Work

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

Year: 2025

Type: preprint

Study design: Observational Study

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.

Source: medRxiv

DOI: 10.1101/2025.05.28.25328519

Authors: Guihong Wan, Hannah Rashdan, Olivia M. Burke, Sara Khattab, Nga Nguyen, Bonnie W. Leung, Emma Beagles, Crystal T. Chang, Kun-Hsing Yu, Mia S. DeSimone, Yevgeniy R. Semenov

Institutions: Harvard University, Massachusetts General Hospital, Brigham and Women's Hospital

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: U.S. Department of Defense, Melanoma Research Alliance, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of General Medical Sciences, National Institute of Arthritis and Musculoskeletal and Skin Diseases

Awards: RISK PREDICTION MODELS FOR RARE MELANOMAS, G6921871960, Mitigating Hematologic Adverse Events in Patients with Myeloid Malignancies: A Novel Causal Artificial Intelligence Approach, Explainable Artificial Intelligence for Melanoma Recurrence Prediction via Integrative Modeling of Multiplexed Imaging with Transcriptomics and Histopathology, Predictive modeling of cutaneous immune checkpoint inhibitor toxicities, Robust, Generalizable, and Fair Machine Learning Models for Biomedicine

OpenAlex ID: W4410851959

Cites: 14

Cited by: 1

Related to: 10