Quantitative Issues in Cancer Research Working Seminar

Nabihah Tayob, Ph.D.
Assistant Professor of Data Science, Dana Farber Cancer Institute, and Assistant Professor of Medicine, Harvard Medical School

“Improved early detection of hepatocellular carcinoma with longitudinal screening algorithms”

ABSTRACT: The early detection of hepatocellular carcinoma (HCC) is critical to improving outcomes since advanced HCC has limited treatment options. Blood-based biomarkers are a promising direction since they are more easily standardized and less resource intensive than standard of care imaging. Combining 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 have proposed two longitudinal biomarker algorithms. The first is a multivariate fully Bayesian algorithm (mFB) that models the joint biomarker trajectory and uses the posterior risk of HCC estimate to make screening decisions. The second is 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 mFB and 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.