Development and validation of a CT-based prognostication model for diffuse lung parenchymal diseases
Principal Investigator
Name
Hyungjin Kim
Degrees
M.D., Ph.D.
Institution
Seoul National University Hospital
Position Title
Clinical associate professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-1061
Initial CDAS Request Approval
May 30, 2023
Title
Development and validation of a CT-based prognostication model for diffuse lung parenchymal diseases
Summary
This project aims to develop and validate CT-based prognostication models for diffuse lung parenchymal diseases. Recent advancement of deep learning algorithms enabled end-to-end training using CT scans for the patient outcomes. In addition, low-dose CT scans, which contain rich prognostic information for the various lung diseases, are widely available in daily clinical practice. Therefore, it is reasonable to apply a deep learning model for quantification and prognostication of lung diseases on low-dose CT. Our target diseases include interstitial lung abnormality, interstitial lung disease, and chronic obstructive pulmonary disease. The study outcomes are overall survival, respiratory mortality, and cancer mortality.
Aims
1. This project aims to develop and validate CT-based prognostication models for diffuse lung parenchymal diseases.
2. Target diseases include interstitial lung abnormality, interstitial lung disease, and chronic obstructive pulmonary disease.
3. The study outcomes are overall survival, respiratory mortality, and cancer mortality.
Collaborators
Jong Hyuk Lee, Seoul National University Hospital
Seungho Lee, Seoul National University Hospital