Interstitial lung disease associated with anti-aminoacyl-tRNA synthetase syndrome: quantitative evaluation of CT after initial treatment and long-term follow-up.

Background: Visual evaluation of interstitial lung disease (ILD)-related changes can generate intra- and inter-observer errors. However, recent deep learning (DL) algorithm advances have facilitated accurate lung segmentation, lesion characterization, and quantification.

Purpose: To evaluate the treatment response and long-term course in ILD associated with anti-aminoacyl-tRNA synthetase syndrome (anti-ARS ILD) using a DL algorithm.

Material and methods: Patients with anti-ARS ILD who underwent both pre- and post-initial-treatment computed tomography (CT) (n = 68) were divided into two groups (responders and non-responders) according to forced vital capacity improvement after initial treatment. We also analyzed the CT images of patients for whom long-term follow-up CT (>5 years) was performed after post-treatment CT (n = 43). DL analysis was used to classify CT imaging features into five patterns: normal; ground-glass opacity (GGO); consolidation; fibrotic lesions; and emphysema.

Results: The initial responder group had a larger volume of consolidation. Consolidation and GGO volumes decreased after initial treatment in both groups. However, whole-lung and normal-area volumes increased in the responder group; conversely, there was no significant increase in the non-responder group. At the long-term follow-up, fibrotic lesions significantly increased in both groups. The emphysema pattern increased significantly in both groups after initial treatment and long-term follow-up. Six of 26 (23.1%) responders and 8 of 17 (47.1%) non-responders were judged as having progressive pulmonary fibrosis.

Conclusion: DL-based analysis facilitated the chronological evaluation of anti-ARS ILD. During the long-term follow-up, anti-ARS ILD was associated with chronological progression, regardless of initial treatment efficacy.