Exploration of deep learning-based model's lung cancer screening capabilities.

Principal Investigator

Name
Seungwook Yang

Degrees
Ph.D

Institution
Samsung Electronics

Position Title
Research Scientist

Email
sw1315.yang@samsung.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-579

Initial CDAS Request Approval
Oct 3, 2019

Title
Exploration of deep learning-based model's lung cancer screening capabilities.

Summary
Although chest radiography has proven inferiority in terms of screening accuracy compared to computed tomography, augmenting human readers with deep learning-based computer-aided detection (CAD) model for pulmonary nodules may provide added benefits in screening. This project will first utilize radiographs from the NLST study as a standardized external validation set and compare the detection accuracies between a deep learning-based model and human readers, then the investigators will develop a new lung cancer prediction model involving the detection results.

Aims

- Exploration / validation of deep learning-based nodule detection system and comparison with human readers.
- Development of a novel lung cancer prediction model incorporating deep learning-based nodule detection system's results.

Collaborators

Clinical Research Group, Health & Medical Equipment Business, Samsung Electronics