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Racial Disparities in Comorbidity Patterns of Early-Onset Liver Cancer: A Machine Learning Analysis.

Bingya Ma1, Kai Zheng2,3, Fa-Chyi Lee4

  • 1Department of Epidemiology and Biostatistics, University of California Irvine, Irvine, CA, USA.

Cancer Control : Journal of the Moffitt Cancer Center
|July 30, 2025
PubMed
Summary
This summary is machine-generated.

Early-onset liver cancer risk varies by race, with distinct comorbidity patterns. Machine learning models reveal significant racial disparities, aiding targeted prevention for liver cancer.

Keywords:
SHAPcomorbidityliver cancermachine learningracial disparities

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Area of Science:

  • Oncology
  • Genetics and Genomics
  • Public Health

Background:

  • The incidence of early-onset liver cancer (EOLC) is rising globally, yet etiological understanding is limited, especially in non-Asian populations.
  • Existing research often overlooks the specific comorbidity profiles contributing to EOLC across diverse racial and ethnic groups.

Purpose of the Study:

  • To investigate the comorbidity patterns associated with EOLC across different racial and ethnic groups.
  • To develop and evaluate race/ethnicity-specific machine learning (ML) models for predicting EOLC risk.
  • To identify key comorbidities influencing EOLC risk within distinct populations.

Main Methods:

  • A case-control study utilizing the University of California Health Data Warehouse, including 1574 EOLC patients (ages 18-49) and 7870 controls.
  • Application of multiple ML classification methods (decision trees, random forests, logistic regression, XGBoost, LightGBM) to predict liver cancer risk based on demographics and comorbidities.
  • Evaluation of model performance using F1 scores and SHapley Additive exPlanations (SHAP) to determine influential comorbidities per racial group.

Main Results:

  • Significant racial disparities in comorbidity prevalence were observed: Asian and Pacific Islanders (API) showed higher rates of Hepatitis B virus (HBV) infection; Hispanics had higher rates of cirrhosis, hypertension, diabetes, and Hepatitis C virus (HCV) infection; Whites exhibited higher rates of anxiety, asthma, hypothyroidism, and cholangitis.
  • Race/ethnicity-specific ML models demonstrated varying predictive performance: API (F1=0.77, AUC=0.90) and Hispanic (F1=0.77, AUC=0.92) models outperformed the White model (F1=0.64, AUC=0.87).
  • SHAP analysis identified HBV as the primary comorbidity for API, while HCV and metabolic disorders were significant for Hispanics. The White population displayed a more dispersed pattern of comorbidities.

Conclusions:

  • This study underscores significant racial disparities in EOLC comorbidity patterns.
  • Machine learning models can effectively identify high-risk populations by analyzing these distinct patterns.
  • Findings support the development of targeted prevention strategies for EOLC tailored to specific racial and ethnic groups.