Predicting Lung Cancer Treatment Using an End to End 3D Serial Imaging CNN Model
Principal Investigator
Name
Amy Li
Degrees
B.S.E.
Institution
Princeton University
Position Title
Student
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-618
Initial CDAS Request Approval
Dec 11, 2019
Title
Predicting Lung Cancer Treatment Using an End to End 3D Serial Imaging CNN Model
Summary
This project attempts to evaluate 3D computed-tomography (CT) radiomic features for their capability to predict four main cancer endpoints: 1) distant metastasis (DM) 2) local recurrence (LR) 3) progression and 4) overall survival via the use of a time series 3D convoluted neural network in non-small cell lung cancer.
Aims
- identify a list of significant radiomic features that predict lung cancer treatment response
- build a functioning CNN model with a significant accuracy in solving the project problem
Collaborators
Ludovic Tangpi, Advisor, Princeton University