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Related Experiment Video

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Machine Learning Predicts Hepatocellular Carcinoma Risk from Routine Clinical Data: A Large Population-Based

Jan Clusmann1,2,3,4, Paul-Henry Koop1,2,3,4, David Y Zhang5,6

  • 1Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany.

Cancer Discovery
|March 26, 2026
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Summary

We developed PRE-Screen-HCC, an interpretable machine learning tool for hepatocellular carcinoma (HCC) risk stratification. This framework significantly improves early detection and risk assessment using routine clinical data.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Machine Learning

Background:

  • Hepatocellular carcinoma (HCC) presents a significant global health challenge due to its high fatality rate.
  • Accurate risk stratification for HCC is critical for early detection and intervention but remains a complex clinical problem.

Purpose of the Study:

  • To develop and validate an interpretable machine learning framework for hepatocellular carcinoma (HCC) risk stratification.
  • To assess the utility of multimodal clinical data for improving HCC risk prediction.

Main Methods:

  • Utilized prospectively collected multimodal data from over 900,000 individuals across two large cohorts (UK Biobank and All of Us Research Program).
  • Developed and trained random-forest-based machine learning models incorporating demographics, lifestyle, health records, blood markers, genomics, and metabolomics.
  • Evaluated model performance against existing state-of-the-art risk scores on internal and external test sets.

Main Results:

  • The developed PRE-Screen-HCC framework demonstrated significantly superior performance compared to current state-of-the-art risk scores.
  • The models showed robustness across diverse ethnic subgroups, indicating broad applicability.
  • Comprehensive interpretability analysis was performed, and all code and model weights were released.

Conclusions:

  • PRE-Screen-HCC offers a robust, interpretable, and accurate machine learning solution for hepatocellular carcinoma risk stratification.
  • The framework facilitates early detection and personalized risk assessment using routinely available clinical data.
  • The open-source nature of the code and models promotes external validation and integration into clinical workflows.