Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C.
OBJECTIVE
Assessment of the risk of hepatocellular carcinoma (HCC) development is essential for formulating personalized surveillance or antiviral treatment plan for chronic hepatitis C. We aimed to build a simple model for the identification of patients at high risk of developing HCC.


METHODS
Chronic hepatitis C patients followed for at least 5 years (n=1003) were analyzed by data mining to build a predictive model for HCC development. The model was externally validated using a cohort of 1072 patients (472 with sustained virological response (SVR) and 600 with nonSVR to PEG-interferon plus ribavirin therapy).


RESULTS
On the basis of factors such as age, platelet, albumin, and aspartate aminotransferase, the HCC risk prediction model identified subgroups with high-, intermediate-, and low-risk of HCC with a 5-year HCC development rate of 20.9%, 6.3-7.3%, and 0-1.5%, respectively. The reproducibility of the model was confirmed through external validation (r(2)=0.981). The 10-year HCC development rate was also significantly higher in the high-and intermediate-risk group than in the low-risk group (24.5% vs. 4.8%; p<0.0001). In the high-and intermediate-risk group, the incidence of HCC development was significantly reduced in patients with SVR compared to those with nonSVR (5-year rate, 9.5% vs. 4.5%; p=0.040).


CONCLUSIONS
The HCC risk prediction model uses simple and readily available factors and identifies patients at a high risk of HCC development. The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC.
Division of Gastroenterology and Hepatology, Musashino Red Cross Hospital, Tokyo, Japan.
Masayuki
M
Kurosaki
Naoki
N
Hiramatsu
Minoru
M
Sakamoto
Yoshiyuki
Y
Suzuki
Manabu
M
Iwasaki
Akihiro
A
Tamori
Kentaro
K
Matsuura
Sei
S
Kakinuma
Fuminaka
F
Sugauchi
Naoya
N
Sakamoto
Mina
M
Nakagawa
Namiki
N
Izumi
D000328:Adult; D000368:Aged; D000998:Antiviral Agents / Q000627:therapeutic use; D006528:Carcinoma, Hepatocellular / Q000453:epidemiology*; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods* / Q000706:statistics & numerical data; D003663:Decision Trees; D005260:Female; D019698:Hepatitis C, Chronic / Q000188:drug therapy / Q000453:epidemiology*; D006801:Humans; D007372:Interferons / Q000627:therapeutic use; D008113:Liver Neoplasms / Q000453:epidemiology*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011092:Polyethylene Glycols / Q000627:therapeutic use; D012254:Ribavirin / Q000627:therapeutic use; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors
D000998:Antiviral Agents; D011092:Polyethylene Glycols; D012254:Ribavirin; D007372:Interferons