ArticleCommunications medicine2026
Machine learning-based cfDNA fragmentation profiling using automated capillary electrophoresis for early detection of hepatocellular carcinoma.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence models in hepatocellular carcinoma: a systematic review and diagnostic meta-analysis.BMC gastroenterology · 2026Pooled it
- Immune response to DNA and RNA: structural insights, molecular mechanisms, and therapeutic targeting.Molecular biomedicine · 2026Review
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Authors and funding
21 authors.
Funding
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Abstract
backgroundEarly 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.
methodsHere, 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.
resultsHere 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.
conclusionsCEliver 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.