Abstract
Metabolic dysfunction-associated steatotic liver disease (MASLD) is rapidly emerging as the leading global liver disorder and is poised to become the primary cause of hepatocellular carcinoma (HCC). Research indicates that nearly 50% of HCC cases in MASLD patients occur without cirrhosis, often presenting with more advanced and larger tumors. Despite this, current guidelines primarily focus on HCC screening in cirrhotic patients, with limited guidance for non-cirrhotic MASLD individuals. This narrative review seeks to identify key risk factors for HCC development, consolidate available screening methods, and propose a practical, risk-stratified algorithm for HCC surveillance in non-cirrhotic MASLD patients. We conducted a comprehensive review of studies published between 2017 and 2023 using PubMed, Embase, and CNKI, focusing on HCC risk factors and emerging screening strategies for non-cirrhotic MASLD cohorts. Key risk factors for HCC development in these patients include male sex, age over 65, hypertension, diabetes, mild alcohol consumption, smoking, dyslipidemia, elevated alanine aminotransferase levels, and a platelet count ≤ 150 × 109/L. Among the screening methods evaluated, circulating free DNA, alpha-fetoprotein (AFP) combined with protein induced by vitamin K absence or antagonist-II (PIVKA-II), and the GALAD score (incorporating Glypican-3, AFP, alpha-1-Antitrypsin, and des-gamma-carboxy prothrombin) demonstrated the highest performance. Based on these findings, we proposed a risk-stratified HCC surveillance algorithm that integrates GALAD and PIVKA-II into the existing sonography and AFP screening protocols. This review aims to provide clinicians with actionable recommendations for HCC screening in non-cirrhotic MASLD patients.
Keywords: metabolic dysfunction-associated steatotic liver disease, hepatocellular carcinoma, liver disease without cirrhosis, HCC risk factors, cancer screening
Introduction
Due to the burgeoning prevalence of unhealthy lifestyles in contemporary society, non-alcoholic fatty liver disease (NAFLD) has emerged as the predominant chronic liver condition, affecting approximately 32.4% of adults globally [1, 2]. The recent Delphi Consensus has proposed renaming NAFLD as metabolic dysfunction-associated steatotic liver disease (MASLD) [3]. A recent study found no discernible differences in the clinical and pathological features of the disease under the MASLD and NAFLD definitions. However, MASLD identified a larger number of individuals and was associated with an increased risk of all-cause mortality, whereas NAFLD was not [4]. Notably, between 2010 and 2019, the proportion of hepatocellular carcinoma (HCC) cases arising from metabolic dysfunction-associated steatohepatitis (MASH) surged by 39%, surpassing viral hepatitis as the swiftest-growing cause of liver cancer worldwide [5]. MASLD is defined as a combination of at least one cardiometabolic risk factor with hepatic steatosis and the absence of other causes, such as alcohol consumption and viral infections or other chronic liver diseases [3, 6]. It encompasses a spectrum of conditions, ranging from hepatic steatosis to MASH, cirrhosis, and eventually, HCC [7].
HCC stands as the fifth most prevalent cancer and the second leading cause of cancer-related mortality in Asia [8]. A study investigating the pathological features of 157 HCC patients with non-cirrhotic fatty liver disease revealed that only 15% of these patients exhibited underlying tissue manifestations of steatohepatitis, indicating that HCC in MASLD may manifest in the absence of histologically significant inflammation [9]. The incidence of HCC in MASLD patients was reported to be 0.44 per 1000 person-years based on a systematic review [10]. Furthermore, approximately 30.00% to 48.20% of MASH-associated HCC cases occur in patients without cirrhosis [10–12], solidifying MASLD as the primary etiology of HCC in non-cirrhotic livers [11]. The incidence of HCC in patients with non-cirrhotic MASLD ranges from 0.10 to 1.30 per 1000 person-years [13–16].
Various theories exist regarding the mechanism by which MASLD progresses to HCC, with the multiple-hit theory being the most prominent. This theory suggests that during the progression of MASLD, interactions among genetic factors, lipotoxicity, insulin resistance, oxidative stress, metabolic inflammation, disturbances of intestinal flora, and impaired immune surveillance contribute to hepatocellular carcinogenesis [17, 18]. Current guidelines recommend biannual liver ultrasound with or without alpha-fetoprotein (AFP) screening for HCC in MASLD patients with cirrhosis [19–23]. However, both screening methods have limited sensitivity, particularly ultrasound, which has been associated with a miss rate of nearly 41% in the MASLD population [24]. Given that approximately one-third of adults worldwide have MASLD, the potential for MASLD patients to develop HCC, even in the absence of cirrhosis, underscores a significant disease burden on society. Consequently, recent studies have concluded that current HCC screening strategies inadequately address non-cirrhotic MASLD [21].
