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Trajectory Modelling of Prognostic Biomarkers Linked to Liver Cancer Risk: A Systematic Review.

Bashayr Aldawsari¹, Laura J Bonnett¹, Maria Sudell¹

¹Department of Health Data Science, Institute of Population Health, University of Liverpool, Liverpool, UK.

|June 11, 2026

Summary

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This review identifies key risk factors for liver cancer using longitudinal clustering. It highlights how tracking factors like body mass index and metabolic syndrome over time can predict liver cancer risk.

Area of Science:

Oncology
Biostatistics
Public Health

Background:

Liver cancer is a major cause of cancer mortality globally.
Identifying individuals at high risk is crucial for early detection and prevention.
Longitudinal data analysis offers powerful tools for understanding risk factor trajectories.

Purpose of the Study:

To systematically review studies employing longitudinal clustering methods.
To identify trajectories of risk factors associated with liver cancer development and prognosis.
To synthesize findings on prognostic risk factors for liver cancer.

Main Methods:

Systematic literature search across MEDLINE, PubMed, and Scopus.
Inclusion of articles utilizing longitudinal clustering methods for liver cancer risk factor analysis.

Keywords:

Biomarker Clustering Group-based trajectory model Growth mixture model Hepatocellular carcinoma Liver cancer Longitudinal clustering methods Trajectory

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Published on: September 30, 2021

Registration on PROSPERO (CRD42023406334) for transparency.

Main Results:

Twenty-four studies were included in the review.
Key risk factors identified include increasing BMI, metabolic syndrome, C-reactive protein, low physical activity, high fasting glucose, and smoking.
For individuals with liver disease, elevated alcohol consumption, De Ritis ratio, AFP, FIB-4, liver fibrosis score, and stable HBsAg were associated with higher risk.
In patients with existing liver cancer, increasing AFP and decreasing HBV DNA correlated with lower survival.
Identified longitudinal clustering methods include latent class growth modeling, Gaussian mixture models, and joint latent class modeling.

Conclusions:

This is the first systematic review focusing on longitudinal clustering methods for liver cancer prognostic risk factors.
The study underscores the value of longitudinal data in predicting liver cancer outcomes.
Longitudinal clustering provides a robust framework for understanding dynamic risk factor patterns in liver cancer.