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

doi:10.1148/radiol.222828

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

Radiology. 2023 May;307(4):e222828. doi: 10.1148/radiol.222828. Epub 2023 Apr 25.

Authors

Kum Ju Chae^#¹, Soyeoun Lim^#¹, Joon Beom Seo¹, Hye Jeon Hwang¹, Hyemi Choi¹, David Lynch¹, Gong Yong Jin¹

Affiliation

¹ From the Department of Radiology, Research Institute of Clinical Medicine of Jeonbuk National University-Biomedical Research Institute of Jeonbuk National University Hospital, 20 Geonjiro Deokjin-gu, Jeonju-si, Jeollabuk-do, Korea 54907 (K.J.C., G.Y.J.); Department of Radiology, Jeonbuk National University Medical School, Jeonju, Korea (K.J.C., G.Y.J.); Department of Radiology, National Jewish Health, Denver, Colo (K.J.C., H.J.H., D.L.); Department of Radiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea (S.L.); Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea (J.B.S., H.J.H.); and Department of Statistics and Institute of Applied Statistics, Jeonbuk National University, Jeonju, Republic of Korea (H.C.).

^# Contributed equally.

PMID: 37097142
DOI: 10.1148/radiol.222828

Abstract

Background Interstitial lung abnormalities (ILAs) are associated with worse clinical outcomes, but ILA with lung cancer screening CT has not been quantitatively assessed. Purpose To determine the prevalence of ILA at CT examinations from the Korean National Lung Cancer Screening Program and define an optimal lung area threshold for ILA detection with CT with use of deep learning-based texture analysis. Materials and Methods This retrospective study included participants who underwent chest CT between April 2017 and December 2020 at two medical centers participating in the Korean National Lung Cancer Screening Program. CT findings were classified by three radiologists into three groups: no ILA, equivocal ILA, and ILA (fibrotic and nonfibrotic). Progression was evaluated between baseline and last follow-up CT scan. The extent of ILA was assessed visually and quantitatively with use of deep learning-based texture analysis. The Youden index was used to determine an optimal cutoff value for detecting ILA with use of texture analysis. Demographics and ILA subcategories were compared between participants with progressive and nonprogressive ILA. Results A total of 3118 participants were included in this study, and ILAs were observed with the CT scans of 120 individuals (4%). The median extent of ILA calculated by the quantitative system was 5.8% for the ILA group, 0.7% for the equivocal ILA group, and 0.1% for the no ILA group (P < .001). A 1.8% area threshold in a lung zone for quantitative detection of ILA showed 100% sensitivity and 99% specificity. Progression was observed in 48% of visually assessed fibrotic ILAs (15 of 31), and quantitative extent of ILA increased by 3.1% in subjects with progression. Conclusion ILAs were detected in 4% of the Korean lung cancer screening population. Deep learning-based texture analysis showed high sensitivity and specificity for detecting ILA with use of a 1.8% lung area cutoff value. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Egashira and Nishino in this issue.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Deep Learning*
Disease Progression
Early Detection of Cancer
Humans
Lung / diagnostic imaging
Lung Neoplasms* / diagnostic imaging
Lung Neoplasms* / epidemiology
Prevalence
Republic of Korea / epidemiology
Retrospective Studies
Tomography, X-Ray Computed / methods