Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival,
Guihong Wan1, Hannah Rashdan1, Olivia M Burke1
1Department of Dermatology, Massachusetts General Hospital, Harvard Medical School.
Medrxiv : the Preprint Server for Health Sciences
|June 10, 2025
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.
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