Abstract
The aim of this study was to construct and validate a nomogram to predict the 1-, 3- and 5-year overall survival (OS) in external ear melanoma (EEM) patients in the elderly based on the Surveillance, Epidemiology, and End Results (SEER) database. The information of patients diagnosed with EEM in the elderly between 2010 and 2014 was downloaded from the SEER database. Univariable and multivariable Cox analyses were carried out to identify the independent characteristics, and the independent factors were further included to construct a nomogram. The discriminative ability and calibration of the nomogram to predict OS were tested using C-index value, and calibration plots. Based on the risk score of the nomogram, the patients were divided into high- and low-risk subgroup. Finally, the survival differences of different subgroups were explored by Kaplan-Meier curves. All statistical analyses were performed by R 4.2.0. A total of 710 elderly EMM patients were included and randomly divided into training cohort and validation cohort. Univariable Cox regression were used to identify age, race, sex, American Joint Committee on Cancer (AJCC), T, surgery, radiation, chemotherapy, and tumor size as independent risk factors. Then, multivariable Cox model to determine significant risk factors was used to establish the selected factors. A nomogram for predicting the 1-, 3- and 5-year OS was constructed using the independent variables including age, AJCC, T, surgery and chemotherapy. The C-index values were 0.78 (95% CI 0.75-0.81) in training set and 0.72 (95% CI 0.66-0.78) in validation set. The calibration curves were closer to ideal curves indicated the accurate predictive ability of this nomogram. The elderly patients with EEM in the low-risk group showed a longer OS than patients in the high-risk group in both training and validation cohorts. Our study established and validated a novel model to predict 1-, 3- and 5-year OS for EEM. The individualized nomogram has a good prognostic ability and can be used as a new survival prediction tool for the elderly patients with EMM.
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