Determining the progression stages of liver fibrosis in patients with chronic hepatitis B

authors:

avatar Tanhapour Tanhapour , avatar leila keikha ORCID , avatar maghooli maghooli , avatar Sharareh R. Niakan Kalhori , *

Koomesh: Vol.24, issue 5; 639-647
published online: December 01, 2022
article type: Research Article
received: October 01, 2021
accepted: April 01, 2022

how to cite: Tanhapour T, keikha L, maghooli M, Kalhori S R N . Determining the progression stages of liver fibrosis in patients with chronic hepatitis B. koomesh. 2022;24(5):e152775. 

Abstract

Introduction: Chronic hepatitis B (CHB) leads to liver fibrosis, its failure, and death in the long term. The stage of fibrosis in CHB patients can also be detected based on the biochemical markers. The aim of this study was to predict the state of liver fibrosis in CHB patients and determine the possibility of patients shifting from a given state to another one. Materials and Methods: This study is a cross-sectional study conducted in 2021. Age, blood platelet count, AST, and ALT enzymes were used as the input variable to create predictive models. Predictive models were Decision Tree (DT), Naïve Bayes, Support Vector Machine (SVM), and Neural Network (NN). The probability of a patient shifting from a given stage of fibrosis to another was calculated using the transition matrix. The 10-fold cross-validation was used to ensure the generalization of predictive models. Results: The DT had the best precision, recall, and accuracy (100%) among developed algorithms to predict the stage of fibrosis in CHB patients. The NN was the second most efficient algorithm. Its accuracy and mean square error was 99.35±0.60 and 0.058±0.025, respectively. Besides, SVM had the lowest recall, precision, and accuracy values. Based on the transition matrix results, there is a very low probability that the patients with non-significant fibrosis state shifted to the cirrhosis state. Conclusion: Computational approaches like machine learning algorithms are the non-invasive way to predict the fibrosis state in CHB patients efficiently.

References

  • 1.

    Chen EQ, Zeng F, Zhou LY, Tang H. Early warning and clinical outcome prediction of acute-on-chronic hepatitis B liver failure. World J Gastroenterol 2015; 21: 11964.

  • 2.

    Pattyn J, Hendrickx G, Vorsters A, Van Damme P. Hepatitis B Vaccines. J Infect Dis 2021; 224: S343-S351.

  • 3.

    A'zami M, Nikpey S, Pakzad I, Sayehmiri K. Effects of immunization to hepatitis B vaccine in Iranian health staff: A systematic review and meta-analysis study. Koomesh 2016; 17: 789-795. (Persian).##https://doi.org/10.5812/hepatmon.37781.

  • 4.

    Saffar MJ, Rezai MS. Long-term antibody response and immunologic memory in children immunized with hepatitis B vaccine at birth. Koomesh 2004; 5: 63-72. (Persian).

  • 5.

    Brouwer WP, van der Meer A, Boonstra A, Plompen EP, Pas SD, de Knegt RJ, et al. Prediction of longterm clinical outcome in a diverse chronic hepatitis B population: Role of the PAGEB score. J Viral Hepat 2017; 24: 1023-1031.

  • 6.

    Philips CA, Rajesh S, Nair DC, Ahamed R, Abduljaleel JK, Augustine P. Hepatocellular carcinoma in 2021: an exhaustive update. Cureus 2021; 13: e19274.##https://doi.org/10.7759/cureus.19274.

  • 7.

    Keltch B, Lin Y, Bayrak C. Comparison of AI techniques for prediction of liver fibrosis in hepatitis patients. J Med Syst 2014; 38: 1-8.

  • 8.

    Sumida Y, Nakajima A, Itoh Y. Limitations of liver biopsy and non-invasive diagnostic tests for the diagnosis of nonalcoholic fatty liver disease/nonalcoholic steatohepatitis. World J Gastroenterol 2014; 20: 475.

  • 9.

    Neuberger J, Cain O. The need for alternatives to liver biopsies: non-invasive analytics and diagnostics. Hepat Med 2021; 13: 59.

  • 10.

    Wang D, Wang Q, Shan F, Liu B, Lu C. Identification of the risk for liver fibrosis on CHB patients using an artificial neural network based on routine and serum markers. BMC Infect Dis 2010; 10: 1-8.

  • 11.

    Wai CT, Cheng CL, Wee A, Dan YY, Chan E, Chua W, et al. Noninvasive models for predicting histology in patients with chronic hepatitis B. Liver Int 2006; 26: 666-672.##https://doi.org/10.1111/j.1478-3231.2006.01287.x.

  • 12.

    Mohamadnejad M, Montazeri G, Fazlollahi A, Zamani F, Nasiri J, Nobakht H, et al. Noninvasive markers of liver fibrosis and inflammation in chronic hepatitis B-virus related liver disease. Am J Gastroenterol 2006; p. 2537-2545.

