High-resolution computed tomography assessment of connective tissue disease-associated interstitial lung disease: Advances in diagnosis and prognostic evaluation

Abstract

Connective tissue disease-associated interstitial lung disease (CTD-ILD), an important pulmonary manifestation of systemic autoimmune diseases and a major determinant of morbidity and mortality, is highly heterogeneous across disease subtypes, requiring reliable imaging assessment. High-resolution computed tomography (HRCT) remains the cornerstone imaging modality in this setting. This narrative review summarizes recent advances in HRCT-based assessment of CTD-ILD, based on a structured literature search of PubMed/MEDLINE, EMBASE, and Web of Science through May 31, 2026, with emphasis on diagnosis, prognostic assessment, and longitudinal monitoring. Conventional visual interpretation and semiquantitative scoring remain the established foundation of clinical assessment. Quantitative computed tomography provides an emerging objective adjunct but remains variably implemented across centers, whereas radiomics, machine learning, and deep learning offer promising capabilities for automated segmentation, high-dimensional phenotyping, pattern classification, and individualized risk prediction but remain predominantly investigational. Current evidence supports a layered imaging framework in which conventional and advanced approaches are complementary rather than competing; however, the maturity and strength of evidence vary substantially across CTD subtypes and clinical applications. Relatively more evidence is available for systemic sclerosis-associated ILD, rheumatoid arthritis-associated ILD, and inflammatory myopathy-associated ILD, whereas evidence for Sjögren syndrome-associated ILD and systemic lupus erythematosus-associated ILD remains comparatively limited. Future research should prioritize standardized imaging workflows, reproducibility, harmonized endpoints, prospective multicenter validation, and demonstration of clinical utility before broader implementation of advanced HRCT-derived biomarkers.

Keywords: Connective tissue disease-associated interstitial lung disease; High-resolution computed tomography; Semiquantitative scoring; Quantitative computed tomography; Radiomics; Machine learning

Core Tip: This narrative review summarizes current advances in high-resolution computed tomography (CT)-based assessment of connective tissue disease-associated interstitial lung disease across diagnosis, prognostic assessment, and longitudinal monitoring. Visual interpretation and semiquantitative scoring remain the established foundation of clinical practice, whereas quantitative CT provides an emerging objective adjunct. Radiomics, machine learning, and deep learning offer promising capabilities but remain predominantly investigational. We propose a layered, complementary framework that reflects current clinical readiness rather than a validated clinical pathway and highlight key priorities for future translation, including standardization, reproducibility, multicenter validation, harmonized endpoints, and demonstration of clinical utility.