J Cancer 2019; 10(18):4380-4388. doi:10.7150/jca.27992

Research Paper

Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Patients with Major Salivary Gland Mucoepidermoid Carcinoma

Jia-Qian Hu1,2,*, Peng-Cheng Yu1,2,*, Xiao Shi1,2,*, Wan-Lin Liu1,2*, Ting-Ting Zhang1,2, Bo-Wen Lei1,2, Nai-Si Huang1,2, Wei-Bo Xu1,2, Li-Tao Han1,2, Ben Ma1,2, Tian Liao1,2, Wen-Jun Wei1,2, Yu Wang1,2, Zhong-Wu Lu1,2✉, Yu-Long Wang1,2✉, Qing-Hai Ji1,2 ✉

1. Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai, 200032, China
2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
*These authors have contributed equally to this work.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.
Hu JQ, Yu PC, Shi X, Liu WL, Zhang TT, Lei BW, Huang NS, Xu WB, Han LT, Ma B, Liao T, Wei WJ, Wang Y, Lu ZW, Wang YL, Ji QH. Prognostic Nomograms for Predicting Overall Survival and Cancer-Specific Survival of Patients with Major Salivary Gland Mucoepidermoid Carcinoma. J Cancer 2019; 10(18):4380-4388. doi:10.7150/jca.27992. Available from http://www.jcancer.org/v10p4380.htm

File import instruction


Background: The aim of this study was to develop and validate prognostic nomograms predicting overall (OS) and cancer-specific survival (CSS) of patients with major salivary gland (MaSG) mucoepidermoid carcinoma (MEC).

Methods: 1398 MaSG-MEC patients were identified from the Surveillance, Epidemiology and End Results (SEER) database. They were randomly and equally divided into a training cohort (n=699) and a validation cohort (n=699). The best subsets of covariates were identified to develop nomograms predicting OS and CSS based on the smallest Akaike Information Criterion (AIC) value in the multivariate Cox models. The nomograms were internally and externally validated by the bootstrap resampling method. The predictive ability was evaluated by Harrell's Concordance Index (C-index).

Results: For the training cohort, eight (age at diagnosis, tumor grade, primary site, surgery, radiation, T, N and M classification) and seven predictors (all the above factors except primary site) were selected to create the nomograms estimating the 3- and 5- year OS and CSS, respectively. C-index indicated better predictive performance of the nomograms than the 7th AJCC staging system, which was confirmed by both internal (via the training cohort: OS: 0.888 vs 0.785, CSS: 0.938 vs 0.821) and external validation (via the validation cohort: OS: 0.844 vs 0.743, CSS: 0.882 vs 0.787). The calibration plots also revealed good agreements between the nomogram-based prediction and observed survival.

Conclusions: We have proposed and validated the nomograms predicting OS and CSS of MaSG-MEC. They are proved to be of higher predictive value than the AJCC staging system and may be adopted in future clinical practice.

Keywords: Major salivary gland, Mucoepidermoid carcinoma, Nomogram, Overall survival, Cancer-specific survival, C-index.