The role of sentinel node tumor burden in modeling the prognosis of melanoma patients with positive sentinel node biopsy: an Italian melanoma intergroup study (N=2,086)

Background The management of melanoma patients with metastatic melanoma in the sentinel nodes (SN) is evolving based on the results of trials questioning the impact of completion lymph node dissection (CLND) and demonstrating the efficacy of new adjuvant treatments. In this landscape, new prognostic tools for fine risk stratification are eagerly sought to optimize the therapeutic path of these patients. Methods A retrospective cohort of 2,086 patients treated with CLND after a positive SN biopsy in thirteen Italian Melanoma Centers was reviewed. Overall survival (OS) was the outcome of interest; included independent variables were the following: age, gender, primary melanoma site, Breslow thickness, ulceration, sentinel node tumor burden (SNTB), number of positive SN, non-sentinel lymph nodes (NSN) status. Univariate and multivariate survival analyses were performed using the Cox proportional hazard regression model. Results The 3-year, 5-year and 10-year OS rates were 79%, 70% and 54%, respectively. At univariate analysis, all variables, except for primary melanoma body site, were found to be statistically significant prognostic factors. Multivariate Cox regression analysis indicated that older age (P < 0.0001), male gender (P = 0.04), increasing Breslow thickness (P < 0.0001), presence of ulceration (P = 0.004), SNTB size (P < 0.0001) and metastatic NSN (P < 0.0001) were independent negative predictors of OS. Conclusion The above results were utilized to build a nomogram in order to ease the practical implementation of our prognostic model, which might improve treatment personalization.

Automated summary

This study reviewed a cohort of 2,086 melanoma patients who underwent CLND after a positive SN biopsy, and identified age, gender, Breslow thickness, ulceration, SNTB size, and metastatic NSN as independent negative predictors of OS. The results were used to build a prognostic model for personalized treatment.