Li Xiancai, Hu Mingbin, Gu Weiguo, Liu Dewu, Mei Jinhong, Chen Shaoqing
Department of Oncology, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Department of Burn, The First Affiliated Hospital of Nanchang University, Nanchang, China.
Front Oncol. 2021 Oct 13;11:698870. doi: 10.3389/fonc.2021.698870. eCollection 2021.
Multiple factors have been shown to be tied to the prognosis of individuals with parotid cancer (PC); however, there are limited numbers of reliable as well as straightforward tools available for clinical estimation of individualized mortality. Here, a competing risk nomogram was established to assess the risk of cancer-specific deaths (CSD) in individuals with PC.
Data of PC patients analyzed in this work were retrieved from the Surveillance, Epidemiology, and End Results (SEER) data repository and the First Affiliated Hospital of Nanchang University (China). Univariate Lasso regression coupled with multivariate Cox assessments were adopted to explore the predictive factors influencing CSD. The cumulative incidence function (CIF) coupled with the Fine-Gray proportional hazards model was employed to determine the risk indicators tied to CSD as per the univariate, as well as multivariate analyses conducted in the R software. Finally, we created and validated a nomogram to forecast the 3- and 5-year CSD likelihood.
Overall, 1,467 PC patients were identified from the SEER data repository, with the 3- and 5-year CSD CIF after diagnosis being 21.4% and 24.1%, respectively. The univariate along with the Lasso regression data revealed that nine independent risk factors were tied to CSD in the test dataset ( = 1,035) retrieved from the SEER data repository. Additionally, multivariate data of Fine-Gray proportional subdistribution hazards model illustrated that N stage, Age, T stage, Histologic, M stage, grade, surgery, and radiation were independent risk factors influencing CSD in an individual with PC in the test dataset ( < 0.05). Based on optimization performed using the Bayesian information criterion (BIC), six variables were incorporated in the prognostic nomogram. In the internal SEER data repository verification dataset ( = 432) and the external medical center verification dataset ( = 473), our nomogram was well calibrated and exhibited considerable estimation efficiency.
The competing risk nomogram presented here can be used for assessing cancer-specific mortality in PC patients.
多项因素已被证明与腮腺癌（PC）患者的预后相关；然而，用于临床评估个体死亡风险的可靠且简便的工具数量有限。在此，我们建立了一个竞争风险列线图来评估PC患者的癌症特异性死亡（CSD）风险。
本研究分析的PC患者数据来自监测、流行病学和最终结果（SEER）数据库以及南昌大学第一附属医院（中国）。采用单变量套索回归和多变量Cox评估来探索影响CSD的预测因素。在R软件中，结合Fine-Gray比例风险模型的累积发病率函数（CIF）用于根据单变量和多变量分析确定与CSD相关的风险指标。最后，我们创建并验证了一个列线图，以预测3年和5年CSD的可能性。
总体而言，从SEER数据库中识别出1467例PC患者，诊断后的3年和5年CSD的CIF分别为21.4%和24.1%。单变量和套索回归数据显示，从SEER数据库检索的测试数据集（n = 1035）中有九个独立风险因素与CSD相关。此外，Fine-Gray比例子分布风险模型的多变量数据表明，在测试数据集中，N分期、年龄、T分期、组织学类型、M分期、分级、手术和放疗是影响PC患者CSD的独立风险因素（P < 0.05）。基于使用贝叶斯信息准则（BIC）进行的优化，六个变量被纳入预后列线图。在内部SEER数据库验证数据集（n = 432）和外部医疗中心验证数据集（n = 473）中，我们的列线图校准良好，并表现出相当高的估计效率。
本文提出的竞争风险列线图可用于评估PC患者的癌症特异性死亡率。
