z-logo
open-access-imgOpen Access
Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model
Author(s) -
Michael T. Lu,
Vineet K. Raghu,
Thomas Mayrhofer,
Hugo J.W.L. Aerts,
Udo Hoffmann
Publication year - 2020
Publication title -
annals of internal medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.839
H-Index - 390
eISSN - 1539-3704
pISSN - 0003-4819
DOI - 10.7326/m20-1868
Subject(s) - medicine , national lung screening trial , lung cancer , lung cancer screening , chest radiograph , receiver operating characteristic , radiology , incidence (geometry) , lung , cancer screening , cancer , oncology , physics , optics
Lung cancer screening with chest computed tomography (CT) reduces lung cancer death. Centers for Medicare & Medicaid Services (CMS) eligibility criteria for lung cancer screening with CT require detailed smoking information and miss many incident lung cancers. An automated deep-learning approach based on chest radiograph images may identify more smokers at high risk for lung cancer who could benefit from screening with CT.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here