A novel postoperative nomogram and risk classification system for individualized estimation of survival among patients with parotid gland carcinoma after surgery.

Abstract

Parotid gland carcinoma (PGC) is a rare but aggressive head and neck cancer, and the prognostic model associated with survival after surgical resection has not yet been established. This study aimed to construct a novel postoperative nomogram and risk classification system for the individualized prediction of overall survival (OS) among patients with resected PGC. Patients with PGC who underwent surgery between 2004 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database were randomized into training and validation cohorts (7:3). A nomogram developed using independent prognostic factors based on the results of the multivariate Cox regression analysis. Harrell's concordance index (C-index), time-dependent area under the curve (AUC), and calibration plots were used to validate the performance of the nomogram. Moreover, decision curve analysis (DCA) was performed to compare the clinical use of the nomogram with that of traditional TNM staging. In this study, 5077 patients who underwent surgery for PGC were included. Age, sex, marital status, tumor grade, histology, TNM stage, surgery type, radiotherapy, and chemotherapy were independent prognostic factors. Based on these independent factors, a postoperative nomogram was developed. The C-index of the proposed nomogram was 0.807 (95% confidence interval 0.797-0.817). Meanwhile, the time-dependent AUC (> 0.8) indicated that the nomogram had a satisfactory discriminative ability. The calibration curves showed good concordance between the predicted and actual probabilities of OS, and DCA curves indicated that the nomogram had a better clinical application value than the traditional TNM staging. Moreover, a risk classification system was built that could perfectly classify patients with PGC into three risk groups. This study constructed a novel postoperative nomogram and corresponding risk classification system to predict the OS of patients with PGC after surgery. These tools can be used to stratify patients with high or low risk of mortality and provide high-risk patients with more directed therapies and closer follow-up.