Colorectal cancer predicting model established on a large prospective screening project.
Principal Investigator
Name
Ziqi Zhang
Degrees
Master
Institution
Sun Yat-Sen University Cancer Center
Position Title
resident doctor
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCO-1087
Initial CDAS Request Approval
Nov 1, 2022
Title
Colorectal cancer predicting model established on a large prospective screening project.
Summary
Colorectal cancer has now become a major cancer, accounting for about 10% of cancer related deaths in Western countries. However, most patients have advanced to the middle or late stage of the disease at the time of diagnosis. Therefore, early detection, diagnosis and treatment of colorectal cancer are very important. It is of great public health significance to explore the risk factors of colorectal cancer, build an efficient and feasible colorectal cancer risk prediction model, conduct risk stratification and individual risk prediction for the population, and accurately carry out colorectal cancer prevention and control work, which will reduce the burden of colorectal cancer.
At present, some scholars have established a variety of risk prediction models for colorectal cancer, which are used to identify risk factors, screen high-risk populations and assess the risk of the disease. The current models still have the shortages for low predictive value, fewer risk factors included, and lack of internal validation. In this study, we will use stepwise cox regression to screen the risk factors of colorectal cancer, and the predictive nomogram of colorectal cancer will be established to predict the incidence rate of colorectal cancer, providing a simple and rapid tool for clinical practitioners to screen high-risk groups of colorectal cancer.
At present, some scholars have established a variety of risk prediction models for colorectal cancer, which are used to identify risk factors, screen high-risk populations and assess the risk of the disease. The current models still have the shortages for low predictive value, fewer risk factors included, and lack of internal validation. In this study, we will use stepwise cox regression to screen the risk factors of colorectal cancer, and the predictive nomogram of colorectal cancer will be established to predict the incidence rate of colorectal cancer, providing a simple and rapid tool for clinical practitioners to screen high-risk groups of colorectal cancer.
Aims
1、To establish a nomogram for predicting colorectal cancer risk using PLCO dataset.
Collaborators
Chuan-Bo Xie，Sun Yat-Sen University Cancer Center