Direct prediction of cardiovascular mortality from low-dose chest CT using deep learning

Sanne G. M. van Velzen, Majd Zreik, Nikolas Lessmann, Max A. Viergever, Pim A. de Jong, Helena M. Verkooijen, Ivana Išgum

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)

Abstract

Cardiovascular disease (CVD) is a leading cause of death in the lung cancer screening population. Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD. Existing methods analyzing CT images from lung cancer screening for prediction of CVD events or mortality use engineered features extracted from the images combined with patient information. In this work we propose a method that automatically predicts 5-year cardiovascular mortality directly from chest CT scans without the need for hand-crafting image features. A set of 1,583 participants of the National Lung Screening Trial was included (1,188 survivors, 395 nonsurvivors). Low-dose chest CT images acquired at baseline were analyzed and the follow-up time was 5 years. To limit the analysis to the heart region, the heart was first localized by our previously developed algorithm for organ localization exploiting convolutional neural networks. Thereafter, a convolutional autoencoder was used to encode the identified heart region. Finally, based on the extracted encodings subjects were classified into survivors or non-survivors using a neural network. The performance of the method was assessed in eight cross-validation experiments with 1,433 images used for training, 50 for validation and 100 for testing. The method achieved a performance with an area under the ROC curve of 0.73. The results demonstrate that prediction of cardiovascular mortality directly from low-dose screening chest CT scans, without hand-crafted features, is feasible, allowing identification of subjects at risk of fatal CVD events.
Original languageEnglish
Title of host publicationMedical Imaging 2019: Image Processing
EditorsBennett A. Landman, Elsa D. Angelini
PublisherSPIE
Volume10949
ISBN (Electronic)9781510625457
DOIs
Publication statusPublished - 2019
EventMedical Imaging 2019: Image Processing - San Diego, United States
Duration: 19 Feb 201921 Feb 2019

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE

Conference

ConferenceMedical Imaging 2019: Image Processing
Country/TerritoryUnited States
CitySan Diego
Period19/02/201921/02/2019

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