A multivariate parametric empirical Bayes screening approach for early detection of hepatocellular carcinoma using multiple longitudinal biomarkers

Abstract

The early detection of hepatocellular carcinoma (HCC) is critical to improving outcomes since advanced HCC has limited treatment options. Current guidelines recommend HCC ultrasound surveillance every 6 months in high-risk patients however the sensitivity for detecting early stage HCC in clinical practice is poor. Blood-based biomarkers are a promising direction since they are more easily standardized and less resource intensive. Combining of multiple biomarkers is more likely to achieve the sensitivity required for a clinically useful screening algorithm and the longitudinal trajectory of biomarkers contains valuable information that should be utilized. We propose a multivariate parametric empirical Bayes (mPEB) screening approach that defines personalized thresholds for each patient at each screening visit to identify significant deviations that trigger additional testing with more sensitive imaging. The Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) trial provides a valuable source of data to study HCC screening algorithms. We study the performance of the mPEB algorithm applied to serum $\alpha$ -fetoprotein, a widely used HCC surveillance biomarker, and des- $\gamma$ carboxy prothrombin, an HCC risk biomarker that is FDA approved but not used in practice in the United States. Using cross-validation, we found that the mPEB algorithm demonstrated moderate but improved sensitivity compared to alternative screening approaches. Future research will validate the clinical utility of the approach in larger cohort studies with additional biomarkers.

Keywords: biomarkers; early detection; empirical Bayes; longitudinal screening history; numeric optimization.