The European Association for the Study of the Liver guidelines stipulate that screening becomes cost-effective when the annual prevalence of the disease in the population exceeds 1.5% [25]. Another modeling study suggests that an HCC incidence rate of approximately 0.8% per year would be a cost-effective threshold for initiating surveillance of patients with cirrhosis [26]. Consequently, screening all non-cirrhotic MASLD patients for HCC is not deemed cost-effective and would result in a substantial misallocation of societal resources. Hence, risk-stratifying MASLD patients to identify subgroups more prone to HCC development is imperative. In recent years, with further elucidation of the pathogenesis of MASLD-associated HCC, researchers have identified numerous novel assays and serologic markers. To render liver cancer screening cost-effective in this population, researchers have proposed various risk prediction models and scores, such as the GALAD score and the HCC-RIFLE model [27, 28]. Nonetheless, comparisons of the validity and specificity of these models and scores are currently lacking. Clinicians necessitate a comprehensive review to visually discern and compare the sensitivities and specificities among the various liver cancer screening methods currently available.
This narrative review summarized the latest screening methods for HCC associated with non-cirrhotic MASLD over the past seven years, analyzed the risk factors for non-cirrhotic MASLD patients, explored the mechanisms underlying HCC development in these patients, and provided a comparative analysis of the characteristics and differences between the various screening methods. This review will hopefully benefit clinicians when faced with HCC screening for some reference. We also hope that this review will serve as a catalyst for more studies in the future to improve the screening of HCC in this growing population.
Methods
Database and search strategy
To encompass a comprehensive array of pertinent studies, we used a relatively systematic search strategy to retrieve relevant original research in this narrative review. Primary searches were conducted in PubMed and Embase, with supplementary searches in the CNKI database. We targeted studies spanning from January 2017 to December 2023. Key search terms were determined following the PICO methodology, with the target population being MASLD patients without cirrhosis, the intervention being HCC screening, and the outcome being HCC prevalence. Specific search strategies are outlined in the Supplementary Data.
Eligibility criteria
Inclusion criteria for studies included: (1) being conducted in adult MASLD patients, (2) focusing on HCC screening or risk factors, and (3) being published in English or Chinese with full-text availability. Exclusion criteria for studies encompassed: (1) absence of “HCC screening” or “risk factors” in the relevant content, (2) case reports or abstract-only publications, (3) exclusive focus on HCC in cirrhotic MASLD patients, and (4) limited to basic research or animal experiments solely exploring disease mechanisms. Descriptive reviews underwent initial screening by examining titles and abstracts to exclude case discussions, reviews, letters, and studies lacking full-text access. The remaining articles were further evaluated against the inclusion and exclusion criteria, with a final manual review of the included studies and their reference lists conducted to ensure robust relevance to this narrative review.
Data extraction
Details of authorship and publication, methodological characteristics, results, and screening methods from the included studies will be extracted. To elucidate the efficacy and variances among different screening methods, data on validity, specificity, sensitivity, and HCC prevalence detected by each screening method will be presented. Additionally, we will synthesize the risk factors for HCC development in non-cirrhotic MASLD patients alongside their corresponding ratios or relative risks.
Literature review
Through our search strategy, we retrieved a total of 248 articles. Among these, 83 were excluded due to their non-original nature, while 126 were eliminated based on language criteria, lack of exploration into non-cirrhotic MASLD and HCC, and absence of mention regarding HCC screening. Additionally, 19 articles focusing solely on disease mechanisms without clinical applications were excluded. Ultimately, 20 articles meeting the inclusion criteria were incorporated into this narrative review. A detailed flow chart illustrating the selection process of the included studies is provided in Figure 1.
Figure 1.
Flowchart illustrating the process of selecting articles for the narrative review. Initially, 248 articles were identified through the search process, followed by screening based on inclusion and exclusion criteria, resulting in 20 articles included in the narrative review. HCC = hepatocellular carcinoma.
