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      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

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          Abstract

          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.

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          Most cited references31

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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            Random Forests

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              Support-vector networks

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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                12 December 2022
                2022
                : 13
                : 1031400
                Affiliations
                [1] 1 Department of Traditional and Western Medical Hepatology, Third Hospital of Hebei Medical University, Hebei Provincial Key Laboratory of liver fibrosis in chronic liver diseases , Shijiazhuang, China
                [2] 2 School of Engineering, Penn State Erie, The Behrend College , Erie, PA, United States
                [3] 3 Shanghai Ashermed Healthcare Co., Ltd. , Shanghai, China
                Author notes

                Edited by: Jinghua Pan, Jinan University, China

                Reviewed by: Yasir Waheed, Shaheed Zulfiqar Ali Bhutto Medical University (SZABMU), Pakistan; Fang Qi, Peking University, China; Bo Zhou, Southeast University, China

                *Correspondence: Yuemin Nan, nanyuemin@ 123456163.com

                †These authors have contributed equally to this work and share first authorship

                This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology

                Article
                10.3389/fimmu.2022.1031400
                9791942
                36578484
                ed1bd3f1-d858-41f6-a970-5028195d3ced
                Copyright © 2022 Nan, Zhao, Zhang, Xiao and Guo

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 August 2022
                : 18 November 2022
                Page count
                Figures: 7, Tables: 3, Equations: 5, References: 31, Pages: 11, Words: 4986
                Categories
                Immunology
                Original Research

                Immunology
                hepatocellular carcinoma,blood biomarkers,machine learning,hepatitis b,cirrhosis,risk model,immunotherapy

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