  • 13.

    Fung J, Lai CL, Fong DY, Yuen JCH, Wong DK, Yuen MF. Correlation of liver biochemistry with liver stiffness in chronic hepatitis B and development of a predictive model for liver fibrosis. Liver Int 2008; 28: 1408-1416.##https://doi.org/10.1111/j.1478-3231.2008.01784.x.

  • 14.

    Liaw YF, Kao JH, Piratvisuth T, Chan HL, Chien RN, Liu CJ, et al. Asian-Pacific consensus statement on the management of chronic hepatitis B: a 2012 update. Hepatol Int 2012; 6: 531-561.

  • 15.

    European Association For The Study Of The Liver. EASL clinical practice guidelines: management of chronic hepatitis B virus infection. J Hepatol 2012; 57: 167-185.

  • 16.

    Sung J, Tsoi K, Wong V, Li K, Chan H. Metaanalysis: treatment of hepatitis B infection reduces risk of hepatocellular carcinoma. Aliment Pharmacol Ther 2008; 28: 1067-1077.

  • 17.

    Marcellin P, Gane E, Buti M, Afdhal N, Sievert W, Jacobson IM, et al. Regression of cirrhosis during treatment with tenofovir disoproxil fumarate for chronic hepatitis B: a 5-year open-label follow-up study. Lancet 2013; 381: 468-475.

  • 18.

    Chang TT, Liaw YF, Wu SS, Schiff E, Han KH, Lai CL, et al. Longterm entecavir therapy results in the reversal of fibrosis/cirrhosis and continued histological improvement in patients with chronic hepatitis B. J Hepatol 2010; 52: 886-893.

  • 19.

    Tian J, Smith G, Guo H, Liu B, Pan Z, Wang Z, et al. Modular machine learning for Alzheimer's disease classification from retinal vasculature. Sci Rep 2021; 11: 1-11.

  • 20.

    Elhoseny M, Mohammed MA, Mostafa SA, Abdulkareem KH, Maashi MS, Garcia-Zapirain B, et al. A new multi-agent feature wrapper machine learning approach for heart disease diagnosis. Comput Mater Contin 2021; 67: 51-71.##https://doi.org/10.32604/cmc.2021.012632.

  • 21.

    Battineni G, Sagaro GG, Chinatalapudi N, Amenta F. Applications of machine learning predictive models in the chronic disease diagnosis. J Pers Med 2020; 10: 21.

  • 22.

    Madadizadeh F, Montazeri M, Bahrampour A. Predicting of liver disease using hidden markov model. Razi J Med Sci 2016; 23: 66-74. (Persian).

  • 23.

    Attia IM. New method utilizing continuous time markov Chains to analyze evolution in the nine states model of non-alcoholic fatty liver disease. IEEE 2021; 1-9.

  • 24.

    Bzdok D, Altman N, Krzywinski M. Statistics versus machine learning. Nat Methods 2018; 15: 233-234.

  • 25.

    Kaggle. 2019. https://www.kaggle.com/.

  • 26.

    Bedossa P, Poynard T. An algorithm for the grading of activity in chronic hepatitis C. J Hepatol 1996; 24: 289-293.

  • 27.

    Park BK, Park YN, Ahn SH, Lee KS, Chon CY, Moon YM, et al. Longterm outcome of chronic hepatitis B based on histological grade and stage. J Gastroenterol Hepatol 2007; 22: 383-388.

  • 28.

    Cao Y, Hu ZD, Liu XF, Deng AM, Hu CJ. An MLP classifier for prediction of HBV-induced liver cirrhosis using routinely available clinical parameters. Dis Markers 2013; 35: 653-660.

  • 29.

    Cao X, Shang QH, Chi XL, Zhang W, Xiao HM, Sun MM, et al. Serum N-glycan markers for diagnosing liver fibrosis induced by hepatitis B virus. World J Gastroenterol 2020; 26: 1067.

  • 30.

    Ding R, Zhou X, Huang D, Wang Y, Li X, Yan L, et al. Predictive performances of blood parameter ratios for liver inflammation and advanced liver fibrosis in chronic hepatitis B infection. Biomed Res Int 2021; 2021.

  • 31.

    Chen Y, Gong J, Zhou W, Jie Y, Li Z, Chong Y, et al. A novel prediction model for significant liver fibrosis in patients with chronic hepatitis B. Biomed Res Int 2020; 2020.

  • 32.

    Ertel W. Introduction to artificial intelligence. New York: Springer; 2018.##https://doi.org/10.1007/978-3-319-58487-4.

  • 33.

