Amoros Ruben, King Ruth, Toyoda Hidenori, Kumada Takashi, Johnson Philip J, Bird Thomas G
1School of Mathematics, University of Edinburgh, Edinburgh, EH9 3FD UK.
2Department of Gastroenterology, Ogaki Municipal Hospital, Ogaki, Japan.
Metron. 2019;77(2):67-86. doi: 10.1007/s40300-019-00151-8. Epub 2019 May 30.
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.
肝细胞癌（HCC）是全球第四大常见癌症死因，其早期检测是能否实现治愈性治疗的关键决定因素。早期HCC通常无症状。因此，筛查计划用于在有肿瘤发生风险的患者中检测癌症。放射学筛查方法受到数据不完善、成本和相关风险的限制，此外，在病变生长到一定大小之前无法检测到。因此，一些筛查计划使用额外的血液/血清生物标志物来帮助确定进行诊断性癌症检查的目标个体。结合几种血液生物标志物水平、年龄和性别的GALAD评分已被开发用于识别早期HCC患者。在此，我们提出一种贝叶斯分层模型，用于个体在HCC监测期间的纵向GALAD评分，以识别GALAD评分趋势中潜在的显著变化，表明HCC的发生，旨在比标准方法提高早期检测率。针对个体水平的纵向数据开发了一种吸收性两状态连续时间隐马尔可夫模型，其中状态对应于HCC的存在/不存在。该模型还通过标准临床实践中的诊断信息得到补充，考虑到HCC可能在实际诊断之前就已存在，因此诊断数据中可能存在假阴性。我们将该模型应用于一组接受HCC监测的日本患者，结果表明该方法的检测能力优于使用固定切点的方法。
