Predicting Recurrence-Free and Overall Survival for Patients With Stage II Melanoma: The MIA Calculator

Abstract

PURPOSE Improvements in recurrence-free survival (RFS) were demonstrated in two recent randomized trials for patients with sentinel node (SN)–negative stage IIB or IIC melanoma receiving adjuvant systemic therapy (pembrolizumab/nivolumab). However, adverse events also occurred. Accurate individualized prognostic estimates of RFS and overall survival (OS) would allow patients to more accurately weigh the risks and benefits of adjuvant therapy. Since the current American Joint Committee on Cancer eighth edition (AJCC-8) melanoma staging system focuses on melanoma-specific survival, we developed a multivariable risk prediction calculator that provides estimates of 5- and 10-year RFS and OS for these patients. METHODS Data were extracted from the Melanoma Institute Australia (MIA) database for patients diagnosed with stage II (clinical or pathological) melanoma (n 5 3,220). Survival prediction models were developed using multivariable Cox regression analyses (MIA models) and externally validated twice using data sets from the United States and the Netherlands. Each model’s performance was assessed using C-statistics and calibration plots and compared with Cox models on the basis of AJCC-8 staging (stage models). RESULTS The 5-year and 10-year RFS C-statistics were 0.70 and 0.73 (MIA-model) versus 0.61 and 0.60 (stage-model), respectively. For OS, the 5-year and 10-year C-statistics were 0.71 and 0.75 (MIA-model) compared with 0.62 and 0.61 (stage-model), respectively. The MIA models were well calibrated and externally validated. CONCLUSION The MIA models offer accurate and personalized estimates of both RFS and OS in patients with stage II melanoma even in the absence of pathological staging with SN biopsy. These models were robust on external validations and may be used in everyday practice both with (ideally) and without performing SN biopsy to identify high-risk patients for further management strategies. An online tool will be available at the MIA website (Risk Prediction Tools).