Open AccessDeep 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.