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.
Highlights
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 per 100,000 person years [5]
We focus on the dataset provided by the Ogaki Municipal Hospital, Japan, comprising of individual longitudinal data collected at irregular times over the period of several years from patients with cirrhosis being screened for HCC
We evaluate the detection of HCC by our model in terms of sensitivity, specificity and timeliness and compare it with the established use of a static cut-point over the GALAD score
Summary
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 per 100,000 person years [5]. Significant underlying liver disease is typically used as an entry point into HCC surveillance programmes These surveillance programmes usually rely upon imaging based HCC detection, typically using ultrasound, which may be supplemented by serum biomarkers [12,14,20]. The actual diagnosis of HCC, following suspicion of underlying tumour raised by screening, is achieved by diagnostic cross sectional imaging; multiphasic computed tomography and/or dynamic contrast-enhanced magnetic resonance imaging. Each of these radiological tests has their own limitations including expense, radiation exposure, diagnostic accuracy— in the presence of cirrhosis or presence of fat in the liver—and an inability to accurately diagnose small lesions (< 1 cm). The measurement of biomarkers using simple blood tests is less expensive than radiological imaging tests and has the potential of detecting the development of tumours before they may be confirming on imaging [17]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
