Prognostic model for predicting overall and cancer-specific survival among patients with superficial spreading melanoma: A SEER based study.

Skin malignant melanoma is one of the most aggressive skin tumors. Superficial spreading melanoma (SSM) is the most common histological type, which can originate from different body skin sites, and some patients can still accumulate regional lymph nodes and even have distant metastasis in some cases. This study used the relevant data from the monitoring, epidemiology and results database of the National Cancer Institute database to study the overall survival (OS) and cancer-specific survival (CSS) of SSM patients and established an SSM nomogram to evaluate the prognosis of patients. A total of 13,922 patients were collected from the monitoring, epidemiology and results database of the National Cancer Institute and randomly divided into a training cohort (8353 cases) and a validation cohort (5569 cases). Univariate and multivariate Cox regression analysis were used to determine prognostic factors, and these factors were used to construct OS and CSS nomograms for patients with SSM. Finally, the discrimination and consistency of the nomogram model were evaluated by the consistency index (C-index), area under the curve (AUC) and calibration curve. Multivariate Cox regression analysis suggested that age, sex, tumor site, the American joint committee on cancer T stage and the first primary melanoma were independent predictors of OS and CSS in patients with SSM and that the American joint committee on cancer N stage was also an independent predictor of CSS in patients with SSM. Based on the above prognostic factors, this study constructed a predictive model. The C-index of the model OS and CSS for this training cohort was 0.805 [95% CI: 0.793-0.817] and 0.896 [95% CI: 0.878-0.913], respectively. The AUC values for 1-, 3-, and 5-year OS were 0.822, 0.820, and 0.821, respectively, and the AUC values for CSS were 0.914, 0.922, and 0.893, respectively. The data indicated that both nomograms showed better predictive accuracy. The calibration curves of the training cohort and the validation cohort were in good agreement. The nomogram has superior predictive performance in predicting 1-, 3-, and 5-year OS and CSS prognosis in patients with SSM and can provide a reference for individualized treatment and clinical counseling of SSM.