Prognostic nomograms based on immune scores for head-neck squamous cell carcinoma patients
Wenfei Li1, Kun Zhao2, Zhanqiu Wang3
1Department of Radiology, First Hospital of Qinhuangdao, Qinhuangdao, 066000, Hebei, China.
Purpose:
We aim to develop an immune-score nomograms for predicting overall survival (OS) of patients with HNSCC and assess the association of immune scores with prognosis.
Methods:
The data of 530 patients used in this study were retrieved from The Cancer Genome Atlas database. The optimization cut-point for immune scores was expressed by X-tile 3.6.1 tool. Possible prognostic factors from univariate Cox analysis were further included in a multivariate Cox proportional hazards analysis to obtain significant risk factors. Prognostic nomograms were constructed based on the factors of significant multivariate prognostic using R version 3.5.1. A calibration map was generated by comparing the nomogram prediction probability and the observation for the 3-year and 5-year OS rates.
Results:
We retrospectively analyzed 462 patients downloaded from TCGA dataset. Prognostic nomograms was integrated following risk factors of significant multivariate prognostic, such as age, angiolymphaic invasion (AI), perineura invasion(Per_invasion),tumor site, immune score, tumor-node-metastasis(TNM) stage. The concordance Index (C-index) for OS predictions was 0.723 (95% CI 0.671-0.785). Moreover, we compared the powerful efficiency of the nomograms with that of the TNM staging system. OS prediction determined on immune score set compared with the TNM staging with C-index = 0.723 vs 0.612. The calibration curves for the probability of OS of 3-year or 5-year showed no deviations between the prediction by nomograms and actual reference line.
Conclusion:
The present study indicate that high and intermediate immune scores are as independent prognostic variables for OS of head-neck squamous cell carcinoma patients. We constructed novel nomograms may has the potential to provide individualized survival risk assessments and guide treatment decisions.
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