背景:鼻窦粘膜黑色素瘤(SNMM)是一种相对罕见的恶性肿瘤,预后较差。这项研究旨在确定预后因素并建立列线图模型来预测SNMM患者的总体生存(OS)。
方法:从监测中选择459例SNMM患者,流行病学,和最终结果(SEER)数据库作为训练队列。使用单变量和多变量Cox回归分析筛选与患者预后相关的独立因素并建立列线图模型。此外,我们对北京协和医院的34例SNMM患者进行了外部验证,以评估列线图的有效性.
结果:来自SEER数据库的队列中的中位OS为28个月。1年,3年和5年OS率为69.8%,40.4%,和30.0%,分别。多因素Cox回归分析表明,年龄,T级,N级,手术和放疗是与OS相关的独立变量.用于预测1-的列线图的接收器工作特征曲线(AUC)下的面积,3年和5年OS分别为0.78、0.71和0.71,在训练队列中。在验证队列中,用于预测1-的列线图的曲线下面积(AUC),3年和5年OS分别为0.90、0.75和0.78。根据列线图的总分将患者分为低风险组和高风险组。在训练队列(P<0.0001)和验证队列(P=0.0016)中,低风险组患者的生存预后明显优于高风险组患者。
结论:我们建立并验证了一个新的列线图模型来预测按年龄分层的SNMM患者的OS,T级,N级,手术和放疗。这种预测工具在患者咨询和临床决策领域具有潜在的重要性。
BACKGROUND: Sinonasal mucosal melanoma (SNMM) is a relatively rare malignant tumour with a poor prognosis. This study was designed to identify prognostic factors and establish a nomogram model to predict the overall survival (OS) of patients with SNMM.
METHODS: A total of 459 patients with SNMM were selected from the Surveillance, Epidemiology, and End Results (SEER) database as the training cohort. Univariate and multivariate Cox regression analyses were used to screen for independent factors associated with patient prognosis and develop the nomogram model. In addition, external validation was performed to evaluate the effectiveness of the nomogram with a cohort of 34 patients with SNMM from Peking Union Medical College Hospital.
RESULTS: The median OS in the cohort from the SEER database was 28 months. The 1-year, 3-year and 5-year OS rates were 69.8%, 40.4%, and 30.0%, respectively. Multivariate Cox regression analysis indicated that age, T stage, N stage, surgery and radiotherapy were independent variables associated with OS. The areas under the receiver operating characteristic curves (AUCs) of the nomograms for predicting 1-, 3- and 5-year OS were 0.78, 0.71 and 0.71, respectively, in the training cohort. In the validation cohort, the area under the curve (AUC) of the nomogram for predicting 1-, 3- and 5-year OS were 0.90, 0.75 and 0.78, respectively. Patients were classified into low- and high-risk groups based on the total score of the nomogram. Patients in the low-risk group had a significantly better survival prognosis than patients in the high-risk group in both the training cohort (P < 0.0001) and the validation cohort (P = 0.0016).
CONCLUSIONS: We established and validated a novel nomogram model to predict the OS of SNMM patients stratified by age, T stage, N stage, surgery and radiotherapy. This predictive tool is of potential importance in the realms of patient counselling and clinical decision-making.