Characteristics of the included studies
Among the 20 articles included, 10 addressed HCC screening in non-cirrhotic MASLD patients [28–37], while 12 investigated risk factors for HCC development in patients with non-cirrhotic MASLD [14, 33, 36, 38–46]. These encompassed three prospective cohort studies [14, 33, 41], and one study utilizing the Gene Expression Omnibus (GEO) data repository [36]. Notably, despite several proposed methods for HCC screening in non-cirrhotic MASLD patients, the lack of prospective randomized controlled trials hampers the determination of the most suitable screening interval. Table 1 delineates the characteristics and methodological attributes of the included studies pertaining to screening for non-cirrhotic HCC in MASLD patients.
Table 1.
Characteristics and findings of studies investigating HCC screening in non-cirrhotic patients with MASLD
| Author/year | Study design/time/country | Patient (n) | Screening method | Screening duration | Screening outcome |
|---|---|---|---|---|---|
| Best et al. (2020) [32] | RCC/2005–2016/Germany | Non-cirrhotic MASH patients: HCC (30); controls (182) | GALAD score | 200 days | High risk: above −0.63 |
| Patarat et al. (2021) [36] | RCS/2018–2020/Thailand | Healthy (76), HCC (153), fibrosis (20), MASLD (20) | Gene expression of the PBMCs: FLNA and CLU | NA | High risk: Ct value < 33 |
| Bianco et al. (2021) [31] | RCC/2008–2019/Europe | Non-cirrhotic MASLD patients: HCC (95); controls (356725) | PRS-HFC and PRS-5 | Once-in-a-lifetime | High risk: PRS-HFC ≥ 0.532 and PRS-5 ≥ 0.495 |
| Lewinska et al. (2021) [37] | RCS/2017–2020/Europe | MASLD-HCC (27), healthy control (35), without cirrhosis (9) | NHDS | NA | High risk: value > 0 |
| Guan et al. (2022) [34] | RCC/2016–2020/China | MASLD-HCC patients (139); MASLD patients (345) | AFP + PIVKA-II | 6 months |
|
| Fujiwara et al. (2022) [35] | RCS/2003–2015/Japan | MASLD patients (106), non-cirrhotic HCC (6), 3 minimal fibrosis patients | PLS-NAFLD Score | 2.3 years | High risk: value of 1 |
| Foda et al. (2023) [33] | PCS/NA/US, EU, East Asia | HCC patients (75), high risk of HCC (133), without HCC (293) | DELFI model | NA | High risk: DELFI score > 0.48 |
| Imai et al. (2023) [30] | RCS/2006–2021/Japan | HCC patients with MASLD/MASH (126) | FIB-4 index and NFS | NA | High risk: FIB-4 index ≥ 1.30; NFS ≥ −1.455 |
| Kim et al. (2024) [28] | RCS/2004–2007/Korea | MASLD patients (409088), patients developed HCC (841) | HCC-RIFLE model | NA | High risk: the risk score of 9–11 |
| Kobayashi et al. (2024) [29] | RCS/2012–2022/Japan | MASLD patients (405), HCC (8) | △LSM | 23.5 months | High risk: ΔLSM ≥ 19% |
RCC = retrospective case-control study, RCS = retrospective cohort study, PCS = prospective cohort study, MASH = metabolic dysfunction-associated steatohepatitis, HCC = hepatocellular carcinoma, MASLD = metabolic dysfunction-associated steatotic liver disease, PBMC = peripheral blood mononuclear cell, NHDS = the NAFLD-HCC diagnostic score, PRS-HFC = polygenic risk score for hepatic fatty content, AIM = apoptosis inhibitor of macrophage, AFP = alpha-fetoprotein, PIVKA-II = protein induced by vitamin K antagonist-II, PLS = prognostic liver signature, DELFI = DNA evaluation of fragments for early interception, FIB-4 = fibrosis-4, NFS = NAFLD fibrosis score, HCC-RIFLE model = HCC risk in non-alcoholic fatty liver disease, LSM = liver stiffness measurements.
Risk of HCC in non-cirrhotic patients with NAFLD
Male sex, obesity, and type 2 diabetes mellitus (T2DM) are well-established risk factors for the progression of MASLD and HCC [47], and these factors were also associated with the development of non-cirrhotic HCC in patients with MASLD in the studies we included. A study analyzing the relationship between MASLD-associated HCC found that all metabolic factors—obesity, T2DM, hypertension, and dyslipidemia—are associated with an increased risk of HCC, with the risk positively correlated with the number of metabolic factors [43].
