Machine learning-based predictive and risk analysis using real-world data with blood biomarkers for hepatitis B patients in the malignant progression of hepatocellular carcinoma

被引:5
|
作者
Nan, Yuemin [1 ]
Zhao, Suxian [1 ]
Zhang, Xiaoxiao [1 ]
Xiao, Zhifeng [2 ]
Guo, Ruihan [3 ]
机构
[1] Hebei Med Univ, Hosp 3, Dept Tradit & Western Med Hepatol, Hebei Prov Key Lab Liver Fibrosis Chron Liver Dis, Shijiazhuang, Peoples R China
[2] Behrend Coll, Sch Engn, Penn State Erie, Erie, PA USA
[3] Shanghai Ashermed Healthcare Co Ltd, Shanghai, Peoples R China
来源
FRONTIERS IN IMMUNOLOGY | 2022年 / 13卷
关键词
hepatocellular carcinoma; blood biomarkers; machine learning; hepatitis B; cirrhosis; risk model; immunotherapy;
D O I
10.3389/fimmu.2022.1031400
中图分类号
R392 [医学免疫学]; Q939.91 [免疫学];
学科分类号
100102 ;
摘要
Hepatitis B Virus (HBV) infection may lead to various liver diseases such as cirrhosis, end-stage liver complications, and Hepatocellular carcinoma (HCC). Patients with existing cirrhosis or severe fibrosis have an increased chance of developing HCC. Consequently, lifetime observation is currently advised. This study gathered real-world electronic health record (EHR) data from the China Registry of Hepatitis B (CR-HepB) database. A collection of 396 patients with HBV infection at different stages were obtained, including 1) patients with a sustained virological response (SVR), 2) patients with HBV chronic infection and without further development, 3) patients with cirrhosis, and 4) patients with HCC. Each patient has been monitored periodically, yielding multiple visit records, each is described using forty blood biomarkers. These records can be utilized to train predictive models. Specifically, we develop three machine learning (ML)-based models for three learning tasks, including 1) an SVR risk model for HBV patients via a survival analysis model, 2) a risk model to encode the progression from HBV, cirrhosis and HCC using dimension reduction and clustering techniques, and 3) a classifier to detect HCC using the visit records with high accuracy (over 95%). Our study shows the potential of offering a comprehensive understanding of HBV progression via predictive analysis and identifies the most indicative blood biomarkers, which may serve as biomarkers that can be used for immunotherapy.
引用
收藏
页数:11
相关论文
共 50 条
  • [1] A machine learning model for predicting hepatocellular carcinoma risk in patients with chronic hepatitis B
    Lee, Hye Won
    Kim, Hwiyoung
    Park, Taeyun
    Park, Soo Young
    Chon, Young Eun
    Seo, Yeon Seok
    Lee, Jae Seung
    Park, Jun Yong
    Kim, Do Young
    Ahn, Sang Hoon
    Kim, Beom Kyung
    Kim, Seung Up
    LIVER INTERNATIONAL, 2023, 43 (08) : 1813 - 1821
  • [2] MACHINE LEARNING-BASED CLASSIFICATION OF HBV AND HCV-RELATED HEPATOCELLULAR CARCINOMA USING GENOMIC BIOMARKERS br
    Akbulut, Sami
    Kucukakcali, Zeynep
    Colak, Cemil
    JOURNAL OF ISTANBUL FACULTY OF MEDICINE-ISTANBUL TIP FAKULTESI DERGISI, 2022,
  • [3] Machine learning-based transcriptome analysis of lipid metabolism biomarkers for the survival prediction in hepatocellular carcinoma
    Xiong, Ronghong
    Wang, Hui
    Li, Ying
    Zheng, Jingpeng
    Cheng, Yating
    Liu, Shunfang
    Yang, Guohua
    FRONTIERS IN GENETICS, 2022, 13
  • [4] A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models
    Rodriguez, Patricia J.
    Veenstra, David L.
    Heagerty, Patrick J.
    Goss, Christopher H.
    Ramos, Kathleen J.
    Bansal, Aasthaa
    VALUE IN HEALTH, 2022, 25 (03) : 350 - 358
  • [5] Conventional and machine learning-based risk scores for patients with early-stage hepatocellular carcinoma
    Ho, Chun-Ting
    Tan, Elise Chia-Hui
    Lee, Pei-Chang
    Chu, Chi-Jen
    Huang, Yi-Hsiang
    Huo, Teh-Ia
    Su, Yu-Hui
    Hou, Ming-Chih
    Wu, Jaw-Ching
    Su, Chien-Wei
    CLINICAL AND MOLECULAR HEPATOLOGY, 2024, 30 (03) : 406 - 420
  • [6] Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data
    Byeonggwan Noh
    Young Mok Park
    Yujin Kwon
    Chang In Choi
    Byung Kwan Choi
    Kwang il Seo
    Yo-Han Park
    Kwangho Yang
    Sunju Lee
    Taeyoung Ha
    YunKyong Hyon
    Myunghee Yoon
    BMC Gastroenterology, 22
  • [7] Machine learning-based survival rate prediction of Korean hepatocellular carcinoma patients using multi-center data
    Noh, Byeonggwan
    Park, Young Mok
    Kwon, Yujin
    Choi, Chang In
    Choi, Byung Kwan
    Seo, Kwang Il
    Park, Yo-Han
    Yang, Kwangho
    Lee, Sunju
    Ha, Taeyoung
    Hyon, YunKyong
    Yoon, Myunghee
    BMC GASTROENTEROLOGY, 2022, 22 (01)
  • [8] A machine learning model for predicting blood concentration of quetiapine in patients with schizophrenia and depression based on real-world data
    Hao, Yupei
    Zhang, Jinyuan
    Yang, Lin
    Zhou, Chunhua
    Yu, Ze
    Gao, Fei
    Hao, Xin
    Pang, Xiaolu
    Yu, Jing
    BRITISH JOURNAL OF CLINICAL PHARMACOLOGY, 2023, 89 (09) : 2714 - 2725
  • [9] Development and Validation of a Machine Learning-Based Model Used for Predicting Hepatocellular Carcinoma Risk in Patients with Hepatitis B-Related Cirrhosis: A Retrospective Study
    Hou, Yixin
    Yan, Jianguo
    Shi, Ke
    Liu, Xiaoli
    Gao, Fangyuan
    Wu, Tong
    Meng, Peipei
    Zhang, Min
    Jiang, Yuyong
    Wang, Xianbo
    ONCOTARGETS AND THERAPY, 2024, 17 : 215 - 226
  • [10] Treatment journey of patients with hepatocellular carcinoma using real-world data in British Columbia, Canada
    Seung, Soo Jin
    Saherawala, Hasnain
    Zagorski, Brandon
    Tong, Carman
    Lim, Howard
    Kim, Peter
    Marquez, Vladimir
    Gill, Sharlene
    Liu, David
    Davies, Janine M.
    HEPATIC ONCOLOGY, 2023, 10 (04)