Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C

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• 作者：Masayuki Kurosaki¹ ; Naoki Hiramatsu² ; Minoru Sakamoto³ ; Yoshiyuki Suzuki⁴ ; Manabu Iwasaki⁵ ; Akihiro Tamori⁶ ; Kentaro Matsuura⁷ ; Sei Kakinuma⁸ ; Fuminaka Sugauchi⁹ ; Naoya Sakamoto⁸ ; Mina Nakagawa⁸ ; Namiki Izumi¹ ; ^{nizumi@musashino.jrc.or.jp}
• 关键词：Decision tree ; Prediction ; Pegylated interferon ; Ribavirin ; Risk
• 刊名：Journal of Hepatology
• 出版年：2012
• 出版时间：March 2012
• 年：2012
• 卷：56
• 期：3
• 页码：602-608
• 全文大小：678 K

文摘

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Background & Aims

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 (r² = 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.