Long-Term Follow-Up of Interstitial Lung Abnormalities in Low-Dose Chest CT in Health Screening: Exploring the Predictors of Clinically Significant Interstitial Lung Diseases Using Artificial Intelligence-Based Quantitative CT Analysis

This study analyzed the long-term changes in Interstitial Lung Abnormalities (ILA) detected during health screenings and identified predictors for the progression to Interstitial Lung Disease (ILD). A review of 36,891 low-dose chest CT records from 2003 to 2021 identified 101 patients with ILA, of whom 23 (23%) were diagnosed with ILD after an average of 8.7 years. Subpleural fibrotic ILA observed on initial CT was associated with a higher likelihood of ILD progression, and disease progression on follow-up CT increased the risk of ILD (p < 0.05). Additionally, AI-based quantitative analysis using Aview Lung Texture ILA (Coreline Soft) was employed to objectively evaluate fibrosis patterns and disease progression. The study found that the emergence of respiratory symptoms (OR 5.56, p = 0.022) and disease progression on follow-up CT (OR 4.07, p = 0.050) were significant predictors of ILD, contributing to early diagnosis and management strategies.