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A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma

Author

Listed:
  • Ruben Amoros

    (University of Edinburgh)

  • Ruth King

    (University of Edinburgh)

  • Hidenori Toyoda

    (Ogaki Municipal Hospital)

  • Takashi Kumada

    (Ogaki Municipal Hospital)

  • Philip J. Johnson

    (University of Liverpool)

  • Thomas G. Bird

    (Cancer Research UK Beatson Institute
    University of Edinburgh)

Abstract

Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer deaths worldwide, and its early detection is a critical determinant of whether curative treatment is achievable. Early stage HCC is typically asymptomatic. Thus, screening programmes are used for cancer detection in patients at risk of tumour development. Radiological screening methods are limited by imperfect data, cost and associated risks, and additionally are unable to detect lesions until they have grown to a certain size. Therefore, some screening programmes use additional blood/serum biomarkers to help identify individuals in whom to target diagnostic cancer investigations. The GALAD score, combining the levels of several blood biomarkers, age and sex, has been developed to identify patients with early HCC. Here we propose a Bayesian hierarchical model for an individual’s longitudinal GALAD scores whilst in HCC surveillance to identify potentially significant changes in the trend of the GALAD score, indicating the development of HCC, aiming to improve early detection compared to standard methods. An absorbent two-state continuous-time hidden Markov model is developed for the individual level longitudinal data where the states correspond to the presence/absence of HCC. The model is additionally informed by the information on the diagnosis by standard clinical practice, taking into account that HCC can be present before the actual diagnosis so that there may be false negatives within the diagnosis data. We fit the model to a Japanese cohort of patients undergoing HCC surveillance and show that the detection capability of this proposal is greater than using a fixed cut-point.

Suggested Citation

  • Ruben Amoros & Ruth King & Hidenori Toyoda & Takashi Kumada & Philip J. Johnson & Thomas G. Bird, 2019. "A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 67-86, August.
    • Handle: RePEc:spr:metron:v:77:y:2019:i:2:d:10.1007_s40300-019-00151-8
    DOI: 10.1007/s40300-019-00151-8
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    References listed on IDEAS

    as
    1. Nabihah Tayob & Francesco Stingo & Kim†Anh Do & Anna S. F. Lok & Ziding Feng, 2018. "A Bayesian screening approach for hepatocellular carcinoma using multiple longitudinal biomarkers," Biometrics, The International Biometric Society, vol. 74(1), pages 249-259, March.
    2. Thomas G Bird & Polyxeni Dimitropoulou & Rebecca M Turner & Sara J Jenks & Pearce Cusack & Shiying Hey & Andrew Blunsum & Sarah Kelly & Catharine Sturgeon & Peter C Hayes & Sheila M Bird, 2016. "Alpha-Fetoprotein Detection of Hepatocellular Carcinoma Leads to a Standardized Analysis of Dynamic AFP to Improve Screening Based Detection," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-22, June.
    3. Skates S. J & Pauler D. K & Jacobs I. J, 2001. "Screening Based on the Risk of Cancer Calculation From Bayesian Hierarchical Changepoint and Mixture Models of Longitudinal Markers," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 429-439, June.
    4. Carles Serrat & Montserrat Ru� & Carmen Armero & Xavier Piulachs & H�ctor Perpi��n & Anabel Forte & �lvaro P�ez & Guadalupe G�mez, 2015. "Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1223-1239, June.
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    Cited by:

    1. Alan Riva-Palacio & Ramsés H. Mena & Stephen G. Walker, 2023. "On the estimation of partially observed continuous-time Markov chains," Computational Statistics, Springer, vol. 38(3), pages 1357-1389, September.
    2. Jan Bulla & Roland Langrock & Antonello Maruotti, 2019. "Guest editor’s introduction to the special issue on “Hidden Markov Models: Theory and Applications”," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 63-66, August.
    3. Chris Sherlock, 2021. "Direct statistical inference for finite Markov jump processes via the matrix exponential," Computational Statistics, Springer, vol. 36(4), pages 2863-2887, December.

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