Semin Liver Dis 2017; 37(04): 287-295
DOI: 10.1055/s-0037-1607452
Review Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

HCC Risk Scores: Useful or Not?

Morris Sherman
1   Department of Medicine, University Health Network, Toronto, Ontario, Canada
› Institutsangaben
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Publikationsverlauf

Publikationsdatum:
22. Dezember 2017 (online)

Abstract

The advent and efficacy of surveillance for hepatocellular carcinoma (HCC) has necessitated the refinement of assessing who is at risk for this cancer. Initially, risk was assessed for all individuals with hepatitis B and all those with cirrhosis. However, the majority of these individuals do not develop HCC so that providing surveillance for all is a waste of resources. There are now many different scores that have been developed that allow better identification of who is at risk and who is not. Specific models have been developed for hepatitis B before and on treatment, for hepatitis C before and after treatment, and for cirrhosis in general. There are also models for assessing risk in the general population. Some models can only be applied to patients coming from the population in which the score was developed (e.g., hepatitis B). Others are more generalizable. Many lack external validation. With some exceptions, the models do not attempt to assess the score at which surveillance should start. Overall, the models provide some useful guidance as to who does not need to undergo surveillance, but the long-term performance and how changes in risk score correlate with changes in HCC risk has not been completely assessed.

 
  • References

  • 1 Lederle FA, Pocha C. Screening for liver cancer: the rush to judgment. Ann Intern Med 2012; 156 (05) 387-389
  • 2 Bruix J, Sherman M. ; Practice Guidelines Committee, American Association for the Study of Liver Diseases. Management of hepatocellular carcinoma. Hepatology 2005; 42 (05) 1208-1236
  • 3 Yuen MF, Tanaka Y, Fong DY. , et al. Independent risk factors and predictive score for the development of hepatocellular carcinoma in chronic hepatitis B. J Hepatol 2009; 50 (01) 80-88
  • 4 Wong GL, Chan HL, Chan HY. , et al. Accuracy of risk scores for patients with chronic hepatitis B receiving entecavir treatment. Gastroenterology 2013; 144 (05) 933-944
  • 5 Chen CJ, Yang HI, Su J. , et al; REVEAL-HBV Study Group. Risk of hepatocellular carcinoma across a biological gradient of serum hepatitis B virus DNA level. JAMA 2006; 295 (01) 65-73
  • 6 Yang HI, Sherman M, Su J. , et al. Nomograms for risk of hepatocellular carcinoma in patients with chronic hepatitis B virus infection. J ClinOncol 2010; 28 (14) 2437-2444
  • 7 Yang HI, Yuen MF, Chan HL. , et al; REACH-B Working Group. Risk estimation for hepatocellular carcinoma in chronic hepatitis B (REACH-B): development and validation of a predictive score. Lancet Oncol 2011; 12 (06) 568-574
  • 8 Lee MH, Yang HI, Liu J. , et al; R.E.V.E.A.L.-HBV Study Group. Prediction models of long-term cirrhosis and hepatocellular carcinoma risk in chronic hepatitis B patients: risk scores integrating host and virus profiles. Hepatology 2013; 58 (02) 546-554
  • 9 Yang HI, Tseng TC, Liu J. , et al. Incorporating serum level of hepatitis B surface antigen or omitting level of hepatitis B virus DNA does not affect calculation of risk for hepatocellular carcinoma in patients without cirrhosis. Clin Gastroenterol Hepatol 2016; 14 (03) 461-468.e2
  • 10 Arends P, Sonneveld MJ, Zoutendijk R. , et al; VIRGIL Surveillance Study Group. Entecavir treatment does not eliminate the risk of hepatocellular carcinoma in chronic hepatitis B: limited role for risk scores in Caucasians. Gut 2015; 64 (08) 1289-1295
  • 11 Abu-Amara M, Cerocchi O, Malhi G. , et al. The applicability of hepatocellular carcinoma risk prediction scores in a North American patient population with chronic hepatitis B infection. Gut 2016; 65 (08) 1347-1358
