Racial Disparities in Comorbidity Patterns of Early-Onset Liver Cancer: A Machine Learning Analysis.
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IntroductionThe incidence of early-onset liver cancer (EOLC) has been increasing in many countries, yet evidence on its etiology remains limited, particularly outside the Asian population. This case-control study explores the comorbidity patterns of EOLC and develops race/ethnicity-specific machine learning (ML) models to predict liver cancer risk.MethodsWe included patients diagnosed with primary liver cancer between ages 18 and 49 from the University of California Health Data Warehouse, matching each patient with five controls. ML classification methods, including decision trees, random forests, logistic regression, XGBoost, and LightGBM, were used to assess liver cancer risk based on demographics and comorbidities. Model performance was evaluated using F1 scores, and SHapley Additive exPlanations (SHAP) was applied to identify the most influential comorbidities within each racial group.ResultsA total of 1574 patients and 7870 controls were identified. Asian and Pacific Islanders (API) had significantly higher rates of Hepatitis B virus (HBV) infection, while Hispanics had higher prevalences of cirrhosis, hypertension, diabetes, and Hepatitis C virus (HCV) infection. Whites showed higher rates of anxiety, asthma, hypothyroidism, and cholangitis. Race/ethnicity-specific models for API (F1 score = 0.77, AUC = 0.90) and Hispanics (F1 score = 0.77, AUC = 0.92) outperformed the model for Whites (F1 score = 0.64, AUC = 0.87) in the validation dataset. The SHAP results indicated that HBV infection was the dominant comorbidity for API, and HCV and metabolic disorders were notable among Hispanics. In contrast, the White population showed a broader and less concentrated comorbidity pattern.ConclusionsOur study highlights significant racial disparities in comorbidity patterns for early-onset liver cancer, demonstrating the potential of ML models to identify high-risk populations and inform targeted prevention strategies.