Impact of neutrophil-to-lymphocyte ratio on effects of targeted therapy for metastatic renal cell carcinoma patients with extrapulmonary metastasis
Bibliographic record
Abstract
INTRODUCTION: The aim of our present study was to investigate the impact of the pretreatment neutrophil-to-lymphocyte ratio (NLR) on the antitumour effects of targeted agents in patients with metastatic renal cell carcinoma (mRCC). METHODS: The NLRs in 283 cases of molecular targeted therapy for mRCC were measured before starting the prescription of the molecular targeted agent. The significance of pretreatment NLR on the site of metastatic organs and on progression-free survival (PFS) in each case was analyzed. RESULTS: Metastases other than lung, which is defined as "extrapulmonary metastasis," were observed in 190 cases (67.1%). The median of pretreated NLR was 2.39 (0.49-68.7). In 97 of the 283 cases, pretreated NLR was 3.0 or higher. These cases were categorized as the high NLR group and the rest as the low NLR group. When the cases with extrapulmonary metastasis were investigated and classified based on their pretreated NLR, 50% PFS in the high NLR and low NLR groups was 6.7 months and 12 months (p=0.0001), respectively. Multivariate analysis revealed that high NLR (>3.0) was an independent predictive factor for PFS in the cases with extrapulmonary metastasis (hazard ratio 2.762; p<0.0001), while there was no significant difference between PFS in the high and low NLR groups in cases with no extrapulmonary metastasis (p=0.3457). CONCLUSIONS: Our data indicate that the predictive significance of the NLR in mRCC cases involving targeted therapy depends on the metastatic organs. NLR is an independent predictive factor of PFS in cases of mRCC with extrapulmonary metastasis treated with targeted therapy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".