# Biomarker screening algorithms for the improved early detection of hepatocellular carcinoma

> **NIH NIH R01** · DANA-FARBER CANCER INST · 2021 · $24,656

## Abstract

PROJECT SUMMARY/ABSTRACT
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 develope...

## Key facts

- **NIH application ID:** 9899216
- **Project number:** 5R01CA230503-03
- **Recipient organization:** DANA-FARBER CANCER INST
- **Principal Investigator:** Nabihah Tayob
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $24,656
- **Award type:** 5
- **Project period:** 2019-04-01 → 2025-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9899216

## Citation

> US National Institutes of Health, RePORTER application 9899216, Biomarker screening algorithms for the improved early detection of hepatocellular carcinoma (5R01CA230503-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9899216. Licensed CC0.

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