A Machine Learning Model to Predict De Novo Hepatocellular Carcinoma Beyond Year 5 of Antiviral Therapy in Patients With Chronic Hepatitis B
- Yeonjung Ha 1, Seungseok Lee 2, Jihye Lim 3, Kwanjoo Lee 1, Young Eun Chon 1, Joo Ho Lee 1, Kwan Sik Lee 1, Kang Mo Kim 4, Ju Hyun Shim 4, Danbi Lee 4, Dong Keon Yon 5, Jinseok Lee 2, Han Chu Lee 4
- Yeonjung Ha 1, Seungseok Lee 2, Jihye Lim 3
- 1Department of Gastroenterology, CHA Bundang Medical Center, CHA University, Seongnam-si, Gyeonggi-do, South Korea.
- 2Department of Biomedical Engineering, College of Electronics and Informatics, Kyung Hee University, Yongin-si, Gyeonggi-do, South Korea.
- 3Division of Gastroenterology and Hepatology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, South Korea.
- 4Asan Liver Center, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
- 5Center for Digital Health, Medical Research Institute, Kyung Hee University Medical Center, Kyung Hee University, Seoul, South Korea.
- 0Department of Gastroenterology, CHA Bundang Medical Center, CHA University, Seongnam-si, Gyeonggi-do, South Korea.
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed a machine learning model to predict hepatocellular carcinoma (HCC) risk in chronic hepatitis B (CHB) patients on antiviral therapy. The model accurately identifies high-risk individuals for personalized surveillance.
Area Of Science
- Hepatology
- Machine Learning in Medicine
- Oncology
Background
- Chronic hepatitis B (CHB) patients on long-term antiviral therapy require risk stratification for hepatocellular carcinoma (HCC).
- Predicting HCC development beyond 5 years of entecavir (ETV) or tenofovir (TFV) therapy is crucial for patient management.
Purpose Of The Study
- To develop and validate a machine learning (ML) model for predicting HCC risk in CHB patients after 5 years of ETV/TFV therapy.
- To create a tool for individualized HCC surveillance strategies.
Main Methods
- Utilized data from CHB patients treated with ETV/TFV for over 5 years, excluding those diagnosed with HCC in the first 5 years.
- Developed models using 36 variables including baseline characteristics and laboratory values at baseline, 5 years, and changes over time.
- Applied and validated five ML algorithms, including logistic regression and random forest, through internal and external validation.
Main Results
- An ensemble ML model combining logistic regression and random forest demonstrated superior performance.
- The ensemble model achieved an AUC of 0.811 (balanced accuracy 0.754) in the training set and 0.862 (balanced accuracy 0.771) in the external validation cohort.
- A web-based calculator was developed based on the validated model.
Conclusions
- The developed ML model accurately predicts HCC risk in CHB patients beyond 5 years of antiviral therapy.
- The model facilitates individualized HCC surveillance by stratifying patient risk.
- This tool can aid clinicians in optimizing monitoring strategies for CHB patients at risk of HCC.
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