Unmet needs around HCC monitoring, surveillance & diagnosis
Several risk prediction models for liver cancer have been explored in the literature, but not implemented in clinical practice
Up to 90% of HCC cases arise in the presence of liver cirrhosis;1 however, there is a high need for reliable and effective risk prediction protocols for HCC2
Risk prediction models for HCC evaluated so far present some limitations and are not established in routine clinical practice:
Virological indexes demand high economic costs3-5
Sociodemographic and clinical scores focus on a specific etiology and liver function measures which comprise applicability to other etiologies6-10
Genetic risk scores may not be applicable for routine use for the general population11
Given the complexity and heterogeneity of HCC, risk prediction models or scores that are tailored to the individual are warranted12
Screening of people at risk of developing HCC should rely on multimodal information: clinical, imaging and molecular
The sensitivity of AFP testing, even when combined with ultrasound screening, remains suboptimal in detecting early-stage HCC;13 however, HCC-screening biomarkers are lacking and not implemented in clinical practice14
The sensitivity of AFP alone is 49%.13 False positive AFP elevations can occur with increased serum ALT levels,15 and up to 40–50% of HCCs do not have elevated levels of AFP16
The sensitivity of US alone is 51.6%.13 The applicability of US-based screening is limited by a significant heterogeneity in sensitivity reported across studies (21–89%)17
Potential biomarkers are yet to be clinically validated, undergo the process of clinical deployment and implementation, and incorporation in clinical practice guidelines14
The risk of developing HCC can vary depending on different etiologies of origin; therefore, risk stratification models are needed
The recent shift of HCC etiologies from viral to metabolic liver diseases has significantly increased the potential target population to be screened for HCC development14
HCC screening in the emerging target population is elusive and not cost-effective due to the shift in etiologies, from hepatitis to metabolic liver diseases that can develop also in the absence of cirrhosis14
Identifying patients with pre-cirrhotic NAFLD who have a high enough HCC risk to justify HCC screening represents one of the greatest clinical challenges in NAFLD. Validated models are not yet available for patients with NAFLD18
Although recommended by several guidelines, HCC surveillance is limited and underutilised in real-world practice
HCC surveillance remains a major health concern even in countries where jurisdictional surveillance programmes are implemented, with ~50–60% of the high-risk population not receiving routine screening19,20
The real-world utilisation rate of HCC surveillance is below 20% due to multiple patient- and provider-related factors21
Only 15% of primary care practitioners are reported to order biannual HCC surveillance in the USA, many incorrectly (liver enzymes, or AFP alone without US)22
Access to HCC surveillance programs remains a key barrier to the successful monitoring of people at risk of developing HCC
High level of burden on patients and healthcare systems, and poor patient compliance all represent barriers to successful early detection of HCC14,27-28
| High patient burden | Low compliance | High healthcare costs |
Patients face barriers to HCC surveillance:28 • 30.5% faced scheduling process • 25.3% struggled with costs of surveillance tests • 19.6% faced uncertainty of where to get surveillance US performed • 17.3% had difficulty with transportation |
Of patients have good compliance and are able to follow guidelines or expert suggestion for liver cancer surveillance27 |
Cost-effectiveness represents a barrier to the implementation of HCC surveillance programmes, especially considering the emerging shift from viral to metabolic etiologies of HCC, leading to an increase of the target population. Therefore, a stratified surveillance regimen is necessary to reduce the burden of liver cancer screening on healthcare systems14 |
References
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- Liu Y, et al. Front. Public Health 2022;10:955287.
- Wong VW, et al. J Clin Oncol 2010;28:1660–5.
- Yuen MF, et al. J Hepatol 2009;50:80–8.
- Yang HI, et al. Lancet Oncol 2011;12:568–74.
- Rau HH, et al. Comput Methods Programs Biomed 2016;125:58–65.
- Wen CP, et al. J Natl Cancer Inst 2012;104:1599–611.
- Yu C, et al. Int J Cancer 2021;148:2924–34.
- Jain A, et al. Comput Intell Neurosci 2022;2022:8154523.
- Kim HY, et al. J Hepatol 2022;76:311–8.
- De Vincentis A, et al. Clin Gastroenterol Hepatol 2022;20:658–73.
- Fujiwara N, et al. J Hepatol 2018; 68: 526–549.
- Singal AG, et al. Hepatol Commun 2022;6:2925–2936.
- Lee Y, et al. Hepatology 2022;00:1–45.
- Yang JD, et al. Cancer Epidemiol Biomarkers Prev 2017;26:1085–1092.
- Parikh ND, et al. Cancer Epidemiol Biomarkers Prev 2020;29:2495–2503.
- Tzartzeva K, et al. Gastroenterology 2018;154:1706–18. e1.
- Ioannou GN. J Hepatol 2021;75:1476–1484.
- Xu K, et al. Ann Glob Health 2017;83:281–92.
- Dai J, et al. Cancer Manag Res 2020;12:6209–20.
- Singal AG. and El-Serag HB. Clin Gastroenterol Hepatol 2015;13:2140–51.
- Dalton-Fitzgerald E, et al. Clin Gastroenterol Hepatol 2015;13:791–8.e1.
- Singal AG, et al. PLoS Med 2014;11:e1001624.
- Singal AG, et al. Am J Med 2017; 130:1099–1106.
- Cadier B, et al. Hepatology 2017;65:1237–48.
- Mourad A, et al. Hepatology 2014;59:1471–81.
- Ding H, et al. Hepatoma Res 2021;7:17.
- Farvardin S, et al. Hepatology 2017;65:875–84.
