SEER-based survival nomogram for salivary acinar cell carcinoma using clinical and surgical factors

Acinar cell carcinoma (AcCC) has a certain risk of recurrence, metastasis or even death. This study aimed to explore the relationships between clinicopathological characteristics and survival in AcCC patients, and a nomogram model was developed and validated for predicting overall survival (OS). AcCC patients were identified from the Surveillance, Epidemiology, and End Results Program. An external validation was conducted using an independent cohort from our hospital. Independent prognostic factors for OS were determined using univariate/multivariate Cox regression analyses, and a nomogram was created to predict survival. The model was validated using various methods, including calibration curves, receiver operating characteristic curves, and concordance indexes. A total of 1306 patients (916 in the training set and 390 in the validation set) with AcCC were enrolled. The results of multivariate Cox regression analysis revealed that age, sex, advanced T stage, N stage, M stage, and the type of surgery were independent prognostic factors for OS. The established nomograms incorporating the clinical factors and surgery type had robust and accurate performance according to the concordance indexes (0.824) and area under the curve values of 0.864 and 0.829, respectively, in predicting 3-year survival and 5-year survival in the training set. Receiver operating characteristic curve also showed better prognostic prediction performance for OS in the internal and external validation group. Moreover, the calibration curves exhibited excellent agreement between the actual observations and nomogram predictions. The OS of high-risk patients exhibited worse than that of low-risk patients in Kaplan-Meier survival analysis. A nomogram based on clinical features and surgery was developed for the first time and validated to predict personalized 3- and 5-year OS in AcCC patients. It helps clinicians predict survival and obtain prognostic information. Integrating this nomogram into clinical practice could improve decision-making, optimize therapy, and enhance patient outcomes.