Genetic factors, such as genes PNPLA3, MBOAT7, and TM6SF2, are implicated as risk factors for the development of HCC in patients with MASLD. In 2017, a retrospective cohort study analyzed 765 non-cirrhotic MASLD patients from Italy, revealing that the rs641738 T allele was associated with MASLD-related HCC, with an odds ratio (OR) of 1.65, 95% confidence interval (CI) 1.08–2.55, after adjusting for age, sex, obesity, and T2DM. When combined with data from an independent UK MASLD cohort, the T allele remained associated with HCC (OR 2.10, 95% CI 1.33–3.31) in the entire cohort of non-cirrhotic patients (n = 913, 41 with HCC) [38]. However, a Latin American cohort study suggested that the rs631738 C > T allele of MBOAT7 was not associated with an increased risk of HCC but was associated with a higher risk of MASLD [48].
Cholankeril et al. [46] conducted a retrospective cohort study of patients with MASLD seen at 130 Veterans Administration hospitals in the United States who were followed up at three-year intervals. The primary aim was to assess the effect of longitudinal changes in fibrosis scores on the risk of HCC in patients with MASLD. The hazard ratio for the HCC endpoint in this study was 57.7 for MASLD patients with a FIB-4 score greater than 2.67 at both the baseline and three-year follow-up nodes, suggesting that the degree of fibrosis is a risk factor for the development of HCC in MASLD, even in those without cirrhosis. Further details are provided in Table 2.
Table 2.
Risk factors associated with HCC development in non-cirrhotic patients with MASLD
| Independent predictor | (OR/RR, 95% CI) | Related article, author/year |
|---|---|---|
| Male sex | (7.77, 2.18–27.78) | Tobari et al. (2020) [41] |
| (2.57, 1.88–3.49) | Pinyopornpanish et al. (2021) [42] | |
| Obesitya | (1.69, 1.05–2.56) | Donati et al. (2017) [38] |
| (3.90, 2.02–7.40) | Xie et al. (2022) [43] | |
| T2DM | (1.56, 1.15–2.11) | Pinyopornpanish et al. (2021) [42] |
| (2.40, 1.25–4.23) | Xie et al. (2022) [43] | |
| (4.73, 2.75–8.30) | Ajmera et al. (2023) [44] | |
| Age > 65 years | (3.37, 2.47–4.59) | Pinyopornpanish et al. (2021) [42] |
| The MBOAT7 rs641738 T alleles | (2.10, 1.33–3.31) | Donati et al. (2017) [38] |
| TM6SF2, n E167K alleles | (1.99, 1.08–3.65) | Donati et al. (2017) [38] |
| PNPLA3, n I148M alleles | (1.61, 1.12–2.32) | Donati et al. (2017) [38] |
| Hyperlipidemia | (1.73, 1.68–2.32) | Phan et al. (2019) [40] |
| Light drinkerb | (4.89, 1.92–12.45) | Tobari et al. (2020) [41] |
| Elevated ALT | (2.69, 2.14–3.37) | Pinyopornpanish et al. (2021) [42] |
| Smoking historyc | (1.75, 1.23–2.49) | Pinyopornpanish et al. (2021) [42] |
| Hypertension | (1.80, 1.02–3.24) | Xie et al. (2022) [43] |
| Dyslipidemia | (2.60, 1.46–4.66) | Xie et al. (2022) [43] |
| Planet ≤ 150 × 109/L | (5.79, 1.67–20.1) | Huang et al. (2024) [14] |
| Sustained FIB-4 index > 2.67 | (57.70, 0.50–82.2) | Cholankeril et al. (2023) [46] |
Obesity: BMI > 30 kg/m2 or waist > 110 cm in man or 100 cm in woman.
Light drinker: ethanol intake < 30 g per week for men and < 20 g per week for women and drank > 120 g of ethanol per year.
Smoking history: smoking >10 years or 10 cigarettes per day.
T2DM = type 2 diabetes mellitus, ALT = alanine aminotransferase, FIB-4 = fibrosis-4.
