Interstitial Lung Abnormalities at CT in the Korean National Lung Cancer Screening Program: Prevalence and Deep Learning–based Texture Analysis

Kum Ju Chae, Soyeoun Lim, Joon Beom Seo, Hye Jeon Hwang, Hyemi Choi, David Lynch, Gong Yong Jin
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Lung Texture

Date Published

The prevalence of interstitial lung abnormalities (ILAs) in the Korean lung cancer screening population was found to be 4%, as identified in this retrospective study. The study aimed to quantitatively assess ILA with CT examinations from the Korean National Lung Cancer Screening Program using a deep learning–based texture analysis. The study included participants who underwent chest CT between April 2017 and December 2020. The findings were classified by three radiologists into three groups: no ILA, equivocal ILA, and ILA (fibrotic and nonfibrotic). ILA progression was evaluated between baseline and last follow-up CT scans. The extent of ILA was assessed visually and quantitatively with the help of deep learning–based texture analysis using the AVIEW Lung Texture ILA, v software by Coreline Soft. This deep learning-based lung texture analysis software demonstrated high sensitivity and specificity for detecting ILA with a 1.8% lung area cutoff value. This study indicates that ILAs are prevalent in the Korean lung cancer screening population and can be effectively detected and quantified using deep learning–based texture analysis.


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