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A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
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A Bayesian screening approach for hepatocellular carcinoma using multiple longitudinal biomarkers
Nabihah Tayob1, Francesco Stingo2, Kim-Anh Do1
1Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas, U.S.A.
Biometrics
|May 9, 2017
Summary
Early detection of hepatocellular carcinoma (HCC) is crucial. This study introduces a new screening algorithm using alpha-fetoprotein (AFP) and des-gamma carboxyprothrombin (DCP) biomarkers to improve early HCC detection and patient survival.
Area of Science:
- Hepatocellular Carcinoma (HCC) Research
- Biomarker Discovery
- Statistical Modeling in Oncology
Background:
- Advanced hepatocellular carcinoma (HCC) presents limited treatment options and poor patient survival rates.
- Current screening methods like ultrasound lack sensitivity for early HCC detection, and serum alpha-fetoprotein (AFP) has limited diagnostic accuracy.
- There is a critical need for more sensitive and reliable biomarkers for early HCC detection.
Purpose of the Study:
- To develop and evaluate a novel screening algorithm for early hepatocellular carcinoma (HCC) detection.
- To integrate serum alpha-fetoprotein (AFP) and des-gamma carboxyprothrombin (DCP) into a joint hierarchical mixture model for improved screening accuracy.
- To assess the performance of the new algorithm using data from the Hepatitis C Antiviral Long-term Treatment against Cirrhosis (HALT-C) Trial.
Main Methods:
- Utilized a joint hierarchical mixture model with random changepoints to analyze the trajectories of AFP and DCP.
- Employed a Markov Random Field distribution to jointly model changepoint indicators for improved detection of borderline changepoints.
- Applied Markov chain Monte Carlo methods for posterior distribution calculations to inform risk assessment and screening decisions.
Main Results:
- The developed screening algorithm demonstrated potential for improved HCC detection compared to existing methods.
- The joint modeling approach effectively captured distinct biomarker trajectories and changepoint times.
- Cross-validation in the HALT-C Trial and simulation studies supported the algorithm's performance under various scenarios.
Conclusions:
- The novel screening algorithm incorporating both AFP and DCP shows promise for enhancing early HCC detection.
- This approach offers a more sensitive and robust method for identifying high-risk individuals for HCC.
- Further validation and implementation of this biomarker-based screening strategy could significantly improve patient outcomes in HCC.