    Su J, Zhang H, editors. A fast decision tree learning algorithm. Proceeding of 21th national conference on artifitial intelligence 2006 Jul. 16-20; Boston, Massachusetts: American Association for Articial Intelligence.

  • 34.

    Maind SB, Wankar P. Research paper on basic of artificial neural network. Int J Rec Innovat Trends Comput Commun 2014; 2: 96-100.

  • 35.

    Guenther N, Schonlau M. Support vector machines. Stata J 2016; 16: 917-937.##https://doi.org/10.1177/1536867X1601600407.

  • 36.

    Campbell C, Ying Y. Learning with support vector machines. In: Brachman R, Technion-Cornell J, Rossi F, Stone P, editors. Synthesis lectures on artificial intelligence and machine learning. 5. California: morgan and claypool; 2011. p. 1-95.##https://doi.org/10.2200/S00324ED1V01Y201102AIM010.

  • 37.

    Ashenden PJ. Transition Matrix: Science Direct; 2008. https://www.sciencedirect.com/topics/computer-science/transition-matrix.##.

  • 38.

    Andrilli S. Transition Matrix: Science Direct; 2016. https://www.sciencedirect.com/topics/mathematics/transition-matrix.

  • 39.

    Shortliffe EH, Shortliffe EH, Cimino JJ, Cimino JJ. Biomedical informatics: computer applications in health care and biomedicine. New York: Springer; 2014.##https://doi.org/10.1007/978-1-4471-4474-8.

  • 40.

    French METAVIR Cooperative Study Group, Bedossa P. Intraobserver and interobserver variations in liver biopsy interpretation in patients with chronic hepatitis C. J Hepatol 1994; 20: 15-20.

  • 41.

    Hashem S, Esmat G, Elakel W, Habashy S, Raouf SA, Elhefnawi M, et al. Comparison of machine learning approaches for prediction of advanced liver fibrosis in chronic hepatitis C patients. IEEE/ACM Trans Comput Biol Bioinform 2017; 15: 861-868.

  • 42.

    Chen Y, Luo Y, Huang W, Hu D, Zheng RQ, Cong SZ, et al. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B. Comput Biol Med 2017; 89: 18-23.

  • 43.

    Khan DA, Fatima-Tuz-Zuhra KF, Mubarak A. Evaluation of diagnostic accuracy of APRI for prediction of fibrosis in hepatitis C patients. J Ayub Med Coll Abbottabad 2008; 20: 122-126.

  • 44.

    Parikh P, Ryan JD, Tsochatzis EA. Fibrosis assessment in patients with chronic hepatitis B virus (HBV) infection. Ann Transl Med 2017; 5.

  • 45.

    Lee J, Kim M, Kang S, Kim J, Uh Y, Yoon K, et al. The gamma-glutamyl transferase to platelet ratio and the FIB-4 score are noninvasive markers to determine the severity of liver fibrosis in chronic hepatitis B infection. Br J Biomed Sci 2018; 75: 128-132.

  • 46.

    Ayeldeen H, Shaker O, Ayeldeen G, Anwar KM, editors. Prediction of liver fibrosis stages by machine learning model: A decision tree approach. Proceeding of 2015 Third World Conference on Complex Systems (WCCS); 2015 May 23-25; Marrakech, Morocco. New York: IEEE.##https://doi.org/10.1109/ICoCS.2015.7483212.

  • 47.

    Podgorelec V, Kokol P, Stiglic B, Rozman I. Decision trees: an overview and their use in medicine. J Med Syst 2002; 26: 445-463.

  • 48.

    Montazeri M, Montazeri M. Machine learning models for predicting the diagnosis of liver disease. Koomesh 2014; 16: 53-59. (Persian).

  • 49.

    Hou Y, Zhang Q, Gao F, Mao D, Li J, Gong Z, et al. Artificial neural network-based models used for predicting 28-and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure. BMC Gastroenterol 2020; 20: 1-12.

  • 50.

    Yarasuri VK, Indukuri GK, Nair AK, editors. Prediction of hepatitis disease using machine learning technique. Proceeding of 2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC); 2019 Dec. 12 - 14; Coimbatore, India. New York: IEEE.##https://doi.org/10.1109/I-SMAC47947.2019.9032585.

  • 51.

    Islam B, Mahmud F, Hossain A, editors. High performance facial expression recognition system using facial region segmentation, fusion of HOG & LBP features and multiclass SVM. Proceeding of 10th International Conference on Electrical and Computer Engineering (ICECE); 2018 Dec. 20-22; BUET, Dhaka, Bangladesh. New York: IEEE.

  • 52.

    Twumasi C, Asiedu L, Nortey EN. Markov chain modeling of HIV, tuberculosis, and Hepatitis B Transmission in Ghana. Interdiscip Perspect Infect Dis 2019; 2019: 9362492.