Lin Guo, Liang Hengrui, Wang Wei, Liu Jun, Li Jianfu, Liang Wenhua, He Jianxing
Department of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou, China.
Guangzhou Medical University, Guangzhou, China.
Ann Transl Med. 2021 Mar;9(5):407. doi: 10.21037/atm-20-6555.
Primary pulmonary mucoepidermoid carcinoma (PMEC) is a rare malignant tumor, and the clinical manifestations lack specificity. The study evaluates the prognostic factors and constructs a practicable nomogram to estimate the individualized survival status for PMEC patients.
Surveillance, Epidemiology, and End Results (SEER) database was used to selected eligible patients between 1975 and 2016. The baseline characteristics including age, sex, race, marital status, tumor stage, differentiated degree, tumor laterality, primary tumor site, tumor size, lymph node metastases status, distant metastases status, surgery, chemotherapy, and radiation. We identified independent variables to build 3-, 5-, 10-year overall survival (OS) and cancer-specific survival (CSS) nomograms by univariate and multivariate analyses.
A total of 438 PMEC patients met our selection criteria. In multivariate analysis, age, tumor stage, differentiated grade, tumor size, lymph node metastases status, distant metastases status, surgery and radiation were involved in the nomogram. The C-index (0.887 (95% CI: 0.863-0.911), calibrate plots and ROC curves (AUC =0.941, 0.951, 0.935 for 3-, 5-, 10-year OS, respectively) indicated the satisfied accuracy and practicability of our nomograms. Compared to TNM system, our model also showed a superior prediction (IDI =0.167, 0.171, 0.172, P<0.001).
We built OS (CSS) nomograms that can accurately estimate individualized survival time and identify the risk classification of PMEC.
原发性肺黏液表皮样癌（PMEC）是一种罕见的恶性肿瘤，临床表现缺乏特异性。本研究评估其预后因素并构建一个可行的列线图，以估计PMEC患者的个体生存状况。
利用监测、流行病学和最终结果（SEER）数据库选取1975年至2016年间符合条件的患者。记录基线特征，包括年龄、性别、种族、婚姻状况、肿瘤分期、分化程度、肿瘤位置、原发肿瘤部位、肿瘤大小、淋巴结转移状态、远处转移状态、手术、化疗和放疗情况。通过单因素和多因素分析确定独立变量，构建3年、5年、10年总生存（OS）和癌症特异性生存（CSS）列线图。
共有438例PMEC患者符合入选标准。多因素分析显示，年龄、肿瘤分期、分化程度、肿瘤大小、淋巴结转移状态、远处转移状态、手术和放疗被纳入列线图。C指数（0.887（95%CI：0.863 - 0.911））、校准图和ROC曲线（3年、5年、10年OS的AUC分别为0.941、0.951、0.935）表明列线图具有满意的准确性和实用性。与TNM系统相比，我们的模型也显示出更好的预测能力（IDI = 0.167、0.171、0.172，P < 0.001）。
我们构建的OS（CSS）列线图能够准确估计个体生存时间，并识别PMEC的风险分类。
