Projects per year
Abstract / Description of output
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-off point.
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-off point.
Original language | English |
---|---|
Pages (from-to) | 67-86 |
Number of pages | 20 |
Journal | METRON |
Volume | 77 |
Issue number | 2 |
Early online date | 30 May 2019 |
DOIs | |
Publication status | Published - 31 Aug 2019 |
Fingerprint
Dive into the research topics of 'A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma'. Together they form a unique fingerprint.Projects
- 2 Finished
-
Validating and improving serum biomarkers for liver cancer surveillance
King, R., Bird, T. & Calhau Fernandes Inacio De Carvalho, V.
1/09/18 → 28/02/19
Project: Research
Profiles
-
Tom Bird
- Deanery of Clinical Sciences - Personal Chair of Hepatobiliary Cancer
- Centre for Inflammation Research
Person: Academic: Research Active