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Journal of Cardio-Thoracic Medicine، جلد ۱۳، شماره ۲، صفحات ۱۵۴۵-۱۵۵۴
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عنوان انگلیسی Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance
چکیده انگلیسی مقاله Pulmonary fibrosis represents a heterogeneous group of interstitial lung diseases (ILDs) marked by progressive scarring of the lung parenchyma and declining respiratory function. High-resolution computed tomography (HRCT) plays a pivotal role in diagnosing and managing fibrotic ILDs, particularly idiopathic pulmonary fibrosis (IPF), connective tissue disease-associated ILD (CTD-ILD), and chronic hypersensitivity pneumonitis (CHP). This narrative review explores key imaging patterns observable on chest CT and their prognostic implications across these major subtypes. We conducted a literature review of studies published between 2010 and 2025 using PubMed, Scopus, and Web of Science, focusing on CT features, subtype differentiation, prognostic imaging biomarkers, and recent innovations in radiomics and artificial intelligence (AI). Characteristic HRCT findings, such as honeycombing, reticulation, ground-glass opacities, and traction bronchiectasis, were analyzed in the context of usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP) patterns. UIP pattern on HRCT is strongly associated with IPF and confers a worse prognosis compared to NSIP or other non-UIP patterns. Quantitative imaging methods and automated CT analytics offer objective measurements of fibrosis extent and have demonstrated promising correlations with physiologic parameters such as forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO). Emerging AI tools enhance disease classification, monitor progression, and support clinical decision-making. Despite substantial advances, challenges such as inter-reader variability, limited access to quantitative software, and unstandardized CT protocols persist. Furthermore, AI applications require broader validation in multicenter cohorts before routine implementation. This review highlights the central role of HRCT in the multidisciplinary evaluation of pulmonary fibrosis. It underscores the prognostic significance of specific imaging features and advocates for standardized imaging interpretation and the integration of AI to refine diagnostic accuracy and therapeutic planning.
کلیدواژه‌های انگلیسی مقاله CT Scan, ILD, AI, IPF, NSIP
نویسندگان مقاله | Mehrshad Dabbagh
Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.


| Mohammad Amin Shajareh Pour Salavati
Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.


| Yas Zeinaly Parizi
Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.
نشانی اینترنتی https://jctm.mums.ac.ir/article_26359.html
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زبان مقاله منتشر شده en
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نوع مقاله منتشر شده Review Article
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