Machine learning based lung cancer identification and characterization
Principal Investigator
Name
Richard Vlasimsky
Degrees
B.S., M.B.A.
Institution
IMIDEX
Position Title
CEO
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-495
Initial CDAS Request Approval
Apr 5, 2019
Title
Machine learning based lung cancer identification and characterization
Summary
Radiologist are under significant pressure to increase their reading efficiency, while at the same time to improve their diagnostic accuracy. This is a particularly the case with cancer, where early detection and treatment can make a big difference in both morbidity and mortality. As a result, radiological examinations for cancer often lead to false positives, putting the patient at un-necessary risk from invasive procedures such as lung biopsies.
This project will use machine learning technology to develop 3D image recognition algorithms that intercept CT images for early cancer detection and treatment guidance. Evaluation of the algorithms will be performed on a hold out sample of data and sensitivity / specificity ROC curves will be generated to assess accuracy.
This project will use machine learning technology to develop 3D image recognition algorithms that intercept CT images for early cancer detection and treatment guidance. Evaluation of the algorithms will be performed on a hold out sample of data and sensitivity / specificity ROC curves will be generated to assess accuracy.
Aims
The aims of this project are:
-provide early detection of cancer from CT,
-more accurately characterize and classify lesions from CT based on pathology.
-predict the response of different treatment modalities based on the radiological CT,
-predict the progression of cancer based on series of radiological CT’s and ultimately mortality.
Collaborators
Kenneth Bellian, MD
Jake Gelfand
Roger Nichols, MD
Tom Suby-Long, MD