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Journal Article
Research Support, Non-U.S. Gov't
Utility of gamma-glutamyl transpeptidase to lymphocyte count ratio in predicting prognosis of patients with oral cancer: A prospective cohort study in Southeastern China.
Head & Neck 2023 May
BACKGROUND: To assess the prognostic role of gamma-glutamyl transpeptidase to lymphocyte count ratio (GLR) and develop a prognostic nomogram for patients with oral cancer.
METHODS: A prospective cohort (n = 1011) was conducted during July 2002 to March 2021 in Southeastern China.
RESULTS: The median follow-up time was 3.5 years. Multivariate Cox regression (OS: HR = 1.51, 95% CI: 1.04, 2.18) and Fine-Gray model (DSS: HR = 1.68, 95% CI: 1.14, 2.49) both showed that high GLR could act as an indicator of poor prognosis. A nonlinear dose-response relationship was observed between continuous GLR and the risk of all-cause mortality (p for overall = 0.028, p for nonlinear = 0.048). Compare with TNM stage, time-dependent ROC curve proved that GLR-based nomogram model performs better in predicting prognosis (the area under curve for 1-, 3-, and 5-years mortality: 0.63, 0.65, and 0.64 vs. 0.76, 0.77, and 0.78, p < 0.001).
CONCLUSION: GLR might be a useful tool in predicting prognosis for patients with oral cancer.
METHODS: A prospective cohort (n = 1011) was conducted during July 2002 to March 2021 in Southeastern China.
RESULTS: The median follow-up time was 3.5 years. Multivariate Cox regression (OS: HR = 1.51, 95% CI: 1.04, 2.18) and Fine-Gray model (DSS: HR = 1.68, 95% CI: 1.14, 2.49) both showed that high GLR could act as an indicator of poor prognosis. A nonlinear dose-response relationship was observed between continuous GLR and the risk of all-cause mortality (p for overall = 0.028, p for nonlinear = 0.048). Compare with TNM stage, time-dependent ROC curve proved that GLR-based nomogram model performs better in predicting prognosis (the area under curve for 1-, 3-, and 5-years mortality: 0.63, 0.65, and 0.64 vs. 0.76, 0.77, and 0.78, p < 0.001).
CONCLUSION: GLR might be a useful tool in predicting prognosis for patients with oral cancer.
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