  • 12 Wong GL, Chan HL, Wong CK. , et al. Liver stiffness-based optimization of hepatocellular carcinoma risk score in patients with chronic hepatitis B. J Hepatol 2014; 60 (02) 339-345
  • 13 Papatheodoridis G, Dalekos G, Sypsa V. , et al. PAGE-B predicts the risk of developing hepatocellular carcinoma in Caucasians with chronic hepatitis B on 5-year antiviral therapy. J Hepatol 2016; 64 (04) 800-806
  • 14 Kim MN, Hwang SG, Rim KS. , et al. Validation of PAGE-B model in Asian chronic hepatitis B patients receiving entecavir or tenofovir. Liver Int 2017; [Epub ahead of print] DOI: 10.1111/liv.13450.
  • 15 Riveiro-Barciela M, Tabernero D, Calleja JL. , et al. Effectiveness and safety of entecavir or tenofovir in a Spanish cohort of chronic hepatitis B patients: validation of the Page-B score to predict hepatocellular carcinoma. Dig Dis Sci 2017; 62 (03) 784-793
  • 16 Jung KS, Kim SU, Ahn SH. , et al. Risk assessment of hepatitis B virus-related hepatocellular carcinoma development using liver stiffness measurement (FibroScan). Hepatology 2011; 53 (03) 885-894
  • 17 Lok AS, Seeff LB, Morgan TR. , et al; HALT-C Trial Group. Incidence of hepatocellular carcinoma and associated risk factors in hepatitis C-related advanced liver disease. Gastroenterology 2009; 136 (01) 138-148
  • 18 El-Serag HB, Kanwal F, Davila JA, Kramer J, Richardson P. A new laboratory-based algorithm to predict development of hepatocellular carcinoma in patients with hepatitis C and cirrhosis. Gastroenterology 2014; 146 (05) 1249.e1-1255.e1
  • 19 White DL, Richardson P, Tayoub N, Davila JA, Kanwal F, El-Serag HB. The updated model: an adjusted serum alpha-fetoprotein-based algorithm for hepatocellular carcinoma detection with hepatitis C virus-related cirrhosis. Gastroenterology 2015; 149 (07) 1986-1987
  • 20 Singal AG, Mukherjee A, Elmunzer BJ. , et al. Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma. Am J Gastroenterol 2013; 108 (11) 1723-1730
  • 21 Chang KC, Wu YY, Hung CH. , et al. Clinical-guide risk prediction of hepatocellular carcinoma development in chronic hepatitis C patients after interferon-based therapy. Br J Cancer 2013; 109 (09) 2481-2488
  • 22 Masuzaki R, Tateishi R, Yoshida H. , et al. Prospective risk assessment for hepatocellular carcinoma development in patients with chronic hepatitis C by transient elastography. Hepatology 2009; 49 (06) 1954-1961
  • 23 Chang KC, Hung CH, Lu SN. , et al. A novel predictive score for hepatocellular carcinoma development in patients with chronic hepatitis C after sustained response to pegylated interferon and ribavirin combination therapy. J AntimicrobChemother 2012; 67 (11) 2766-2772
  • 24 Poynard T, Vergniol J, Ngo Y. , et al; FibroFrance Study Group; Epic3 Study Group; Bordeaux HCV Study Group. Staging chronic hepatitis C in seven categories using fibrosis biomarker (FibroTest™) and transient elastography (FibroScan®). J Hepatol 2014; 60 (04) 706-714
  • 25 Ganne-Carrié N, Layese R, Bourcier V. , et al; ANRS CO12 CirVir Study Group. Nomogram for individualized prediction of hepatocellular carcinoma occurrence in hepatitis C virus cirrhosis (ANRS CO12 CirVir). Hepatology 2016; 64 (04) 1136-1147
  • 26 Flemming JA, Yang JD, Vittinghoff E, Kim WR, Terrault NA. Risk prediction of hepatocellular carcinoma in patients with cirrhosis: the ADRESS-HCC risk model. Cancer 2014; 120 (22) 3485-3493
  • 27 Wen CP, Lin J, Yang YC. , et al. Hepatocellular carcinoma risk prediction model for the general population: the predictive power of transaminases. J Natl Cancer Inst 2012; 104 (20) 1599-1611
  • 28 Hung YC, Lin CL, Liu CJ. , et al. Development of risk scoring system for stratifying population for hepatocellular carcinoma screening. Hepatology 2015; 61 (06) 1934-1944
  • 29 Michikawa T, Inoue M, Sawada N. , et al; Japan Public Health Center-based Prospective Study Group. Development of a prediction model for 10-year risk of hepatocellular carcinoma in middle-aged Japanese: the Japan Public Health Center-based Prospective Study Cohort II. Prev Med 2012; 55 (02) 137-143