Development and validation of risk prediction models for hepatocellular carcinoma in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
Principal Investigator
Name
Zhangyan Lyu
Degrees
Ph.D.
Institution
Tianjin Medical University Cancer Institute and Hospital
Position Title
Research Associate
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-822
Initial CDAS Request Approval
Sep 14, 2021
Title
Development and validation of risk prediction models for hepatocellular carcinoma in the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial
Summary
Hepatocellular carcinoma (HCC) is the fourth leading cause of cancer-related death worldwide and one of the fastest increasing causes of cancer-related mortality in the United States. Prognosis of HCC is poor in all regions of the world , thus, screening and early diagnosis of HCC is vital.
Ultrasound and serum alpha-fetoprotein (AFP) are the most commonly used methods for HCC surveillance, but have been reported to be ineffective in the early diagnosis of early-stage HCC. For population-based screening, an accurate HCC risk prediction model for identifying high-risk subpopulation is urgently needed.
Available prediction models for HCC are limited to individuals at elevated risk who are with hepatitis B virus (HBV) or hepatitis C virus (HCV), or cirrhosis. The majority of these models also need additional information from two aspects: 1) routine clinical workup markers, such as tumor biomarkers, immune markers, and hormones; 2) single nucleotide polymorphisms (SNPs) identified by genome-wide association study (GWAS).
Taken together, a comprehensive personalized risk prediction model for HCC based on blood biomarkers, genetic susceptibility, routinely available predictors such as medical history, metabolic risk factors and lifestyle factors is urgently needed.
Ultrasound and serum alpha-fetoprotein (AFP) are the most commonly used methods for HCC surveillance, but have been reported to be ineffective in the early diagnosis of early-stage HCC. For population-based screening, an accurate HCC risk prediction model for identifying high-risk subpopulation is urgently needed.
Available prediction models for HCC are limited to individuals at elevated risk who are with hepatitis B virus (HBV) or hepatitis C virus (HCV), or cirrhosis. The majority of these models also need additional information from two aspects: 1) routine clinical workup markers, such as tumor biomarkers, immune markers, and hormones; 2) single nucleotide polymorphisms (SNPs) identified by genome-wide association study (GWAS).
Taken together, a comprehensive personalized risk prediction model for HCC based on blood biomarkers, genetic susceptibility, routinely available predictors such as medical history, metabolic risk factors and lifestyle factors is urgently needed.
Aims
AIM 1: Our primary aim is to develop and validate a set of risk prediction models that including blood biomarkers, genetic susceptibility, routinely available predictors for hepatocellular carcinoma.
AIM 2: Our secondary aim is to explore the potential interaction and/or mediation effects between genetic susceptibility, blood biomarkers and traditional risk factors for HCC, which could enhance the understanding of the mechanisms of hepatocarcinogenesis.
Collaborators
None.