A liver stiffness-based etiology-independent machine learning algorithm to predict hepatocellular carcinoma.

Abstract

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 two 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 [CI] 0.85-0.92) and 0.91 (95%CI 0.87-0.95), respectively. The area under ROC curves of the SMART-HCC score for HCC in 5 years were ≥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 seven hepatitis B related risk scores. Using dual cut-offs 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.

CONCLUSION:

The SMART-HCC score is a useful machine learning-based tool for clinicians to stratify HCC risk in patients with CLDs.

• Authors: Huapeng Lin, Guanlin Li, Adèle Delamarre, Sang Hoon Ahn, Xinrong Zhang, Beom Kyung Kim, Lilian Yan Liang, Hye Won Lee, Grace Lai-Hung Wong, Pong-Chi Yuen, Henry Lik-Yuen Chan, Stephen Lam Chan, Vincent Wai-Sun Wong, Victor de Lédinghen, Seung Up Kim, Terry Cheuk-Fung Yip
• Journal: Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association (Clin Gastroenterol Hepatol) (Nov 20 2023) ISSN: 1542-7714 [Electronic] United States
• PMID: 37993034 (Publication Type: Journal Article)
• Copyright: Copyright © 2023 AGA Institute. Published by Elsevier Inc. All rights reserved.