Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival, and overall survival
Wan, G.; Rashdan, H.; Burke, O. M.; Khattab, S.; Nguyen, N.; Leung, B. W.; Beagles, E.; Chang, C. T.; Yu, K.-H.; DeSimone, M. S.; Semenov, Y. Y.
Show abstract
This study compared machine-learning models for predicting recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) using clinicopathologic data from 1,621 stage I/II primary cutaneous melanoma patients. Our time-to-event models achieved concordance indices of 0.829 for RFS, 0.812 for DSS, and 0.778 for OS. Tumor thickness and mitotic rate were the most important predictors for RFS. Charlson comorbidity score and insurance type were critical for DSS and OS.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.