Data-driven imaging biomarker(DIB) development for the lung cancer screening
Principal Investigator
Name
Minsung Kim
Degrees
M.D.
Institution
Lunit Inc.
Position Title
Medical director
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-424
Initial CDAS Request Approval
Jul 5, 2018
Title
Data-driven imaging biomarker(DIB) development for the lung cancer screening
Summary
Lung cancer screening with low-dose computed tomography (LDCT) has been recommended, based primarily on the results of the NLST (National Lung Screening Trial). However, manual evaluation of large volume of screening LDCT images can cause fatigue and there is a potential risk of human error. In this project, we aim to use the NLST dataset to develop and validate our deep learning model which detects lung nodule/mass with cancer probability. We'll use CT scan with demographic data to improve cancer probability prediction. Our final goal is develop robust deep learning model which improves diagnostic performance and clinical workflow.
Aims
- Develop the state-of-the-art DIB model using deep learning for detecting lung nodule/mass with cancer probability
- Validation of pre-trained DIB model for NLST dataset.
- Improve diagnostic performance and clinical workflow
Collaborators
Lunit