HCC pathogenesis in non-cirrhotic MASLD patients with risk factor
The development of HCC in non-cirrhotic patients with MASLD involves a multifaceted interplay of metabolic dysfunction, inflammation, oxidative stress, and genetic predisposition. A comprehensive understanding of these mechanisms is crucial for early detection and effective management, providing insights into personalized risk assessment and potential therapeutic targets.
Metabolic dysregulation serves as a central driver in HCC development among non-cirrhotic MASLD patients. Insulin resistance, a hallmark of MASLD, promotes hepatic lipogenesis, leading to intracellular lipid accumulation. The excess lipids induce endoplasmic reticulum stress and mitochondrial dysfunction, initiating hepatocyte injury and carcinogenesis [49]. Dysregulated lipid metabolism further generates toxic lipid intermediates, such as free fatty acids and ceramides, exacerbating cellular injury and inflammation [50].
Chronic inflammation and oxidative stress also play pivotal roles in driving HCC in non-cirrhotic MASLD. Inflammatory cytokines, including interleukin-6 and tumor necrosis factor-alpha, perpetuate hepatocyte injury and activate oncogenic signaling pathways. Activation of hepatic stellate cells in response to inflammation promotes fibrogenesis, facilitating tumorigenesis through the secretion of pro-fibrotic factors and extracellular matrix remodeling [51]. Oxidative stress, originating from mitochondrial dysfunction and impaired antioxidant defenses, promotes DNA damage and genomic instability, facilitating malignant transformation.
Genetic predisposition influences HCC susceptibility in non-cirrhotic MASLD patients. Polymorphisms in genes associated with lipid metabolism (e.g. PNPLA3, TM6SF2) and inflammation (e.g. IL28B) modulate disease progression and HCC risk [52]. Epigenetic modifications, such as DNA methylation and histone acetylation, regulate gene expression patterns implicated in hepatocarcinogenesis. Understanding the interplay between genetic variants and environmental factors enables individualized risk stratification and targeted therapies.
Given the complexity of HCC development in non-cirrhotic MASLD, there is a critical need for the development of effective preventive strategies and therapeutic interventions. Addressing current data gaps, including identifying novel biomarkers, refining risk prediction models for HCC screening, and exploring targeted therapies tailored to the unique molecular pathways driving HCC, is essential for improving patient outcomes in this population.
Comparison of HCC screening methods for MASLD patients
Among the various HCC screening methods assessed, the DNA evaluation of fragments for early interception (DELFI) model demonstrated the highest predictive ability, with an area under the receiver operating characteristic curve (AUROC) of 0.98 [33]. The FIB-4 score exhibited the highest sensitivity at 95.20% [30], while the GALAD score boasted the highest specificity at 96.15% [32]. Details are provided in Table 3.
Table 3.
Assessments of various HCC screening methods in non-cirrhotic patients with MASLD
| Screening method | Sensitivity (%) | Specificity (%) | AUROC | HCC incidence |
|---|---|---|---|---|
| GALAD score [32] | 85.71 | 96.15 | 0.94 | 1.96% |
| PRS-HFC [31] | 43.00 | 80.00 | 0.64 | NA |
| PRS-5 [31] | 43.00 | 79.00 | 0.65 | NA |
| FIB-4 index [30] | 95.20 | NA | NA | 0.26% |
| NFS [30] | 93.70 | NA | NA | 0.18% |
| PIVKA-II [34] | 69.60 | 91.00 | 0.84 | NA |
| AFP + PIVKA-II [34] | 80.40 | 93.10 | 0.88 | NA |
| The expression of gene FLNA and CLU in PBMCs [36] | 85.00 | 88.40 | NA | NA |
| PLS- NAFLD Score [35] | NA | NA | MA | 2.80% |
| HCC-RIFLE model [28] | NA | NA |
|
|
| NHDS [37] | NA | NA | 0.91 | NA |
| △LSM [29] | NA | NA | NA | 0.61% |
| DELFI model [33] | 88.00 | 98.00 | 0.98 | NA |
NHDS = the NAFLD-HCC diagnostic score, AIM = apoptosis inhibitor of macrophage, AFP = alpha-fetoprotein, PIVKA-II = protein induced by vitamin K antagonist-II, PLS = prognostic liver signature, DELFI = DNA evaluation of fragments for early interception, FIB-4 = fibrosis-4, NFS = NAFLD fibrosis score, HCC-RIFLE = HCC risk in non-alcoholic fatty liver disease, LSM = liver stiffness measurements.
