Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study

Moon Haeng Hur, Min Kyung Park, Terry Cheuk Fung Yip, Chien Hung Chen, Hyung Chul Lee, Won Mook Choi, Seung Up Kim, Young Suk Lim, Soo Young Park, Grace Lai Hung Wong, Dong Hyun Sinn, Young Joo Jin, Sung Eun Kim, Cheng Yuan Peng, Hyun Phil Shin, Chi Yi Chen, Hwi Young Kim, Han Ah Lee, Yeon Seok Seo, Dae Won JunEileen L. Yoon, Joo Hyun Sohn, Sang Bong Ahn, Jae Jun Shim, Soung Won Jeong, Yong Kyun Cho, Hyoung Su Kim, Myoung Jin Jang, Yoon Jun Kim, Jung Hwan Yoon, Jeong Hoon Lee

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

INTRODUCTION: Tenofovir disoproxil fumarate (TDF) is reportedly superior or at least comparable to entecavir (ETV) for the prevention of hepatocellular carcinoma (HCC) in patients with chronic hepatitis B; however, it has distinct long-term renal and bone toxicities. This study aimed to develop and validate a machine learning model (designated as Prediction of Liver cancer using Artificial intelligence-driven model for Network–antiviral Selection for hepatitis B [PLAN-S]) to predict an individualized risk of HCC during ETV or TDF therapy. METHODS: This multinational study included 13,970 patients with chronic hepatitis B. The derivation (n 5 6,790), Korean validation (n 5 4,543), and Hong Kong–Taiwan validation cohorts (n 5 2,637) were established. Patients were classified as the TDF-superior group when a PLAN-S-predicted HCC risk under ETV treatment is greater than under TDF treatment, and the others were defined as the TDF-nonsuperior group. RESULTS: The PLAN-S model was derived using 8 variables and generated a c-index between 0.67 and 0.78 for each cohort. The TDF-superior group included a higher proportion of male patients and patients with cirrhosis than the TDF-nonsuperior group. In the derivation, Korean validation, and Hong Kong–Taiwan validation cohorts, 65.3%, 63.5%, and 76.4% of patients were classified as the TDF-superior group, respectively. In the TDF-superior group of each cohort, TDF was associated with a significantly lower risk of HCC than ETV (hazard ratio 5 0.60–0.73, all P < 0.05). In the TDF-nonsuperior group, however, there was no significant difference between the 2 drugs (hazard ratio 5 1.16–1.29, all P > 0.1). DISCUSSION: Considering the individual HCC risk predicted by PLAN-S and the potential TDF-related toxicities, TDF and ETV treatment may be recommended for the TDF-superior and TDF-nonsuperior groups, respectively.

Original languageEnglish
Pages (from-to)1963-1972
Number of pages10
JournalThe American journal of gastroenterology
Volume118
Issue number11
DOIs
StatePublished - 1 Nov 2023

Bibliographical note

Publisher Copyright:
© 2023 by The American College of Gastroenterology.

Keywords

  • antiviral selection
  • deep neural networking
  • liver cancer
  • random survival forests

Fingerprint

Dive into the research topics of 'Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study'. Together they form a unique fingerprint.

Cite this