Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Apr 21;8(4):e70713.
doi: 10.1002/hsr2.70713. eCollection 2025 Apr.

The Predictive Significance of Various Prognostic Scoring Systems on the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer Patients: A Retrospective Study

Jianying Liu  1 , Jinzhan Chen  1 , Congyi Xie  1 , Haiyan Chen  1 , Shuwen Yang  1 , Hongni Jiang  1   2 , Yijiao Xu  1
Affiliations

The Predictive Significance of Various Prognostic Scoring Systems on the Efficacy of Immunotherapy in Non-Small Cell Lung Cancer Patients: A Retrospective Study

Jianying Liu et al. Health Sci Rep. .

Abstract

Background and aims: To evaluate the efficacy and prognostic value of various immunoprognostic scoring systems-Lung Immune Prognostic Index (LIPI), modified Glasgow Prognostic Score (mGPS), and Gustave Roussy Immune Score (GRIm)-in non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs).

Methods: We conducted a retrospective analysis of clinical data from 219 NSCLC patients treated with ICIs at Zhongshan Hospital (Xiamen), Fudan University from June 1, 2019 to January 31, 2024. The therapeutic efficacy and predictive capabilities of the scoring systems were assessed using Kaplan-Meier curves, Cox proportional hazards models, time-dependent ROC curves, and random survival forest models.

Results: The median follow-up was 29 months (IQR: 25.96-32.04), resulting in 93 observed deaths. Both LIPI and GRIm scores correlated with declining overall survival (OS) and progression-free survival (PFS) as risk levels increased. LIPI demonstrated superior predictive performance at 12, 24, and 36 months (AUC: 0.70, 0.62, 0.61, respectively). Multivariate analysis identified immune-related adverse events (irAEs) and lactate dehydrogenase (LDH) levels as independent prognostic factors for OS.

Conclusion: LIPI serves as an effective prognostic tool for NSCLC patients receiving immunotherapy, outperforming individual inflammatory markers. Additionally, irAEs and LDH levels are significant independent prognostic factors for OS.

Keywords: Gustave Roussy Immune Score; Lung Immune Prognostic Index; immune checkpoint inhibitors; modified Glasgow Prognostic Score; non‐small cell lung cancer; prognostic analysis.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Kaplan–Meier survival curves of overall survival (OS) and progression‐free survival (PFS) stratified by three prognostic scoring systems: LIPI, mGPS, and GRIm. (a) OS among patients stratified by the LIPI model; (b) PFS among patients stratified by the LIPI model; (c) OS among patients stratified by the mGPS model; (d) PFS among patients stratified by the mGPS model; (e) OS among patients stratified by the GRIm model; (f) PFS among patients stratified by the GRIm model.
Figure 2
Figure 2
Time‐dependent receiver operating characteristic (ROC) curves for predicting overall survival at 12, 24, and 36 months based on PD‐L1 expression and three prognostic scoring systems (LIPI, mGPS, and GRIm). (a) Time‐dependent ROC curve at 12 months; (b) time‐dependent ROC curve at 24 months; and (c) time‐dependent ROC curve at 36 months.
Figure 3
Figure 3
Feature importance ranking based on the random survival forest model. The figure shows the relative prognostic importance of four factors (LIPI, mGPS, PD‐L1 expression, and GRIm score) using permutation importance in the RSF model. LIPI demonstrated the highest predictive importance for survival outcomes among the four indicators, followed by mGPS and PD‐L1. Permutation importance was calculated as the decrease in model performance (mean squared error) after randomly permuting each feature. Higher values indicate greater importance for the survival prediction.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Wang M., Herbst R. S., and Boshoff C., “Toward Personalized Treatment Approaches for Non‐Small‐Cell Lung Cancer,” Nature Medicine 27, no. 8 (2021): 1345–1356. - PubMed
    1. Sung H., Ferlay J., Siegel R. L., et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 71, no. 3 (2021): 209–249. - PubMed
    1. Osmani L., Askin F., Gabrielson E., and Li Q. K., “Current WHO Guidelines and the Critical Role of Immunohistochemical Markers in the Subclassification of Non‐Small Cell Lung Carcinoma (NSCLC): Moving From Targeted Therapy to Immunotherapy,” Seminars in Cancer Biology 52 (2018): 103–109. - PMC - PubMed
    1. Hoy H., Lynch T., and Beck M., “Surgical Treatment of Lung Cancer,” Critical Care Nursing Clinics of North America 31, no. 3 (2019): 303–313. - PubMed
    1. Reck M., Remon J., and Hellmann M. D., “First‐Line Immunotherapy for Non‐Small‐Cell Lung Cancer,” Journal of Clinical Oncology 40, no. 6 (2022): 586–597. - PubMed

LinkOut - more resources