Technology in Cancer Research & Treatment (Dec 2021)

A Prognostic Model Based on Clinicopathological Features and Inflammation- and Nutrition-Related Indicators Predicts Overall Survival in Surgical Patients With Tongue Squamous Cell Carcinoma

  • Lai-Feng Wei,
  • Xu-Chun Huang,
  • Yi-Wei Lin,
  • Yun Luo,
  • Tian-Yan Ding,
  • Can-Tong Liu,
  • Ling-Yu Chu,
  • Yi-Wei Xu,
  • Yu-Hui Peng,
  • Hai-Peng Guo

DOI
https://doi.org/10.1177/15330338211043048
Journal volume & issue
Vol. 20

Abstract

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Objectives: It is reported that inflammation- and nutrition-related indicators have a prognostic impact on multiple cancers. Here we aimed to identify a prognostic nomogram model for prediction of overall survival (OS) in surgical patients with tongue squamous cell carcinoma (TSCC). Methods: The retrospective data of 172 TSCC patients were charted from the Cancer Hospital of Shantou University Medical College between 2008 and 2019. A Cox regression analysis was performed to determine prognostic factors to establish a nomogram and predict OS. The predictive accuracy of the model was analyzed by the calibration curves and the concordance index (C-index). The difference of OS was analyzed by Kaplan–Meier survival analysis. Results: Multivariate analysis showed age, tumor node metastasis (TNM) stage, red blood cell, platelets, and platelet-to-lymphocyte ratio were independent prognostic factors for OS, which were used to build the prognostic nomogram model. The C-index of the model for OS was 0.794 (95% CI = 0.729-0.860), which was higher than that of TNM stage 0.685 (95% CI = 0.605-0.765). In addition, decision curve analysis also showed the nomogram model had improved predictive accuracy and discriminatory performance for OS, compared to the TNM stage. According to the prognostic model risk score, patients in the high-risk subgroup had a lower 5-year OS rate than that in a low-risk subgroup (23% vs 49%, P < .0001). Conclusions: The nomogram model based on clinicopathological features inflammation- and nutrition-related indicators represents a promising tool that might complement the TNM stage in the prognosis of TSCC.