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.

Predicting lung cancer prognosis with deep learning

Principal Investigator
Name
Francesco Ciompi
Institution
Radboud University Medical Center
Position Title
Postdoc
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-325
Initial CDAS Request Approval
Jun 30, 2017
Title
Predicting lung cancer prognosis with deep learning
Summary
In this project, we aim at designing a computer model based on deep learning able to automatically predict the prognosis of lung cancer patients. NLST histopathology images and NLST chest CT images will be used to train the computer model by combining information extracted from nodule growth and appearance, assessed in chest CT images, and from tissue architecture, analyzed in histopathology slides.
Aims

The automatic assessment of prognosis based on both nodule temporal evolution and tissue architecture in HE-stained histopathology slides.

Collaborators

Jeroen van der Laak, Computational Pathology Group, Radboud University Medical Center Nijmegen, Netherlands
Katrien Grunberg, Pathology Department, Radboud University Medical Center Nijmegen, Netherlands
Geert Litjens, Computational Pathology Group, Radboud University Medical Center Nijmegen, Netherlands
Bram van Ginneken, Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, Netherlands
Colin Jacobs, Diagnostic Image Analysis Group, Radboud University Medical Center Nijmegen, Netherlands