Summary
Castle Biosciences Inc. recently developed and independently validated an artificial intelligence-based neural network algorithm to predict SLN positivity risk in patients with T1-T4 CM by integrating the continuous 31-GEP score with clinicopathologic features (i31-GEP)[32]. An objective, individualized approach to SLNB decision-making could reduce unnecessary procedures and associated risks in patients with a low likelihood of having a positive SLNB. Similarly, this approach could identify previously unidentified or subjectively excluded patients with a higher likelihood (>10% risk) of SLN positivity and may benefit most from, a SLNB. In addition, it is important to evaluate the impact of the 31-GEP test on recommendations for SLNB by clinicians, and study long-term outcomes of patients in which the GEP test results were used to inform the procedure in a prospective study.
Objectives
Primary Aim 1. Determine the association of GEP test result with SLNB surgical decisions in patients with SLNB-eligible T1-T2 melanoma
Primary Aim 2: Track and evaluate 5-year clinical outcomes for patients in each GEP subclass and/or i31-GEP risk group, including those who did and did not undergo SLNB and those with T3-T4 melanoma.