With increasing recognition of genetic factors in the pathogenesis and progression of MASLD, studies have explored polygenic risk scores to predict HCC prevalence in non-cirrhotic MASLD patients. Bianco et al. [31] developed two variant scoring systems based on polygenic risk scores, namely the polygenic risk score for hepatic fatty content (PRS-HFC) and the PRS-5 score adjusted for the gene HSD17B13. Although both scores exhibited good specificity (80%, 79%), their sensitivity was slightly lower (both 43%). However, due to the limited availability and cost of genetic screening technology, it may not be the primary choice for HCC screening in non-cirrhotic MASLD patients.
The DELFI model utilizes machine learning to assess alterations in cell-free DNA (cfDNA) fragmentation set coverage at transcription factor binding sites for distinguishing between HCC and non-cancer patients. Foda et al. [33] demonstrated high performance in cancer detection within the general population without cirrhosis, achieving an AUROC of 0.98 and a sensitivity of 88% at 98% specificity. The GALAD score evaluates the risk of HCC based on the patient’s sex, age, and serum levels of AFP, AFP subtype L3 (AFP-L3), and dis-gamma-carboxy prothrombinogen (DCP) [27, 32]. It surpasses other screening methods in performance, sensitivity, specificity, and accessibility of indicators.
Kim et al. [28] developed a new risk prediction model, the HCC risk in NAFLD (HCC-RIFLE model), and calculated the 5-year and 10-year risks of developing HCC in a high-risk group of non-cirrhotic MASLD patients, which were 1.38% and 3.03%, respectively. Fujiwara et al. [35] found that 2.8% of MASLD patients with mild fibrosis stratified as high risk by a 133-gene signature: prognostic liver signature score developed HCC during the follow-up period, demonstrating that these screenings are to some extent cost-effective, as the prevalence of HCC in these studies exceeded the 1.5% threshold.
In the context of imaging screening for non-cirrhotic MASLD patients, no established HCC screening algorithm has been identified. Currently, attention remains primarily focused on the cirrhotic population. A case-control study suggested utilizing magnetic resonance imaging (MRI) every six months instead of ultrasound for HCC screening, particularly in obese cirrhotic MASLD patients, where ultrasound sensitivity may be inadequate [53]. Additionally, considering the potential radiological damage caused by computed tomography (CT) examination, low-dose liver CT has been utilized for HCC monitoring in cirrhotic patients, with a sensitivity of 83.3% and a specificity of 95.6%, surpassing that of liver ultrasonography [54]. However, evidence supporting the application of these strategies in non-cirrhotic MASLD patients remains lacking.
Regrettably, no prospective randomized controlled trials analyzing the prevalence, tumor characteristics, and outcome prognosis of non-cirrhotic MASLD patients screened for HCC were identified. While most studies indicate that non-cirrhotic MASLD patients typically present with larger, more advanced tumors, their 5- or 10-year survival rates have not declined compared to cirrhotic HCC patients, nor has the rate of tumor recurrence after treatment been higher in non-cirrhotic HCC cases compared to cirrhotic MASLD-associated HCC patients [55].
Discussion
Given the low prevalence of HCC, universal screening for all non-cirrhotic patients may not be cost-effective. While there is evidence that HCC associated with MAFLD can occur in the absence of cirrhosis, current guidelines offer limited direction on HCC surveillance in patients with MAFLD outside the context of established cirrhosis. Therefore, it becomes essential to stratify risk and assess fibrosis stage among non-cirrhotic MASLD patients. In this review, we compared studies focused on risk analyses for HCC in non-cirrhotic MASLD patients and summarized the key findings. Independent risk factors for HCC in this subpopulation include male sex, age over 65 years, BMI over 30 kg/m2, waist circumference exceeding 110 cm, hypertension, T2MD, alcohol intake, smoking, dyslipidemia, elevated ALT, and platelet counts ≤ 150 × 109/L.
In cirrhotic MASLD patients, current guidelines primarily recommend biannual liver ultrasound supplemented with AFP testing [19–23]. However, there is less guidance on HCC screening in non-cirrhotic MASLD patients. Although cost-effectiveness data for HCC screening in non-cirrhotic MASLD patients with risk factors are lacking, the American Gastroenterological Association (AGA) expert consensus recommends initiating HCC screening in patients with MASLD and stage III fibrosis using biannual ultrasound, with or without AFP examination [21].
