The HALP score as a prognostic biomarker in non-small cell lung cancer: A comprehensive meta-analysis of over 7,000 patients.
Bibliographic record
Abstract
e20031 Background: The Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) score is an emerging prognostic biomarker in oncology, reflecting nutritional, immune, and inflammatory states. While its prognostic utility has been studied in various cancer types, its role in non-small cell lung cancer (NSCLC) remains unclear. This study presents the first meta-analysis assessing the prognostic significance of the HALP score in NSCLC, utilizing the most comprehensive dataset of studies to date and distinguishing itself from previous systematic reviews that included diverse solid tumor types. Methods: A systematic search was conducted in PubMed, EMBASE, Scopus, and WOS to find studies on pre-treatment HALP score groups (high or low) and hazard ratios (HR) for overall survival (OS), progression-free survival (PFS), or disease-free survival (DFS). Two reviewers independently extracted data and assessed quality. Random-effects meta-analyses were conducted using Cochrane RevMan Web. Confidence intervals (CIs) for OS and PFS applied the Hartung-Knapp-Sidik-Jonkman method, while the Wald-type method was used for DFS. Tau² was estimated using the Restricted Maximum-Likelihood (REML) method. Sensitivity analyses and quality assessments using the Newcastle-Ottawa Scale were performed. Results: An initial search identified 1,352 studies, with 15 selected for full-text review. Of these, 11 cohort studies (10 retrospective, 1 prospective) included 7,452 NSCLC patients across all stages. HALP cut-offs ranged from 13.99 to 48.2, determined by ROC analysis, X-tile software, or mean values. The median follow-up time varied across studies, lasting between 16 and 64 months. Meta-analysis of these 11 studies showed a significant association between a low HALP score and worse OS (HR 1.78, 95% CI 1.30–2.44; I² = 83%). For PFS, low HALP scores were associated with a pooled HR of 2.05 (95% CI: 0.78–5.39; I² = 90%), while for DFS, the HR was 1.98 (95% CI: 0.86–4.56; I² = 56%). Subgroup analyses of OS based on metastatic status showed non-significant results for four studies including only nonmetastatic NSCLC (HR: 1.67; 95% CI: 0.68–4.12; I² = 85%) and for two studies including only metastatic NSCLC (HR: 2.12; 95% CI: 0.03–144.24; I² = 88%). Sensitivity analyses confirmed the stability of the OS results. Conclusions: This meta-analysis identifies the HALP score as a significant prognostic biomarker for OS in NSCLC. However, its associations with PFS, DFS, and OS in subgroup analyses by metastatic status were not statistically significant, likely due to limited study numbers, heterogeneous designs and HALP cut-off values, and stage- or treatment-specific variations in prognostic impact. These findings underscore the need for standardization of HALP cut-off values and the implementation of prospective studies to validate its predictive value across diverse NSCLC cohorts.
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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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.050 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".