Nomogram for predicting the survival rate of primary pulmonary mucoepidermoid carcinoma patients: a retrospective study based on SEER database

Guo Lin; Hengrui Liang; Wei Wang; Jun Liu; Jianfu Li; Wenhua Liang; Jianxing He

doi:10.21037/atm-20-6555

Nomogram for predicting the survival rate of primary pulmonary mucoepidermoid carcinoma patients: a retrospective study based on SEER database

Ann Transl Med. 2021 Mar;9(5):407. doi: 10.21037/atm-20-6555.

Authors

Guo Lin^{1

2}, Hengrui Liang¹, Wei Wang¹, Jun Liu¹, Jianfu Li¹, Wenhua Liang¹, Jianxing He¹

Affiliations

¹ 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.
² Guangzhou Medical University, Guangzhou, China.

Abstract

Background: 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.

Methods: 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.

Results: 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).

Conclusions: We built OS (CSS) nomograms that can accurately estimate individualized survival time and identify the risk classification of PMEC.

Keywords: Cancer-specific survival (CSS); Nomogram; Overall survival; Pulmonary mucoepidermoid carcinoma (PMEC); Surveillance, Epidemiology, and End Results (SEER).