The incidence of hepatocellular carcinoma (HCC) in the United States continues to rise with majority of patients diagnosed with advanced stage disease, limited treatment options and poor prognosis. HCC is projected to become the 3rd leading cause of cancer-related deaths by 2030. The earlier detection of HCC is necessary towards reducing the high HCC mortality rates since those with early stage disease have multiple, potentially curative, treatment options available. Current guidelines recommend those with cirrhosis undergo six-monthly liver ultrasound with or without serum alpha-fetoprotein (AFP), however ultrasound is not sensitive for early lesions and the reported performance of AFP varies widely. We will develop and evaluate two novel biomarker screening algorithms that aim to improve the early detection of HCC. We have previously proposed a parametric empirical Bayes (PEB) screening algorithm for AFP that increased earlier HCC detection through personalized thresholds that incorporate prior AFP results. Blood- based biomarkers are a most promising, cost-effective tool for widespread HCC surveillance and there are multiple novel HCC biomarkers under development.
In Aim 1 we will generalize the PEB algorithm to enable joint screening with multiple biomarkers (e.g. AFP, DCP, AFP-L3, promising novel biomarkers). We propose to develop a robust decision rule for multiple HCC biomarkers that uses prior screening history to increase earlier HCC detection in the Hepatitis C Antiviral Long-term Treatment against Cirrhosis Trial. A second screening strategy is based on the observation that patients under active surveillance have continuously updated clinical and laboratory data collected but not used systematically to improve screening.
In Aim 2 we will develop and evaluate a fully Bayesian screening algorithm that combines longitudinal AFP, other laboratory markers and clinical covariates to increase the likelihood of earlier detection of HCC. Our goal is to improve AFP screening performance through the robust development of joint models for AFP, other laboratory tests and clinical data. Once validated, this algorithm could be implemented based on current clinical practice. We will develop and refine the algorithm in two large retrospective cohorts: a Department of Veterans Affairs national cirrhosis cohort, a Kaiser Permanente Northern California cirrhosis cohort.
In Aim 3 we will evaluate both algorithms in the Hepatocellular carcinoma Early Detection Strategy (HEDS) study and the Trans Texas HCC Consortium (THCCC); the largest prospective cirrhosis cohorts assembled in the United States to date. We will leverage our access to some of the most authoritative cirrhosis studies to build and evaluate HCC screening algorithms, with a target of increasing the sensitivity of HCC screening by 33% while maintaining a low false positive rate to ensure the feasibility of HCC surveillance. Additionally, the statistical methods developed will have broad application in other cancer screening settings (e.g. lung, ovarian, prostate, and pancreatic cancer). 1
The proposed research aims to develop and evaluate hepatocellular carcinoma (HCC) screening algorithms that incorporate changes in biomarkers over time to improve the likelihood of early detection and hence is highly relevant to the mission of the NCI. We hypothesize that our proposed HCC screening algorithms, which will utilize state-of-the-art statistical methods to integrate all the available information, will result in reduced harms (fewer false positives) and increased likelihood of early detection of HCC at a potentially curative stage. In our proposal we will utilize data from a range of study designs, with patients located throughout the US and being surveilled in multiple settings including academic hospitals, community hospitals and Department of Veteran's Affairs hospitals, ensuring robust development, evaluation and validation of our proposed algorithms. 1
