Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model.

ABSTRACT:

Background

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.

Objective

To develop and validate a convolutional neural network (CXR-LC) that predicts long-term incident lung cancer using data commonly available in the electronic medical record (EMR) (chest radiograph, age, sex, and whether currently smoking).

Design

Risk prediction study.

Setting

U.S. lung cancer screening trials.

Particip

SUBMITTER: Lu MT 

PROVIDER: S-EPMC9200444 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature