한빛사논문
- 조회247
Huapeng Lin 1,2,∗, Guanlin Li 1,2,∗, Adèle Delamarre 3,4, Sang Hoon Ahn 5,6, Xinrong Zhang 1,2, Beom Kyung Kim 5,6, Lilian Yan Liang 1,2, Hye Won Lee 5,6, Grace Lai-Hung Wong 1,2, Pong-Chi Yuen 7, Henry Lik-Yuen Chan 1,8, Stephen Lam Chan 9,10, Vincent Wai-Sun Wong 1,2, Victor de Lédinghen 3,4, Seung Up Kim 5,6, Terry Cheuk-Fung Yip 1,2
1Medical Data Analytics Center, Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong
2State Key Laboratory of Digestive Disease, The Chinese University of Hong Kong, Hong Kong
3Hepatology Unit, Hôpital Haut Lévêque, Bordeaux University Hospital, Bordeaux, France
4INSERM U1312, Bordeaux University, Bordeaux, France
5Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Korea
6Yonsei Liver Center, Severance Hospital, Seoul, Korea
7Department of Computer Science, Hong Kong Baptist University, Hong Kong
8Union Hospital, Hong Kong
9Department of Clinical Oncology, Sir YK Pao Centre for Cancer, The Chinese University of Hong Kong, Hong Kong
10State Key Laboratory of Translational Oncology, The Chinese University of Hong Kong, Hong Kong
∗Huapeng Lin and Guanlin Li contributed equally to this study.
Correspondence: Victor de Lédinghen, MD, PhD, Seung Up Kim, MD, PhD, Terry Cheuk-Fung Yip
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.
논문정보
- Research article
- 2023년 11월 (BRIC 등록일 2023-12-04)
- 국내(교신)+국외 연구진
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