Editor’s Note: Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and the third leading cause of cancer-related deaths. The overall prognosis for HCC remains poor, with a five-year survival rate consistently below 20%. However, the five-year survival rate for patients diagnosed at an early stage of HCC can exceed 55%, highlighting early detection and diagnosis as a critical breakthrough for improving HCC outcomes. Despite this, fewer than 30% of HCC cases are diagnosed at an early stage, reflecting the significant clinical challenges still faced in early HCC detection.In recent years, cell-free DNA (cfDNA)—a novel biomarker—has drawn increasing attention for its potential in early prediction and precise diagnosis of HCC. On March 29, during a parallel session at the 34th Annual Conference of the Asian Pacific Association for the Study of the Liver (APASL 2025), Dr. Rong Fan from Nanfang Hospital delivered a keynote lecture titled “The Application of Cell-Free DNA Testing in Early Hepatocellular Carcinoma Surveillance.” In her presentation, Dr. Fan systematically reviewed the current landscape and challenges of HCC screening, recent advances in cfDNA-based surveillance, and the future directions and obstacles facing its clinical application—offering valuable insights for both clinicians and researchers.
Current Landscape and Challenges of HCC Screening
A meta-analysis covering 47 studies and 15,158 patients demonstrated that regular HCC surveillance significantly improves early detection rates, increases the chances of curative treatment, and prolongs overall survival—underlining its vital role in clinical HCC management¹. Yet, in real-world practice, two major obstacles continue to hinder effective HCC surveillance.
The first is the “one-size-fits-all” screening approach. High-risk patients with chronic liver disease are typically recommended to undergo biannual abdominal ultrasound combined with alpha-fetoprotein (AFP) testing. However, this strategy overlooks significant variability in individual HCC risk and access to medical resources. Applying the same surveillance model across diverse patient populations may lead to resource waste or insufficient monitoring for those at higher risk.
The second challenge is low sensitivity in current screening methods, leading to missed early-stage diagnoses. Among patients with liver cirrhosis, the combined sensitivity of ultrasound and AFP testing for detecting early HCC is only about 63%—meaning that 37% of early-stage HCC cases remain undiagnosed.
To address these issues, a range of novel biomarkers and digital algorithms are emerging as valuable tools for guiding personalized surveillance. A reliable HCC risk model is essential for identifying individuals at the highest risk and implementing tailored surveillance strategies. Models such as aMAP, REACH-B/mREACH-B, and PAGE-B/mPAGE-B offer predictive frameworks. Based on data from 11 long-term follow-up cohorts, our team developed and validated the aMAP 1.0 score, which predicts HCC risk across various etiologies (e.g., hepatitis B, hepatitis C, and fatty liver disease) and ethnic groups (including Asian and Western populations)². Building on this, we further developed the aMAP 2.0 score by integrating serial AFP measurements with long-term follow-up data from aMAP 1.0 over an 8-year period³. These scoring systems effectively identify a group of ultra-high-risk patients with an annual HCC incidence rate of 3.7%.
In addition, we should explore more objective and accurate serum biomarkers or diagnostic algorithms to complement AFP. These include des-gamma-carboxy prothrombin (DCP), as well as composite diagnostic scores such as GALAD and GAAD.
This leads to a key question: Is there a potential biomarker that can play a dual role in both risk stratification and detection of HCC? In other words, can a single biomarker be used effectively for both surveillance and early diagnosis?
Research Advances in cfDNA for HCC Surveillance
Cell-free DNA (cfDNA), released into body fluids such as blood, urine, and cerebrospinal fluid through apoptosis, necrosis, or active secretion, has become a key target in liquid biopsy. It is already widely used in non-invasive prenatal testing, organ transplantation, and cancer management.
Recent studies have made notable breakthroughs in applying cfDNA to the diagnosis of hepatocellular carcinoma (HCC). In a large, multicenter study conducted across nine clinical centers in China, researchers developed a combined diagnostic model—referred to as the “Combined method”—which integrates TERT, TP53, and CTNNB1 gene mutations with three serum biomarkers: AFP, AFP-L3, and DCP. This model demonstrated a significant advantage in HCC diagnosis, with an overall sensitivity of 81.25% and an early-stage HCC detection rate of 66.67%, significantly outperforming traditional AFP testing⁴.
Another study focused on the epigenetic characteristics of cfDNA developed the HepaAiQ model based on 20 HCC-specific methylation markers. This model achieved higher sensitivity for early HCC compared to traditional biomarkers, with results of 82.0% versus 52.0% for AFP and 85.5% versus 66.1% for DCP⁵. Similarly, the DELFI model, based on genome-wide cfDNA fragmentation patterns, showed strong diagnostic performance in chronic liver disease patients, achieving an area under the curve (AUC) greater than 0.9⁶.
Importantly, integrating various epigenetic signatures also appears promising for improving diagnostic performance. A study involving 13 hospitals in China constructed the HIFI score by combining different genomic and methylation features from cfDNA. In both validation and test cohorts, the HIFI score accurately identified HCC among patients with liver cirrhosis, with sensitivity and specificity exceeding 90%⁷.
These findings collectively demonstrate that molecular signatures carried by cfDNA—such as genetic mutations, methylation modifications, and fragmentation patterns—are closely associated with HCC pathobiology and provide a reliable basis for early diagnosis.
In addition, the characteristics and dynamic changes of cfDNA show strong potential for improving HCC risk stratification and guiding surveillance. Our team developed the aMAP-2 Plus model by combining multi-time-point cfDNA data and aMAP 2.0 scores, based on a cohort of over 4,000 liver cirrhosis patients from 16 centers across China³. The AUC of aMAP-2 Plus was 0.89 in the training set and 0.85 in the validation set, outperforming the aMAP series and other existing scores such as PAGE-B and mPAGE-B. The model effectively distinguished high-risk individuals, whose 1-year cumulative HCC incidence was 12 to 21 times that of the low-risk group. These findings confirm that the dynamic profiling of cfDNA significantly enhances risk prediction and offers a valuable tool for precision surveillance in patients with cirrhosis.
Overall, multiple studies have confirmed that cfDNA’s diverse features—genetic mutations, epigenetic modifications, and fragmentation profiles—show promising value in both HCC diagnosis and risk prediction. This provides a new technological path for achieving non-invasive, precise monitoring of HCC in clinical settings.
Challenges and Future Directions in cfDNA Application
Looking ahead, the clinical use of cfDNA in HCC surveillance still faces two primary challenges—technical and clinical.
On the technical side, factors such as host variability, tumor heterogeneity, and age-related clonal hematopoiesis mutations increase the complexity of cfDNA analysis. In addition, low cfDNA concentration and differences among detection platforms may impact the sensitivity and reproducibility of test results.
Clinically, variations in molecular features due to different disease etiologies and ethnic backgrounds highlight the need for extensive, reliable validation studies. Moreover, the relationship between cfDNA dynamics and current diagnostic or predictive algorithms for HCC remains underexplored, and the value of cfDNA in early warning still needs to be clearly established.
To address these issues, current research is focusing on identifying the most effective HCC-specific cfDNA biomarkers through multicenter collaboration. Combining cfDNA with other validated biomarkers or diagnostic algorithms and utilizing artificial intelligence to extract meaningful insights from multidimensional data may lead to the development of a precise, robust dynamic monitoring system. Ultimately, large-scale prospective studies will be essential to verify its clinical utility and standardize the use of cfDNA in HCC surveillance.
