Lung cancer prediction model
Principal Investigator
Name
Deyao Kong
Degrees
Master
Institution
Harbin Normal University
Position Title
Student
Email
About this CDAS Project
Study
PLCO
(Learn more about this study)
Project ID
PLCOI-1273
Initial CDAS Request Approval
Jul 19, 2023
Title
Lung cancer prediction model
Summary
Screening reduces lung cancer mortality of high-risk populations. Currently proposed screening eligibility criteria only identify half of those individuals, who later develop lung cancer. This study aimed to develop and validate a sensitive and simple model for predicting 10-year lung cancer risk.
We want to use digitized screening xray images to better analyze the location and size of tumors documented and to make a multimodal data fusion model.
We want to use digitized screening xray images to better analyze the location and size of tumors documented and to make a multimodal data fusion model.
Aims
This study was performed to develop and validate a sensitive lung cancer risk prediction model with standard clinical parameters among former or current smokers from a population-based cohort. The best model was validated in a later examination of the same cohort. In addition, we developed a simple risk chart including the most informative predictors with clinical potential to identify individuals with the highest 10-year lung cancer risk.
Collaborators
deyao kong