Machine learning prediction for early-stage melanoma outcomes: recurrence-free survival, disease-specific survival, and overall survival.

Journal: medRxiv : the preprint server for health sciences
Published Date: May 29, 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.

Authors

  • Guihong Wan
  • Hannah Rashdan
  • Olivia M Burke
  • Sara Khattab
  • Nga Nguyen
  • Bonnie W Leung
  • Emma Beagles
  • Crystal T Chang
    Department of Dermatology, Stanford University, Stanford, California, USA; Clinical Excellence Research Center, School of Medicine, Stanford University, Palo Alto, California, USA.
  • Kun-Hsing Yu
    Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Mia S DeSimone
  • Yevgeniy R Semenov
    Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

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