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Deep learning based assessment of vascular aging to improve prognostication in patients at risk for lung cancer

Principal Investigator
Name
Jakob Weiss
Degrees
MD
Institution
University Hospital Freiburg
Position Title
Radiologist
Email
About this CDAS Project
Study
NLST (Learn more about this study)
Project ID
NLST-816
Initial CDAS Request Approval
Jul 15, 2021
Title
Deep learning based assessment of vascular aging to improve prognostication in patients at risk for lung cancer
Summary
Age-related changes in aortic geometry are associated with negative effects on cardiovascular disease. Current guidelines recommend cardiovascular risk stratification using clinical scores. Recently, aortic arch width was reported as a measure augmenting standard cardiovascular risk factors and coronary artery calcium for prediction of incident adverse cardiovascular events. However, It is unknown whether radiomic features can add to traditional cardiovascular risk factors as predictors for cardiovascular death and all-cause mortality.
Aims

1. Develop and test deep learning models to segment the heart and large vessels.
2. Investigate whether traditional and novel radiomic features extracted from the heart and vasculature can add to standard cardiovascular risk factors to predict cardiovascular death and all-cause mortality in individuals at risk for lung cancer.

Collaborators

Shadi Albarqouni, PhD, Helmholtz AI, Helmholtz Center Munich, Germany