For patients with significant fibrosis (stage II), no expert consensus currently exists. The AGA guidance suggests further risk stratification in this population [21]. Pinyopornpanish et al. [42] found that non-cirrhotic MASLD patients with multiple risk factors for HCC had a significantly high incidence of HCC. In patients who were male, over 65 years old, smokers, had T2DM, elevated ALT levels, and used insulin, the incidence of HCC was 69.4 per 10,000 patients (95% CI 26.6–112.3), compared to the overall incidence among non-cirrhotic MASLD patients, which was 4.6 per 10,000 (95% CI 3.9–5.3) [42]. These findings suggest that, following appropriate risk stratification, screening non-cirrhotic MASLD patients with stage II fibrosis and multiple risk factors for HCC may be worth considering.
However, due to the lack of cost-effectiveness evidence, HCC screening in these patients should be a shared decision between the patient and healthcare provider. For patients who opt not to initiate HCC screening, non-invasive fibrosis monitoring every 6 to 12 months is warranted to prevent missing the opportunity to screen patients who progress to stage III fibrosis.
Regarding screening tools, published data present conflicting results. This review did not identify any cost-effectiveness analyses specifically focused on HCC screening in non-cirrhotic MASLD patients, revealing a significant research gap in this area. In a mixed population of non-cirrhotic Asian patients with chronic hepatitis B and NASH, ultrasound was found to be the most cost-effective screening modality [56]. However, another study involving cirrhotic NAFLD patients indicated that MRI, particularly in those with poor visualization (ultrasound visualization score C), was more cost-effective. MRI provided a lower incremental cost-effectiveness ratio (ICER) than ultrasound alone while offering significantly higher sensitivity [57]. These findings suggest that, in the era of precision medicine, a one-size-fits-all screening strategy may not be appropriate. Instead, combining ultrasound with other imaging modalities or new serologic markers may be more suitable for specific subsets of non-cirrhotic MASLD patients.
The AGA guidance suggests that ultrasound screening, when performed with a good acoustic window, is generally both highly accurate and cost-effective for detecting HCC in non-cirrhotic MASLD patients [21]. However, in cases where ultrasonography quality is suboptimal (e.g. due to obesity), either CT or MRI should be used as alternative screening methods [21].
In addition to imaging, several novel serologic markers, including cfDNA, have shown promise for early HCC detection [33]. However, due to limited availability and high costs, cfDNA testing is not yet a practical primary choice for HCC screening. Additionally, combining serum AFP with protein induced by vitamin K absence or antagonist-II (PIVKA-II) has demonstrated higher sensitivity and specificity compared to AFP alone [34]. Incorporating PIVKA-II into routine HCC screening protocols could therefore enhance screening accuracy, pending on the evidence of cost-effectiveness. This review also found the GALAD score as a promising tool with superior efficacy in HCC screening for MASLD patients. The GALAD score, which incorporates serological markers such as AFP, AFP-L3, and DCP along with patient demographics, has demonstrated the ability to predict HCC prevalence trends as far as 200 days to 1.5 years in advance. Several studies suggest that the GALAD score is a valuable complementary method for HCC screening in this population [27, 32].
Drawing on the available evidence, we propose an HCC surveillance strategy tailored to high-risk MASLD patients without cirrhosis, as detailed in Figure 2. For patients with fibrosis stage F2 and multiple risk factors for HCC, we recommend either initiating HCC screening based on a shared decision between the patient and provider, or closely monitoring fibrosis progression to stage F3, at which point screening should begin. Regardless of specific HCC risk factors, all MASLD patients with fibrosis stage F3 or higher should undergo regular HCC screening at six-month intervals [21]. While ultrasound is the primary screening tool for most patients, CT or MRI should be used when ultrasound quality is suboptimal, or taking the approach of performing ultrasound every 6 months, alternating with MRI or CT scan every 12 months. Our review also found that combining the GALAD score or PIVKA-II with imaging may enhance screening performance. However, the cost-effectiveness of this combined approach has not been evaluated in non-cirrhotic MASLD patients, making these tools optional at present.
Figure 2.
