SUVmax-IPI as a New Prognostic Index in Metastatic Non-Small Cell Lung Cancer Patients Receiving Nivolumab
Bibliographic record
Abstract
Background/Objectives: Nivolumab has significantly improved outcomes in patients with metastatic non-small cell lung cancer (NSCLC); however, reliable prognostic biomarkers remain an unmet need. To address this gap, we developed the SUVmax-IPI, a novel prognostic index combining maximum standardized uptake value (SUVmax) from 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) with systemic inflammatory markers. This study aimed to evaluate the prognostic value of SUVmax-IPI in patients with NSCLC receiving nivolumab therapy. Methods: This multicenter retrospective analysis included 187 patients with metastatic NSCLC receiving nivolumab across 5 tertiary institutions. The SUVmax-IPI incorporated pretreatment SUVmax and laboratory-based inflammatory prognostic index (IPI) parameters. Survival outcomes were evaluated using Kaplan–Meier analysis with log-rank testing and multivariate cox regression. Results: Receiver operating characteristic (ROC) analysis established an optimal SUVmax-IPI cut-off of 241.9. Patients with SUVmax-IPI ≤ 241.9 had significantly better survival outcomes: median overall survival (OS) was 35 versus 15 months (p = 0.002). For progression-free survival (PFS), although a numerical difference favored patients with SUVmax-IPI ≤ 241.9 (median: 15 vs. 8 months), this did not reach statistical significance (log-rank p = 0.175). Multivariate analysis confirmed SUVmax-IPI as an independent predictor of survival (p = 0.002). Conclusions: The SUVmax-IPI represents a promising prognostic tool for patients with metastatic NSCLC who received at least 3 months of nivolumab, integrating metabolic and inflammatory parameters to predict survival outcomes.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".