ArticleNature communications2025
A highly accurate risk factor-based XGBoost multiethnic model for identifying patients with skin cancer.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Genetic Ancestry and Colorectal Cancer in the All of Us Dataset.JAMA network open · 2026Article
- Admixture mapping identifies complex trait associations with local ancestry in themedRxiv : the preprint server for health sciences · 2025Article
- A Machine Learning Model Based on Clinical Factors to Predict the Efficacy of First-Line Immunochemotherapy for Patients With Advanced Gastric Cancer: Retrospective Study.JMIR medical informatics · 2025Article
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Authors and funding
5 authors.
Funding
Abstract
While individuals of European descent have a higher risk of skin cancer, other ancestries tend to have more advanced disease at diagnosis, resulting in outcome disparities. We examine the diverse All of Us dataset and identify genetic ancestries, lifestyle, social determinants of health and PDE5a inhibitor use as independent risk factors for skin cancer. We integrate these risk factors into a highly accurate XGBoost multiethnic model for identifying patients with skin cancer. Analyses of Shapley scores and interactions indicate the presence of strong non-linear associations between age and other risk factors, in particular cancer history, genetic ancestry and annual income, and suggest a stronger dependence on genetic and social determinants in younger individuals. Our XGBoost multiethnic model offers a precision medicine approach for early skin cancer detection in ethnically diverse patients, which could reduce outcome disparities in individuals of non-European ancestries.
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