Research Projects
Cost-Effectiveness of Non-Invasive Tools for HCC Risk Stratification and Early Detection Among Patients with MASLD in China: AI-Based Models Versus Conventional Risk Scores


Programme(s) to which this project applies:
☑ MPhil/PhD ☑ MRes[Med] ☒ URIS

Area of Research:
Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD); Hepatocellular Carcinoma (HCC) surveillance; Artificial Intelligence (AI) in risk stratification; Economic evaluation

Objective and Significance:
HCC is a serious complication of MASLD, and current surveillance strategies using conventional risk scores and ultrasound are suboptimal, leading to late-stage detection. AI models offer the potential to improve risk stratification and early detection of HCC. The aim of this project is to determine the clinical and economic value of using AI-based models compared to conventional methods for HCC surveillance in patients with MASLD, to inform cost-effective healthcare policy and implementation.

Research Plan and Methodology:

  • Model-based economic evaluation, comprising a diagnostic decision tree and a state-transition (Markov) model to simulate patient pathways and long-term outcomes over a lifetime time horizon.
  • Systematic literature review of multiple databases to populate the model with data on diagnostic accuracy, costs, utilities, and survival.
  • The analysis will compare AI models (imaging or multimodal) against conventional risk scores and standard surveillance.
  • Key outcomes include Incremental Cost-Effectiveness Ratios (ICERs), quality-adjusted life years (QALYs), and budget impact.
  • Extensive uncertainty analysis will be conducted, including deterministic, probabilistic, and scenario analyses to test the robustness of the findings.

Professor JD Zhou, Department of Family Medicine and Primary Care

Professor Jiandong Zhou is now working as an Assistant Professor at the Department of Family Medicine and Primary Care, and  he was jointly appointed by Department of Pharmacology and Pharmacy and the School of Public Health at the University of Hong Kong (HKU) Li Ka Shing Faculty of Medicine since 2024. Prof. Zhou received his post-doctoral training as a Medical Statistician at the Nuffield Department of Clinical Medicine, University of Oxford, United Kingdom. He earned his Ph.D. in Data Science from School of Data Science, City University of Hong Kong, Hong Kong SAR, China. Before joining HKU, Prof. Zhou worked as Assistant Professor at Warwick Medical School, University of Warwick, United Kingdom.

Prof Zhou has research interests in big data analytics, medical statistics, primary care and preventive health, case-control and cohort studies, predictive and decision analytics, pharmacoepidemiology and aetiology of chronic diseases (including cardiovascular diseases, diabetes mellitus, etc. for family medicine patients). He also has study interests in casual treatment effects analysis (double machine learning), social epidemiology and adverse online events identification with text mining, high-dimension data representation and latent cluster pattern learning in large-scale health datasets. Recently, he is conducting machine learning analytics for illness trajectories and palliative care, especially progression pattern analysis/visualization, end stage risk assessment following chronic diseases, and non-invasive cancer screening.

 

Biography
HKU Scholars Hub
ORCID
jdzhou@hku.hk

For more information or to express interest for this project, please email the supervisor or the specified contact point in the project description.  Interested candidates are advised to enclose with your email:

  1. your CV,
  2. a brief description of your research interest and experience, and
  3. two reference letters (not required for HKUMed UG students seeking MRes[Med]/URIS projects).

Information on the research programme, funding support and admission documentations could be referenced online at the Research Postgraduate Admissions website. General admission enquiries should be directed to rpgmed@hku.hk.

HKUMed MBBS students interested in the Master of Research in Medicine (MRes[Med]) programme may visit the programme website for more information.  

HKUMed UG students interested in the Undergraduate Research Internship Scheme (URIS) may visit the scheme’s website for more information. 

Research @ Department of Family Medicine and Primary Care