Accurate malignant risk estimation of pulmonary nodules using deep learning
Principal Investigator
Name
Colin Jacobs
Degrees
Ph.D.
Institution
Radboud University Medical Center
Position Title
Assistant Professor
Email
About this CDAS Project
Study
NLST
(Learn more about this study)
Project ID
NLST-846
Initial CDAS Request Approval
Oct 19, 2021
Title
Accurate malignant risk estimation of pulmonary nodules using deep learning
Summary
In this project, we aim to develop and validate artificial intelligence algorithms that are able to automatically assess the malignancy risk of pulmonary nodules in one or multiple CT scans.
NLST data will be used to train the AI model by extracting information from baseline and follow-up scans.
NLST data will be used to train the AI model by extracting information from baseline and follow-up scans.
Aims
To develop and validate a deep learning (DL) algorithms for malignancy risk estimation of pulmonary nodules detected at screening CT.
Collaborators
Kiran Vaidhya Venkadesh, Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
Bram van Ginneken, Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud University Medical Center, Nijmegen, Netherlands
Mathias Prokop, Department of Medical Imaging, Radboud University Medical Center Nijmegen, Netherlands
Related Publications
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Prior CT Improves Deep Learning for Malignancy Risk Estimation of Screening-detected Pulmonary Nodules.
Venkadesh KV, Aleef TA, Scholten ET, Saghir Z, Silva M, Sverzellati N, Pastorino U, van Ginneken B, Prokop M, Jacobs C
Radiology. 2023 Aug; Volume 308 (Issue 2): Pages e223308 PUBMED