Integrating Clinicopathologic Features with the 31-GEP Optimizes Patient Risk-Stratification Compared to Clinicopathologic Features Alone

A recent publication in the Journal of the American Academy of Dermatology (JAAD) highlights the development and validation of an AI algorithm integrating clinicopathological features with a 31-gene expression profile. Titled “Optimizing treatment approaches for patients with cutaneous melanoma by integrating clinical and pathologic features with the 31-gene expression profile test” this study aimed to validate this approach to then provide optimized, personalized risk of recurrence (i31-ROR).

Key Results:

• Importantly, relative to the validated i31-GEP SLNB algorithm that was validated to optimize prediction of SLN status, a different algorithm with the continuous 31-GEP score with different clinicopathologic factors was needed to optimize predictions for risk of recurrence.
• Patients with a low-risk i31-ROR result had significantly higher 5-year recurrence-free (91% vs. 45%, P<.001), distant metastasis-free (95% vs. 53%, P<.001), and melanoma-specific survival (98% vs. 73%, P<.001) than patients with a high-risk i31-ROR result.
• A combined i31-SLNB/ROR analysis identified 44% of patients who could forego SLNB while maintaining high survival rates (>98%) or were re-stratified as being at a higher or lower risk of recurrence or death.

Both the i31-ROR personalized risk of recurrence result and i31-SLNB are currently provided on the DecisionDx™-Melanoma report.