CT Quantification of Interstitial Lung Abnormality and Interstitial Lung Disease: From Technical Challenges to Future Directions

Authors
Choe, Jooae MD, PhD; Hwang, Hye Jeon MD, PhD; Lee, Sang Min MD, PhD; Yoon, Jihye PhD; Kim, Namkug PhD; Seo, Joon Beom MD, PhD
Journal
Investigative Radiology
Related Product

Lung Texture

Date Published
2024.07
Summary

This review focuses on the role of quantitative computed tomography (CT) in evaluating interstitial lung disease (ILD) and interstitial lung abnormalities (ILA). ILD includes various lung disorders requiring clinical, imaging, and pathological data for diagnosis and management. Traditional visual assessments of ILD via CT are subjective and prone to reader variability, while automated quantitative CT provides a more objective and consistent evaluation method. Technological advancements have improved the accuracy and reliability of these measurements. ILAs, which are potential preclinical ILD findings seen in over 5% of any lung zone on CT scans, have gained clinical importance. The accurate identification of ILA remains challenging due to its subjective definition, making quantitative tools crucial. This review highlights how machine learning and deep learning in quantitative imaging can enhance the diagnosis and management of ILD and ILA by providing precise assessments. AVIEW software by Coreline Soft is mentioned as a tool used in this context.

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