BACKGROUND: Adenoid cystic carcinoma (ACC) is a relatively uncommon tumor. The existing prediction model is limited to the head and neck. We aim to construct a prognostic nomogram combined with the clinical features and treatment options of ACC to predict the disease-specific survival (DSS) of patients diagnosed with ACC in different anatomic sites. METHODS: A novel predictive model was constructed using 1285 patients with ACC from the Surveillance, Epidemiology, and End Results (SEER) registry between 2010 and 2015. The performance of this model was externally validated using 118 patients with ACC in the West China Hospital, Sichuan University between 2010 and 2017. RESULTS: The prognostic model demonstrated that age, primary site, lymph node metastasis, distant metastasis, radiotherapy and surgery were independent factors for DSS. The validation of the model using an external cohort proved its reliability. CONCLUSION: The developed novel predictive model is shown to provide accurate and efficient predictive information for patients with ACC for different anatomic sites.
BACKGROUND:Adenoid cystic carcinoma (ACC) is a relatively uncommon tumor. The existing prediction model is limited to the head and neck. We aim to construct a prognostic nomogram combined with the clinical features and treatment options of ACC to predict the disease-specific survival (DSS) of patients diagnosed with ACC in different anatomic sites. METHODS: A novel predictive model was constructed using 1285 patients with ACC from the Surveillance, Epidemiology, and End Results (SEER) registry between 2010 and 2015. The performance of this model was externally validated using 118 patients with ACC in the West China Hospital, Sichuan University between 2010 and 2017. RESULTS: The prognostic model demonstrated that age, primary site, lymph node metastasis, distant metastasis, radiotherapy and surgery were independent factors for DSS. The validation of the model using an external cohort proved its reliability. CONCLUSION: The developed novel predictive model is shown to provide accurate and efficient predictive information for patients with ACC for different anatomic sites.