Hepatocellular carcinoma risk stratification to identify patients suitable for intensive surveillance in viral hepatitis: the SELECT score

Abstract

Objective A risk-stratification strategy can improve the effectiveness of intensive hepatocellular carcinoma (HCC) surveillance with an alternative modality. However, such strategies and prediction models incorporating ultrasound features remain undeveloped for a suitable population. Therefore, we developed and validated an HCC risk prediction model using ultrasound features in patients with viral hepatitis who are potentially eligible for intensive surveillance. Materials and methods This retrospective multicenter study included 17,557 HCC-na & iuml;ve patients with viral hepatitis who underwent US surveillance between 2005 and 2015. In the development dataset (n = 7918), clinical and US features were analyzed to establish the prediction model. Factors associated with HCC were identified by multivariable Cox regression analysis. Model performance was compared to existing prediction models in internal (n = 3393) and external (n = 6246) validation datasets. Results The SELECT model included age, male sex, diabetes, serum albumin and alanine aminotransferase levels, platelet count, and ultrasound-detected cirrhosis and multiple cirrhotic nodules. In the external validation dataset, the low-, intermediate-, and high-risk groups had 0.8%, 6.9%, and 16.1% 5-year cumulative HCC incidence, respectively. In those with an estimated annual HCC incidence >= 2.5% (SELECT score > -2.04), the 5-year cumulative HCC incidence was 15.5%. The SELECT model had better discrimination capability than aMAP, THRI, ADRESS-HCC, the Velazquez score, and mPAGE-B (Uno C-index, 0.791 vs. 0.740, 0.668, 0.658, 0.650, and 0.740, respectively; all adjusted p < 0.001). Conclusion The SELECT model better estimated HCC risk than other models in viral hepatitis patients. Intensive surveillance with alternative modalities may be considered based on this model.