The prognostic role of inflammation-scores on overall survival in lung cancer patients

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Sandfeld-Paulsen, Birgitte;Meldgaard, Peter;Boe S. Sorensen;Safwat, Akmal;Aggerholm-Pedersen, Ninna

Description

Objective: Inflammation has been validated as a host-related prognostic marker in cancer. The Glasgow Prognostic score (GPS) and neutrophil-to-lymphocyte ratio (NLR) are suggested measures of inflammation. However, the allocation of patients has been questioned. Hence, optimized inflammation-scores has been developed, such as the combined NLR and GPS (CNG) system, and the Aarhus composite biomarker score (ACBS). So far, these optimized inflammation-scores have not been validated in lung cancer patients. We evaluated if the optimized inflammation-scores were prognostic markers of inferior survival in lung cancer patients. Furthermore, we tested which of the optimized inflammation-scores led to better patient-allocation. Material and methods: The cohort of this prospective study composed of 275 non-small cell lung cancer patients. We evaluated pre-diagnostic serum biomarkers for GPR, NLR, platelet-to-lymphocyte ratio as well as the optimized inflammation-scores CNG and ABCS as predictors of overall survival (OS), and we examined the patient-allocation derived from each inflammation-score. Results: Each of the evaluated inflammation-scores could predict the overall survival even when adjustments were made for comorbidity and clinicopathological characteristics. When comparing the scores, the optimized inflammation-scores CNG and ACBS led to a better and more balanced patient-allocation. In the early clinical stages I & II, the optimized scores could reveal a subgroup of patients with poorer survival that is similar to stage III. Conclusion: In this cohort of lung cancer patients, we demonstrate that inflammation-scores are prognostic markers of inferior survival. Furthermore, we demonstrate that the optimized inflammation-scores CNG and ACBS lead to better patient-allocation independently of the clinicopathological characteristics and comorbidity.

Citations (1)

Mentions (0)

Metrics

Dataset Index

0.7

FAIR Score

85%

Citations

1

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Taylor & Francis

Assigned Domain

Subfield

Oncology

Field

Medicine

Domain

Health Sciences

Confidence Score

70%

Source

Scholar Data Model

Keywords

MedicineCell BiologyBiotechnologyImmunologyFOS: Clinical medicine19999 Mathematical Sciences not elsewhere classifiedFOS: MathematicsCancer

Normalization Factors

FT

13.46

CTw

1.00

MTw

1.00