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

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*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

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 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.

Original languageEnglish
Pages (from-to)602-610.e7
Number of pages9
JournalClinical Gastroenterology and Hepatology
Volume22
Issue number3
Early online date21 Nov 2023
DOIs
Publication statusPublished - Mar 2024

Scopus Subject Areas

  • Hepatology
  • Gastroenterology

User-Defined Keywords

  • Artificial Intelligence
  • Cirrhosis
  • Liver Cancer
  • Liver Fibrosis
  • Transient Elastography

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