18-0.64; p=0.0009) and NLR ≥5 (mOS 10.9 mo vs. 2.3 mo; HR 0.33; 95% CI, 0.11-0.69; p=0.009), the absolute survival difference was greater in patients with baseline NLR <5. Significant survival benefits associated with CN were maintained in multivariate analyses (HR 0.31; 95% CI, 0.18-0.55; p<0.0001). Conclusions: CN is associated with significantly improved OS in de novo mRCC. The incremental survival benefit associated with CN was seen irrespective of NLR.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".