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

  • Hur, Moon Haeng; 
  • Park, Min Kyung; 
  • Yip, Terry Cheuk-Fung; 
  • Chen, Chien-Hung; 
  • Lee, Hyung-Chul; 
  • ... Park, Soo Young; 
  • 외 25명
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초록

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 = 6,790), Korean validation (n = 4,543), and Hong Kong-Taiwan validation cohorts (n = 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 = 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 = 1.16-1.29, all P > 0.1). [GRAPHICS] .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.

키워드

liver cancer; antiviral selection; deep neural networking; random survival forests; TENOFOVIR DISOPROXIL FUMARATE; HEPATOCELLULAR-CARCINOMA RISK; ENTECAVIR; VALIDATION; REGRESSION; INFECTION; CIRRHOSIS; GENOTYPES
제목
Personalized Antiviral Drug Selection in Patients With Chronic Hepatitis B Using a Machine Learning Model: A Multinational Study
저자
Hur, Moon Haeng; Park, Min Kyung; Yip, Terry Cheuk-Fung; Chen, Chien-Hung; Lee, Hyung-Chul; Choi, Won-Mook; Kim, Seung Up; Lim, Young-Suk; Park, Soo Young; Wong, Grace Lai-Hung; Sinn, Dong Hyun; Jin, Young-Joo; Kim, Sung Eun; Peng, Cheng-Yuan; Shin, Hyun Phil; Chen, Chi-Yi; Kim, Hwi Young; Lee, Han Ah; Seo, Yeon Seok; Jun, Dae Won; Yoon, Eileen L.; Sohn, Joo Hyun; Ahn, Sang Bong; Shim, Jae-Jun; Jeong, Soung Won; Cho, Yong Kyun; Kim, Hyoung Su; Jang, Myoung-jin; Kim, Yoon Jun; Yoon, Jung-Hwan; Lee, Jeong-Hoon
DOI
10.14309/ajg.0000000000002234
발행일
2023-11
유형
Article
저널명
American Journal of Gastroenterology
권
118
호
11
페이지
1963 ~ 1972