A Liver Stiffness–Based Etiology-Independent Machine Learning Algorithm to Predict Hepatocellular Carcinoma

Background & Aims: The existing hepatocellular carcinoma (HCC) risk scores have modest accuracy, and most are specific to chronic hepatitis B infection. In this study, we developed and validated a liver stiffness–based machine learning algorithm (ML) for prediction and risk stratification of HCC in various chronic liver diseases (CLDs). Methods: MLs were trained for prediction of HCC in 5155 adult patients with various CLDs in Korea and further tested in 2 prospective cohorts from Hong Kong (HK) (N = 2732) and Europe (N = 2384). Model performance was assessed according to Harrell's C-index and time-dependent receiver operating characteristic (ROC) curve. Results: We developed the SMART-HCC score, a liver stiffness–based ML HCC risk score, with liver stiffness measurement ranked as the most important among 9 clinical features. The Harrell's C-index of the SMART-HCC score in HK and Europe validation cohorts were 0.89 (95% confidence interval, 0.85–0.92) and 0.91 (95% confidence interval, 0.87–0.95), respectively. The area under ROC curves of the SMART-HCC score for HCC in 5 years was ≥0.89 in both validation cohorts. The performance of SMART-HCC score was significantly better than existing HCC risk scores including aMAP score, Toronto HCC risk index, and 7 hepatitis B–related risk scores. Using dual cutoffs of 0.043 and 0.080, the annual HCC incidence was 0.09%–0.11% for low-risk group and 2.54%–4.64% for high-risk group in the HK and Europe validation cohorts. Conclusions: The SMART-HCC score is a useful machine learning–based tool for clinicians to stratify HCC risk in patients with CLDs.