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.

Purpose: Sentinel 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 Methods: Clinicopathologic 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.

Results: Factors 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.

Conclusion: A 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.