A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular
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A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma Ruben Amoros1 · Ruth King1 · Hidenori Toyoda2 · Takashi Kumada2 · Philip J. Johnson3 · Thomas G. Bird4,5 Received: 13 January 2019 / Accepted: 21 May 2019 © The Author(s) 2019
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.
Grant acknowledgements: RA is supported by a Chief Scientist’s Office Catalyst Award (CGA/17/19) and a Scottish Liver Transplant Unit Endowment Award. TGB is supported by the Wellcome Trust (107492/Z).
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Ruben Amoros [email protected]
1
School of Mathematics, University of Edinburgh, Edinburgh EH9 3FD, UK
2
Department of Gastroenterology, Ogaki Municipal Hospital, Ogaki, Japan
3
Institute of Translational Medicine, University of Liverpool, Liverpool, UK
4
Cancer Research UK Beatson Institute, Switchback Road, Glasgow G61 1BD, UK
5
MRC Centre for Inflammation Research, The Queens Medical Research Institute, University of Edinburgh, Edinburgh EH16 4TJ, UK
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R. Amoros et al.
Keywords Hidden Markov chains · Hepatocellular carcinoma · Disease detection · Change-point models
1 Introduction Primary liver cancer, of which hepatocellular carcinoma (HCC) is the most common form, is the fourth highest cause of cancer deaths worldwide, accounting for 840,000 cases and 780,000 deaths annually with an age adjusted incidence of 9.5 case pe
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