Deep learning models to predict lung cancer malignancy

Principal Investigator

Name
Yufeng Deng

Degrees
Ph.D

Institution
Infervision US Inc.

Position Title
President

Email
dyufeng@infervision.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-604

Initial CDAS Request Approval
Nov 25, 2019

Title
Deep learning models to predict lung cancer malignancy

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.

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

NA