Development of machine learning-based personalized predictive models for risk evaluation of hepatocellular carcinoma in hepatitis B virus-related cirrhosis patients with low levels of serum alpha-fetoprotein.

Journal: Annals of hepatology
PMID: 39151891

Abstract

INTRODUCTION AND OBJECTIVES: The increasing incidence of hepatocellular carcinoma (HCC) in China is an urgent issue, necessitating early diagnosis and treatment. This study aimed to develop personalized predictive models by combining machine learning (ML) technology with a demographic, medical history, and noninvasive biomarker data. These models can enhance the decision-making capabilities of physicians for HCC in hepatitis B virus (HBV)-related cirrhosis patients with low serum alpha-fetoprotein (AFP) levels.

Authors

  • Yuan Xu
    Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, China.
  • Bei Zhang
    College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
  • Fan Zhou
  • Ying-Ping Yi
    Medical Big Data Center, the Second Affiliated Hospital of Nanchang University, Nanchang, PR China.
  • Xin-Lei Yang
    Medical Big Data Center, the Second Affiliated Hospital of Nanchang University, Nanchang, PR China.
  • Xiao Ouyang
    Quiclinic Technology Co., Ltd., Nanchang, PR China.
  • Hui Hu
    Department of Epidemiology, College of Medicine & College of Public Health and Health Professions, University of Florida, Gainesville, FL, USA.

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