Insights into the Prognostic Efficacy of the Geriatric Nutritional Risk Index for Nasopharyngeal Carcinoma in the Era of Volumetric Modulated Arc Therapy: A Nomogram for Predicting Long-Term Survival Outcomes
Bibliographic record
Abstract
Background: The geriatric nutritional risk index (GNRI), a composite metric of serum albumin and body weight, has emerged as a prognostic tool in various cancers. However, its relevance in nasopharyngeal carcinoma (NPC) patients treated with volumetric modulated arc therapy (VMAT) remains unexplored. The aim of this study was to assess the effect of the GNRI in the prediction of the prognosis of nasopharyngeal carcinoma in the era of VMAT. Methods: This retrospective study analyzed 498 newly diagnosed, non-metastatic NPC patients treated with VMAT between 2010 and 2011. The GNRI was calculated using serum albumin and body weight ratios, with receiver operating characteristic (ROC) curve analysis determining its optimal prognostic cutoff. Patients were stratified into training (70%) and validation (30%) cohorts. Cox regression identified independent prognostic factors, which were integrated into a nomogram predicting 3- and 5-year overall survival (OS). Model performance was assessed via the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Results: In the study, 348 patients were included in the training cohort and 150 patients were included in the validation cohort according to a ratio of 7:3. The median follow-up was 68 months, with 5-year OS rates of 79.3%. A GNRI > 102 independently predicted improved survival (HR = 0.64; p = 0.044), alongside tumor volume, age, and N-stage. The nomogram demonstrated strong discrimination (C-index: 0.757–0.762 for training; 0.737–0.744 for validation) and calibration, aligning closely with observed survival. DCA confirmed superior clinical utility over default strategies. NPC patients treated with VMAT with a high GNRI, female sex, and a lower N-stage exhibited significantly better OS (p < 0.05). Conclusions: The GNRI is a robust prognostic marker for NPC patients receiving VMAT, reflecting the interplay of nutrition, inflammation, and treatment response. The validated nomogram provides a practical tool for individualized risk stratification, enhancing clinical decision-making in the era of precision radiotherapy.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".