Non-Sentinel Node Risk Score (N-SNORE): A Scoring System for Accurately Stratifying Risk of Non-Sentinel Node Positivity in Patients With Cutaneous Melanoma With Positive Sentinel Lymph Nodes

Abstract

PurposeSentinel node (SN) biopsy allows identification of patients with melanoma at risk of further metastatic disease in regional non-sentinel nodes (NSN). We investigated clinicopathologic factors that predict NSN positivity in an attempt to identify patients who may be safely spared completion lymph node dissection (CLND).Patients and MethodsClinicopathologic factors previously shown to be predictive of NSN positivity were analyzed in 409 patients with SN-positive disease (309 of whom underwent CLND) managed at a single melanoma center. A weighted score Non-Sentinel Node Risk Score [N-SNORE] incorporating predictive factors was derived, and the efficacy of N-SNORE at stratifying risk of NSN involvement was studied.ResultsFactors independently predictive of NSN positivity included primary tumor regression, proportion of harvested SNs involved by melanoma (%PosSN), sex (trend), and SN tumor burden indices (maximum size of largest deposit [MaxSize], % cross-sectional area of SN occupied by tumor, tumor penetrative depth, intranodal location of tumor) and perinodal lymphatic invasion (PLI). Of SN tumor burden criteria, MaxSize was the strongest predictor. N-SNORE was the sum of scores for five parameters: sex (female = 0, male = 1), regression (absent = 0, present = 2), %PosSN (absent = 0, present = 2), MaxSize (≤ 0.5 mm = 0, 0.51 to 2.00 mm = 1, 2.01 to 10.00 mm = 2, > 10.00 mm = 3), and PLI (absent = 0, present = 3). N-SNOREs of 0, 1 to 3, 4 to 5, 6 to 7, and ≥ 8 were associated with very low (0%), low (5% to 10%), intermediate (15% to 20%), high (40% to 50%), and very high (70% to 80%) risks of NSN involvement.ConclusionA weighted score (N-SNORE) based on clinicopathologic characteristics accurately stratifies risk of NSN involvement in patients with melanoma. If validated in future studies, N-SNORE will better predict prognosis, aid in management decisions, and stratify patient groups for entry into clinical trials.