Abstract
Background
Early detection of hepatocellular carcinoma (HCC) remains a significant clinical challenge due to the limited sensitivity of current surveillance tools, alpha-fetoprotein (AFP) and ultrasound. Recently, cell-free DNA (cfDNA) fragmentation analysis has shown promise in cancer detection; however, current sequencing-based approaches remain costly and unsuitable for large-scale screening.
Methods
Here, we introduce a predictive model for early HCC detection called “CEliver” (CfDNA-based automated capillary Electrophoresis method for Liver cancer screening), a model leveraging high-dimensional fragmentation profiling from the intensity distribution of cfDNA fragment lengths using automated capillary electrophoresis. We developed CF-2D features, a computational framework that extracts over 300 quantitative features from electropherogram data, including cfDNA concentration, dominant fragment sizes, two-dimensional shape descriptors, and short-to-long fragment ratios. We integrated these features with AFP levels to build the CEliver model, developed in 111 individuals and validated in an independent cohort of 69 subjects.
Results
Here we show the CF-2D profiles differ significantly between HCC patients and high-risk individuals. The CEliver model achieves 98% sensitivity across all HCC cases, and 96% sensitivity with 99% specificity for early-stage HCC (stage 0/A), substantially outperforming AFP (60% overall sensitivity, 35% for early-stage). In external validation, CEliver shows 88% sensitivity and 100% specificity.
Conclusions
CEliver provides a practical and accurate strategy for early HCC detection. By enabling high-dimensional cfDNA fragmentomics analysis on a widely accessible electrophoresis platform, it bridges the gap between research-grade cfDNA technologies and real-world clinical implementation. This method represents a simple and scalable approach that could potentially be applied in HCC surveillance.
Plain language summary
Hepatocellular carcinoma (HCC) is a liver cancer that is often diagnosed too late for effective treatment to be used. Here, we developed a computational model to detect HCC early called “CEliver”. The method analyses DNA fragments found in the blood. We developed the model using data from over 100 patients. CEliver accurately distinguishes early-stage HCC from people at risk of disease and performs better than existing diagnosis methods. This simple and scalable approach could be applied for large-scale population screening, helping at-risk people receive earlier diagnosis and treatment, potentially improving survival outcomes.
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Data availability
Source data underlying the analyses in the main figures are available in Supplementary Data. Data underlying Fig. 3A–C are provided as raw data, while data underlying Fig. 3D are provided as a 2D heatmap plot. Source data for Figs. 4B, 4C, 4D, 5A, and 5B are provided as CEliver scores.
Sequencing data and other sensitive information are stored on secure institutional servers at the Faculty of Medicine, Chiang Mai University, Thailand. Access to these data is restricted due to ethical and institutional policies. Requests for access should be directed to Dr. Chaiyawat (parunya.chaiyawat@cmu.ac.th) and will be considered subject to institutional approval procedures and policies.
Code availability
To facilitate reproducibility and further research, all scripts for feature extraction, model training, and prediction, as well as a small demonstration dataset, are publicly available on GitHub: https://doi.org/10.5281/zenodo.1829735749.
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Acknowledgements
This research project was supported by the Fundamental Fund 2024, Chiang Mai University, the Faculty of Medicine, Chiang Mai University, and the National Research Council of Thailand (NRCT) (grant no. N42A670184), Thailand. We would like to thank all the patients who participated in this study. The graphical abstract, as well as Figs. 1 and 2 were created using BioRender.com.
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S. Udomruk: Conceptualization, data curation, formal analysis, investigation, methodology, project administration, validation, visualization, funding acquisition, writing–original draft, writing–review and editing. S. Sutthitthasakul: Formal analysis, software, methodology, validation. N. Bunsermvicha: Resources, data curation, formal analysis. K. Pinyopornpanish, C. Charoentum, S. Kongkarnka, and D. Pruksakorn: Resources, data curation. P. Charoenkwan, T. Jaimalai, T. Duangsan and P. Noisagul: Software, methodology. P. Yongpitakwattana and K. Khounkaew: Investigation, methodology. S. Orrapin and S. Moonmuang: Investigation, validation. A. Pasena, P. Teeyakasem, P. Suksakit and R. Gamngoen: Resources, data curation. W. Lapisatepun: Conceptualization, supervision, resources, validation, funding acquisition, writing–review and editing. P. Chaiyawat: Conceptualization, supervision, methodology, funding acquisition, project administration, resources, validation, visualization, writing–original draft, writing–review and editing. All authors have read and agreed to the published version of this manuscript.
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Sasimol Udomruk, Songphon Sutthitthasakul, Worakitti Lapisatepun, and Parunya Chaiyawat report a petty patent application (Thai-2403001380) currently under review, licensed to CELiver, for the cfDNA-based automated capillary electrophoresis method for liver cancer screening. All other authors declare no competing interests.
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Communications Medicine thanks Jilei Liu and the other anonymous reviewer(s) for their contribution to the peer review of this work.
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Udomruk, S., Sutthitthasakul, S., Bunsermvicha, N. et al. Machine learning–based cfDNA fragmentation profiling using automated capillary electrophoresis for early detection of hepatocellular carcinoma. Commun Med (2026). https://doi.org/10.1038/s43856-026-01437-5
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DOI: https://doi.org/10.1038/s43856-026-01437-5