Proposed screening framework in this review. We advocated for prioritizing risk stratification to screen patients with non-cirrhotic MASLD. Additionally, we provided specific recommendations for HCC screening methods and intervals, considering the validity and ease of implementation of the stratification methods. Risk factors include male sex, age over 65 years, BMI over 30 kg/m2, waist circumference exceeding 110 cm, hypertension, type 2 diabetic, alcohol intake, smoking, dyslipidemia, elevated ALT, and platelet counts 150 × 109/L. BMI = body mass index = AFP = alpha-fetoprotein, GALAD = score based on gender, age, AFP-L3, AFP, and des-carboxy-prothrombin value, PIVKA-Il = protein induced by vitamin K antagonist-II, ALT = alanine aminotransferase, HCC = hepatocellular carcinoma.
This narrative review has several limitations. While we have proposed initial thoughts on HCC screening for non-cirrhotic MASLD patients, these suggestions are primarily based on a general synthesis of the existing literature and collective clinical experience. Additionally, although we aim to provide comprehensive proposals, including considerations of cost-effectiveness, this review primarily serves as a description of current screening methods and an early exploration of potential frameworks and future strategies for risk-based screening, given the significant research gaps in this area. Furthermore, the review mainly included English and Chinese articles, which may have led to the exclusion of relevant studies published in other languages. Moreover, most of the included studies were retrospective in nature and did not assess patients’ survival outcomes following real-world screening, limiting the strength of the conclusions.
Future research should focus on evaluating the efficacy of current HCC screening methods in non-cirrhotic MASLD patients and conducting cost-effectiveness comparisons. Multicenter, prospective studies are needed to investigate the optimal screening intervals for this population. Additionally, assessing long-term survival outcomes after HCC detection will be critical for developing more comprehensive screening guidelines for MASLD patients without cirrhosis.
Conclusions
MASLD encompasses a spectrum of liver conditions, representing a significant public health challenge due to its high prevalence and strong association with HCC. Non-cirrhotic patients with MASLD face a substantial risk of developing HCC, highlighting the need for effective and targeted screening strategies. While current guidelines recommend biannual ultrasound with or without AFP testing for cirrhotic MASLD patients, specific guidance for non-cirrhotic patients remains lacking.
Although novel serologic markers and risk prediction models show promise as screening tools in MSALD patients, comprehensive comparative studies evaluating their validity, sensitivity and specificity are limited. Stratifying the risk among non-cirrhotic MASLD patients could improve the efficiency and cost-effectiveness of HCC screening. Based on our comprehensive review of independent risk factors for HCC in non-cirrhotic MASLD patients, we propose a risk-stratified approach to HCC surveillance using ultrasound, with or without PIVKA-II or the GALAD score. Circulating free DNA, though promising, was excluded from this proposal due to the practical challenges associated with frequent testing and high costs. We hope that this narrative review offers clinicians valuable insights and preliminary frameworks for HCC screening in non-cirrhotic MASLD patients. Future research should prioritize identifying high-risk individuals and assessing the long-term outcomes of HCC screening in this population.
Supplementary Material
Acknowledgements
The authors extend their gratitude to Dr Lei Ma and Dr Mengqi Li for their invaluable suggestions and insightful discussions during the preparation of the initial manuscript draft.
Contributor Information
Ke Mi, Center of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, P. R. China.
Tingdan Ye, Center of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, P. R. China.
Lin Zhu, Center of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, P. R. China.
Calvin Q Pan, Center of Liver Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, P. R. China; Division of Gastroenterology and Hepatology, Department of Medicine, NYU Langone Health, New York University Grossman School of Medicine, New York, NY, USA.
Supplementary data
Supplementary data is available at Gastroenterology Report online.
Authors’ contributions
K.M. and C.Q.P. conceived the concept, designed the review, and outlined the study. K.M., T.Y., and L.Z. carried out the narrative review, data screening, retrieval, and extraction. K.M. and C.Q.P. interpreted and analyzed the data. K.M. drafted the initial manuscript, and C.Q.P. revised it rigorously. All authors contribute to the final version of the manuscript, and the corresponding author confirmed that all listed authors meet the criteria for authorship, with no other eligible authors omitted.
Funding
None declared.
Conflicts of interest
Calvin Q. Pan received institutional research grants from Gilead Sciences and Wuxi Hisky Medical Technologies Co., Ltd.
Other authors have no financial interests to be disclosed.
Data availability
Materials are from published articles. All data supporting the findings of this narrative review are included in this published article.
Ethics approval and consent to participate
Not applicable.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
Materials are from published articles. All data supporting the findings of this narrative review are included in this published article.