Skip to Main Content

An official website of the United States government

Government Funding Lapse

Because of a lapse in government funding, the information on this website may not be up to date, transactions submitted via the website may not be processed, and the agency may not be able to respond to inquiries until appropriations are enacted. The NIH Clinical Center (the research hospital of NIH) is open. For more details about its operating status, please visit  cc.nih.gov. Updates regarding government operating status and resumption of normal operations can be found at OPM.gov.

Machine learning to predict risk from chest radiographs and clinical factors

Principal Investigator
Name
Michael Lu
Degrees
M.D., M.P.H.
Institution
The General Hospital Corporation d/b/a Massachusetts General Hospital
Position Title
Co-Director, Cardiovascular Imaging Research Center
Email
About this CDAS Project
Study
PLCO (Learn more about this study)
Project ID
PLCO-331
Initial CDAS Request Approval
Dec 19, 2017
Title
Machine learning to predict risk from chest radiographs and clinical factors
Summary
Heart disease and cancer are the two most common causes of death in the developed world. PLCO participants underwent screening chest radiography; analysis of these chest radiographs may be useful to predict incident morbidity and mortality. The goal of this proposal is to use machine learning to predict the risk of cancer, heart disease, and death from chest radiographs and other clinical information available in the PLCO. Whether this approach is additive to existing risk prediction models will be assessed via questionnaire data about patient risk factors, medical history, and behaviors including smoking, exercise, alcohol consumption. We will also identify genetic loci associated with machine learning-based risk estimates to gain insight into the biologic bases of machine learning-based predictions.
Aims

1) To predict cancer, heart disease, and death from chest radiographs using machine learning.
2) To compare the performance of this approach to existing risk prediction tools.
3) To identify genetic loci associated with machine learning-based risk estimates

Collaborators

Hugo Aerts (Harvard)
Parastou Eslami (Harvard)
Borek Foldyna (Harvard)
Udo Hoffmann (Harvard)
Alexander Ivanov (Harvard)
Chintan Parmar (Harvard)
Roman Zeleznik (Harvard)
Kaavya Paruchuri (Harvard)
Pradeep Natarajan (Harvard)
Mesbeh Uddin (Harvard)

Related Publications