Interstitial Lung Abnormality: A Bayesian Approach for the Radiologist

Abstract

Interstitial lung abnormalities (ILA) refer to incidental changes seen on chest CT, usually in people without a formal diagnosis of interstitial lung disease (ILD). Interest in ILA has grown in recent years, partly because they may signal early fibrotic lung changes and partly because CT scans are being used more often for screening. Defined by the Fleischner Society and updated by the ATS, ILA encompasses 3 patterns: nonsubpleural, subpleural nonfibrotic, and subpleural fibrotic. Subpleural fibrotic ILA, marked by traction bronchiectasis and honeycombing, carries the greatest likelihood of progression and the poorest prognosis. Older age, smoking history, and certain genetic traits such as the MUC5B promoter variant increase the likelihood of finding ILA. The presence of ILA also appears to raise the risk of lung cancer. For management, current guidelines recommend tailoring follow-up based on individual risk, with closer surveillance for patients more likely to progress. Newer tools, including quantitative imaging and artificial intelligence, may help detect subtle disease earlier and refine risk assessment. Despite advances, challenges remain in defining progression thresholds and treatment strategies, highlighting the need for further research.