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 Aug 1:15:1505485.
doi: 10.3389/fonc.2025.1505485. eCollection 2025.

Development and validation of a Comprehensive Hematological Scoring System for predicting overall survival in patients with soft tissue sarcomas: a comparison with NLR and PLR

Ying Qiu  1 , Jiquan Liu  1 , Shuang Chai  1 , Lili Liu  1   2 , Longqing Li  2 , Yan Zhang  2
Affiliations

Development and validation of a Comprehensive Hematological Scoring System for predicting overall survival in patients with soft tissue sarcomas: a comparison with NLR and PLR

Ying Qiu et al. Front Oncol. .

Abstract

Background: Soft tissue sarcomas (STS) are rare malignancies with high relapse/metastasis risks and limited treatment efficacy. Current biomarkers like neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) lack comprehensive prognostic value due to their reliance on limited hematological parameters.

Methods: This retrospective study analyzed 206 STS patients (2016-2023) to develop a Composite Hematological Scoring System (CHSS) integrating 19 pretreatment markers. LASSO regression selected key variables (glucose, CRP, LDL-C, HDL-C, albumin, platelets, hemoglobin, lymphocytes), weighted by coefficients. CHSS's prognostic performance was compared to NLR/PLR via Kaplan-Meier, time-dependent ROC, and Cox regression analyses. A nomogram combining CHSS with clinical variables was validated using C-index, calibration, and decision curves.

Results: CHSS outperformed NLR/PLR in predicting overall survival (OS) across all timepoints. High CHSS patients had significantly worse OS (HR=6.197, P<0.001). Multivariate analysis confirmed CHSS, age, tumor size, and FNCLCC grade as independent predictors. The CHSS-based nomogram achieved a C-index of 0.79, with accurate 3-/5-year OS calibration.

Conclusion: CHSS integrates inflammation, metabolism, and nutrition markers to provide superior prognostic stratification for STS patients compared to NLR/PLR. Its integration into a nomogram supports personalized management, though multicenter validation is needed.

Keywords: Comprehensive Hematological Scoring System (CHSS); neutrophil-to-lymphocyte ratio (NLR); platelet-to-lymphocyte ratio (PLR); prognosis; soft tissue sarcoma (STS).

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
(A) The optimal cutoff value of the CHSS score. The KM survival curves show overall survival in patients grouped by (B) CHSS, (C) NLR, and (D) PLR.
Figure 2
Figure 2
(A) The time-dependent ROC curves demonstrate the predictive abilities of different biomarkers; (B) The forest plot illustrates the predictive ability of CHSS across different subgroups; (C) The forest plot illustrates the predictive ability of NLR across different subgroups; (D) The forest plot illustrates the predictive ability of PLR across different subgroups.
Figure 3
Figure 3
(A) The forest plot shows the univariate analysis results of CHSS and clinical variables; (B) The forest plot shows the multivariate analysis results of CHSS and clinical variables; (C) The time-dependent ROC curve demonstrates the predictive ability of independent prognostic factors.
Figure 4
Figure 4
(A) The nomogram predicting overall survival in soft tissue sarcoma patients based on independent risk factors; (B) The calibration curve of the nomogram; (C) The net benefit curve of the nomogram; (D) The net reduction curve of the nomogram.
See this image and copyright information in PMC

References

    1. Hoefkens F, Dehandschutter C, Somville J, Meijnders P, Van Gestel D. Soft tissue sarcoma of the extremities: pending questions on surgery and radiotherapy. Radiat Oncol (London England). (2016) 11:136. doi: 10.1186/s13014-016-0668-9, PMID: - DOI - PMC - PubMed
    1. Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA: A Cancer J clinicians. (2023) 73:17–48. doi: 10.3322/caac.21763, PMID: - DOI - PubMed
    1. Fornaciari G. Histology of ancient soft tissue tumors: A review. Int J paleopathology. (2018) 21:64–76. doi: 10.1016/j.ijpp.2017.02.007, PMID: - DOI - PubMed
    1. Mastrangelo G, Coindre JM, Ducimetière F, Dei Tos AP, Fadda E, Blay JY, et al. Incidence of soft tissue sarcoma and beyond: a population-based prospective study in 3 European regions. Cancer. (2012) 118:5339–48. doi: 10.1002/cncr.27555, PMID: - DOI - PubMed
    1. Lindberg RD, Martin RG, Romsdahl MM, Barkley HT, Jr. Conservative surgery and postoperative radiotherapy in 300 adults with soft-tissue sarcomas. Cancer. (1981) 47:2391–7. doi: 10.1002/1097-0142(19810515)47:10<2391::AID-CNCR2820471012>3.0.CO;2-B, PMID: - DOI - PubMed

LinkOut - more resources