Pulmonary CT imaging auxiliary diagnosis software in Thoracic CT in Patients being Evaluated for Possible Lung Cancer (AI)
Principal Investigator
Name
Guoqing Lian
Degrees
Ph.D.
Institution
Beijing Yizhun Medical AI Technology Co. ,Ltd.
Position Title
RA Head
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-827
Initial CDAS Request Approval
Aug 6, 2021
Title
Pulmonary CT imaging auxiliary diagnosis software in Thoracic CT in Patients being Evaluated for Possible Lung Cancer (AI)
Summary
Lung cancer screening by LDCT is a Medicare-covered procedure to eligible patients. However, it is a difficult task to characterize nodules detected from the screening exams.
The National Lung Screening Trial (NLST) contains LDCT images for patients with lung cancers and with benign nodules. We plan to utilize deep learning, radiomics, and clinical data to create an accurate prediction model to estimate lung nodule malignancy from LDCT images.
The National Lung Screening Trial (NLST) contains LDCT images for patients with lung cancers and with benign nodules. We plan to utilize deep learning, radiomics, and clinical data to create an accurate prediction model to estimate lung nodule malignancy from LDCT images.
Aims
We aim to divide the data into training, validation, and testing set. Each set will contain malignant and benign nodules. We plan to train and validate a deep learning model that combines image features, radiomic features, and clinical features to accurately characterize nodule malignancy. We are aiming for sensitivity and specificity over 90%.
Collaborators
None.