Utility of a Model for Predicting the Risk of Sentinel Lymph Node Metastasis in Patients With Cutaneous Melanoma

• Published in: JAMA dermatology (Chicago, Ill.) Vol. 158; no. 6; p. 680
• Main Authors: Marchetti, Michael A, Dusza, Stephen W, Bartlett, Edmund K
• Format: Journal Article
• Language: English
• Published: United States 01.06.2022
• Subjects: Humans; Lymphadenopathy - pathology; Lymphatic Metastasis - pathology; Melanoma - pathology; Melanoma, Cutaneous Malignant; Prognosis; Retrospective Studies; Risk Factors; Sentinel Lymph Node - pathology; Sentinel Lymph Node Biopsy; Skin Neoplasms - pathology
• ISSN: 2168-6084
• DOI: 10.1001/jamadermatol.2022.0970

A neural network-based model (i31-GEP-SLNB) that uses clinicopathologic factors (thickness, mitoses, ulceration, patient age) plus molecular analysis (31-gene expression profiling) has become commercially available to guide selection for sentinel lymph node (SLN) biopsy in cutaneous melanoma, but its clinical utility is not well characterized. To determine if use of the i31-GEP-SLNB model is associated with clinical benefit when used to select patients for SLN biopsy. This decision-analytic study used data derived from a published external validation study of the i31-GEP-SLNB prediction model. Participants included patients with primary cutaneous melanoma. The primary outcome was the net benefit associated with using the i31-GEP-SLNB model for SLN biopsy selection compared with other selection strategies (SLN biopsy for all patients and SLN biopsy for no patients) at a 5% risk threshold. Analyses were stratified by American Joint Committee on Cancer (AJCC) T category. The reduction in the number of avoidable SLN biopsies and relative utility were also calculated. Compared with other SLN biopsy selection strategies, use of the i31-GEP-SLNB model had greater net benefit for patients with T1b (+0.012), T2a (+0.002), and T2b melanoma (+0.002) but not for those with high-risk T1a (-0.003) disease. The improvement in relative utility was +22% in patients with T1b, +1% in T2a, and +2% in T2b melanoma. Compared with SLN biopsy for all patients, use of the model would equate to a 23% decrease in SLN biopsies among patients with T1b disease without an SLN metastasis with no increase in the number of patients with an SLN metastasis left untreated; among patients with T2a and T2b melanoma, the net decrease in avoidable biopsies compared with SLN biopsy for all was 3% and 4%, respectively. The findings of this decision-analytic study suggest that i31-GEP SLNB has significant potential for risk-stratifying patients with T1b melanoma if using a 5% risk threshold; its role among patients with T1a and T2 melanoma or using other risk thresholds requires further study. A prospective validation study confirming the added clinical benefit and cost-effectiveness of i31-GEP-SLNB compared with free clinicopathologic-based prediction models is needed in patients with T1b melanoma.