Liver Cancer Risk Prediction Models
The following risk prediction models are intended primarily for research use and have been peer-reviewed, meaning the methodology and results of these models have been evaluated by qualified scientists and clinicians and published in scientific and medical journals.
Absolute Risk Prediction Models
Absolute cancer risk is the probability that an individual with given risk factors and a given age will develop cancer over a defined period of time. Examples of these risk factors include race, age, sex, genetics, body mass index, family history of cancer, history of tobacco use, use of aspirin and nonsteroidal anti-inflammatory drugs (NSAIDS), physical activity, use of hormone replacement therapy, reproductive factors, history of cancer screening, and dietary factors.
Developing statistical models that estimate the probability of developing cancer over a defined period of time will help clinicians identify individuals at higher risk of specific cancers, allowing for earlier or more frequent screening and counseling of behavioral changes to decrease risk. These types of models also will be useful for designing future chemoprevention and screening intervention trials in individuals at high risk of specific cancers in the general population.
- 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. Oct 17 2012;104(20):1599-1611.
- Michikawa T, Inoue M, Sawada N, et al. 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. Aug 2012;55(2):137-143.
Risk Prediction Models for People at High Risk
These models estimate the likelihood of detecting a mutation in a cancer susceptibility gene (e.g., BRCA1 and BRCA2) in a given family or individual.
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- 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 Antimicrob Chemother. Nov 2012;67(11):2766-2772.
- Kurosaki M, Hiramatsu N, Sakamoto M, et al. Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C. J Hepatol. Mar 2012;56(3):602-608
- Abu Dayyeh BK, Yang M, Fuchs BC, et al. A functional polymorphism in the epidermal growth factor gene is associated with risk for hepatocellular carcinoma. Gastroenterology. Jul 2011;141(1):141-149.
- Kuo YH, Lu SN, Hung CH, et al. Liver stiffness measurement in the risk assessment of hepatocellular carcinoma for patients with chronic hepatitis. Hepatol Int. 2010;4(4):700-706.
- Yang HI, Sherman M, Su J, et al. Nomograms for risk of hepatocellular carcinoma in patients with chronic hepatitis B virus infection. J Clin Oncol. May 10 2010;28(14):2437-2444.
- Wong VW, Chan SL, Mo F, et al. Clinical scoring system to predict hepatocellular carcinoma in chronic hepatitis B carriers. J Clin Oncol. Apr 1 2010;28(10):1660-1665.
- 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. Jan 2009;50(1):80-88.
- Masuzaki R, Tateishi R, Yoshida H, et al. Risk assessment of hepatocellular carcinoma in chronic hepatitis C patients by transient elastography. J Clin Gastroenterol. Aug 2008;42(7):839-843.
- Ikeda K, Arase Y, Saitoh S, et al. Prediction model of hepatocarcinogenesis for patients with hepatitis C virus-related cirrhosis. Validation with internal and external cohorts. J Hepatol. Jun 2006;44(6):1089-1097.
- Carlos RC, Kim HM, Hussain HK, Francis IR, Nghiem HV, Fendrick AM. Developing a prediction rule to assess hepatic malignancy in patients with cirrhosis. AJR Am J Roentgenol. Apr 2003;180(4):893-900.