The Prognostic Value of Neutrophil-to-Lymphocyte Ratio in Metastatic Testicular Cancer
Bibliographic record
Abstract
We investigated the prognostic utility of pre-chemotherapy neutrophil-to-lymphocyte ratio (NLR) in patients with metastatic germ cell tumors (GCTs) undergoing first-line chemotherapy. We utilized two institutional databases to analyze the pretreatment-derived NLR (dNLR). Predictive accuracy was evaluated using the Cox proportional hazard model adjusted for the international germ cell cancer collaborative group (IGCCCG) risk classification. Discriminatory accuracy was evaluated by determining the area under the receiver operating characteristic curve (AUROC). In total, 569 of 690 patients had available dNLR (IGCCCG: good, 64%; intermediate, 21%; poor, 16%). The 5-year and 10-year overall survivals (OSs) for good, intermediate, and poor risk groups were 96.2%, 92.8%, and 62.7% and 93.9%, 90.3%, and 62.7%, respectively. A dNLR of 2 provided the best discriminatory accuracy with an AUROC of 0.58 (95% CI: 0.52–0.65, p = 0.01) for progression-free survival (PFS), whereas for OS, a dNLR of 3 provided the best discriminatory accuracy with an AUROC of 0.62 (95% CI: 0.53–0.70, p < 0.01). A dNLR > 2 was associated with a hazard ratio (HR) of 1.99 (95% CI: 1.27–3.12, p < 0.01) for PFS, which lost its effect after adjustment for IGCCCG (HR: 1.44, 95% CI: 0.90–2.30, p = 0.13). For OS, a dNLR >3 was associated with an HR of 3.00 (95% CI: 1.79–5.01, p < 0.01), but lost its effect after adjustment for IGCCCG. Systemic inflammation plays a role in metastatic GCT, but its prognostic utility beyond established algorithms is limited. The general prognostic value of NLR can be seen across a number of tumors, although the consistency and magnitude of the effect differ according to cancer type, disease stage, and treatment received. We identified that an elevated NLR was associated with an adverse PFS and OS, but not independent of the IGCCCG risk classification. dNLRs >2 and >3 were associated with an adverse PFS and OS, respectively, in patients with metastatic GCT receiving first-line chemotherapy, but not independent of the IGCCCG risk classification.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".