Development and Validation of Nomograms to Predict Risk and Prognosis in Salivary Gland Carcinoma Patient with Distant Metastases

Abstract

Background: Salivary gland carcinoma (SGC) patients with distant metastasis (DM) are rare, and understanding this disease is insufficient. Nomograms can predict the prognostic probability of patients, while few studies have examined diagnostic and prognostic factors in SGC patients with DM. The purpose of this study was to establish and validate the risk and prognostic nomograms of SGC patients with DM. Methods: Based on the SEER database, we analyzed the data of SGC patients between 2004 and 2015. Logistic regression analyses and Cox proportional hazards regression analyses were used to identify risk and prognostic factors for DM in SGC patients. Based on the Akaike information criterion (AIC) value and likelihood ratio test, the best-fitting model was selected to build risk and prognostic nomograms, and the results were evaluated by receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and Kaplan–Meier (K–M) survival curves. ROC curves were also used to compare the nomograms with the American Joint Committee on Cancer (AJCC) staging system. Results: 7418 SGC patients were included in the study, and 307 (4.14%) of them were diagnosed with DM. This study identified that there are variables (age ≥ 80, no-parotid gland primary site, histologic type of mucoepidermoid carcinoma and squamous cell carcinoma, T stage ≥ T2, N staged ≥ N1, histologic grade ≥ III, and tumor size ≥ 41 mm) associated with the occurrence of DM in SGC patients. Therefore, we constructed diagnostic and prognostic nomograms after incorporating these variables. ROC curves illustrated the better predictive efficacy of 2 nomograms over the AJCC staging system. DCA curves, calibration curves, and K-M survival curves showed that 2 nomograms can accurately predict the occurrence and prognosis of DM among SGC patients in training and validation sets. Conclusion: It was shown that the nomograms were highly discriminative in predicting the diagnosis and prognosis of SGC patients with DM, and could identify high-risk patients, thereby providing SGC patients with individualized treatment plans.