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

ABSTRACT:

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 (>

SUBMITTER: Aoki R 

PROVIDER: S-EPMC11558930 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature