Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.

ABSTRACT:

Background

Current lung cancer screening guidelines use mean diameter, volume or density of the largest lung nodule in the prior computed tomography (CT) or appearance of new nodule to determine the timing of the next CT. We aimed at developing a more accurate screening protocol by estimating the 3-year lung cancer risk after two screening CTs using deep machine learning (ML) of radiologist CT reading and other universally available clinical information.

Methods

A deep machine learning (ML) algorithm was developed from 25,097 participants who had received at least two CT screenings up to two years apart in the National Lung Screening Trial. Double-blinded validation was performed using 2,294 participants from the Pan-Canadian Early Detection of Lung Cancer Study (PanCan). Pe

SUBMITTER: Huang P 

PROVIDER: S-EPMC7450858 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature