A Deep Learning Model for Improved Cancer Risk Prediction in Sequential Lung Screening X-Rays
Principal Investigator
Name
Regina Barzilay
Degrees
Ph.D
Institution
Massachusetts Institute of Technology
Position Title
Delta Electronics Professor, EECS
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-885
Initial CDAS Request Approval
Feb 16, 2022
Title
A Deep Learning Model for Improved Cancer Risk Prediction in Sequential Lung Screening X-Rays
Summary
We will analyze the X-Ray examinations of the chest obtained in participants in the National Lung Screening Trial prior to the tissue diagnosis of lung cancer. The X-Ray examinations of patients diagnosed with lung cancer will be compared with X-Ray examinations of patients who did not develop lung cancer during the trial matched for age, gender and smoking exposure. We will also leverage the sequential screenings per patient to capture changes in the imaging features that are predictive of lung cancer. We postulate that a deep learning algorithm can be trained to estimate the risk for developing a clinically active lung cancer within the next 12 months (1-year risk) and within the next 2 to 6 years.
Aims
- Build a state of the art deep learning approach for lung cancer risk prediction
- Model the temporal changes in X-Ray images for individual patients
- Leverage sequential images over time to improve cancer risk prediction over non-sequential approaches
Collaborators
Regina Barzilay, PhD, MIT