Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally, largely due to late diagnosis and inadequate risk stratification. Current clinical risk scores have limited predictive accuracy and often fail to identify high-risk individuals early.
Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally, largely due to late diagnosis and inadequate risk stratification. Current clinical risk scores have limited predictive accuracy and often fail to identify high-risk individuals early. With the increasing availability of large-scale healthcare data, machine learning offers an opportunity to improve early detection using routinely collected clinical information.
Problem Statement
Existing HCC risk prediction models are insufficient in accuracy, lack generalizability across populations, and often rely on limited variables. There is a need for a scalable, interpretable, and robust model that can integrate diverse real-world clinical data to accurately stratify HCC risk and enable early detection at a population level.
Summary
This large multicentric study developed an interpretable machine-learning model (PRE-Screen-HCC) using data from over 900,000 individuals across UK Biobank and the All of Us cohort. The model integrated demographics, lifestyle factors, clinical records, laboratory data, genomics, and metabolomics. It significantly outperformed existing risk scores in predicting HCC risk across diverse populations. Importantly, the model is transparent, externally validated, and made accessible via a web-based calculator, making it clinically applicable. This study represents a major step toward precision screening and early detection of HCC using real-world data and AI.
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