Research Projects
A Novel Explainable Digital Twin Approach for Safer and More Effective Immunotherapy in Hepatocellular Carcinoma

Immune checkpoint inhibitors (ICIs) have become a cornerstone of treatment for advanced Hepatocellular Carcinoma (HCC), offering hope for patients with limited options. However, outcomes are inconsistent, and these therapies carry a significant risk of immune-related adverse events (irAEs). For HCC patients, who often have underlying liver dysfunction (e.g., from cirrhosis or hepatitis), certain toxicities are of particular concern. Immune-mediated hepatitis (hepatotoxicity) can be difficult to distinguish from disease progression or baseline liver failure, and other irAEs can affect the gut, skin, and endocrine system. Severe (grade ≥3) toxicities are not only life-threatening but frequently necessitate discontinuation of ICI therapy, cutting short a potentially effective treatment.

Emerging computational tools like digital twins—virtual, data-driven models of a patient—and deep reinforcement learning (DRL)—an AI method for optimizing decisions—offer a path toward personalized and safer treatment. However, current approaches are limited. They often fail to model the ordered severity of toxicity grades (0-4), lack integration of liver-specific toxicity risks, and use opaque AI that doesn't enforce hard safety constraints against severe liver damage.

This adapted framework aims to bring a new level of precision to immunotherapy for liver cancer. By explicitly modeling HCC's unique biology and its associated toxicity risks, it seeks to create intelligent treatment strategies that are both more effective and safer for a vulnerable patient population.

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.