Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance
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 id...
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| Format: | Article |
| Language: | English |
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Mashhad University of Medical Sciences
2025-06-01
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| Series: | Journal of Cardio-Thoracic Medicine |
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| Online Access: | https://jctm.mums.ac.ir/article_26359_86666ae9128bcde8249a6240fd208fc4.pdf |
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| author | Mehrshad Dabbagh Mohammad Amin Shajareh Pour Salavati Yas Zeinaly Parizi |
| author_facet | Mehrshad Dabbagh Mohammad Amin Shajareh Pour Salavati Yas Zeinaly Parizi |
| author_sort | Mehrshad Dabbagh |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8b43bb892f3448f698ce795e819c26d3 |
| institution | DOAJ |
| issn | 2345-2447 2322-5750 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Mashhad University of Medical Sciences |
| record_format | Article |
| series | Journal of Cardio-Thoracic Medicine |
| spelling | doaj-art-8b43bb892f3448f698ce795e819c26d32025-08-20T03:08:39ZengMashhad University of Medical SciencesJournal of Cardio-Thoracic Medicine2345-24472322-57502025-06-011321545155410.22038/jctm.2025.88917.149226359Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic SignificanceMehrshad Dabbagh0Mohammad Amin Shajareh Pour Salavati1Yas Zeinaly Parizi2Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.Student Research Committee, School of Medicine, Kerman University of Medical Sciences, Kerman, Iran.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.https://jctm.mums.ac.ir/article_26359_86666ae9128bcde8249a6240fd208fc4.pdfct scanildaiipfnsip |
| spellingShingle | Mehrshad Dabbagh Mohammad Amin Shajareh Pour Salavati Yas Zeinaly Parizi Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance Journal of Cardio-Thoracic Medicine ct scan ild ai ipf nsip |
| title | Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance |
| title_full | Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance |
| title_fullStr | Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance |
| title_full_unstemmed | Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance |
| title_short | Chest CT in Pulmonary Fibrosis: A Narrative Review of Imaging Patterns and Their Prognostic Significance |
| title_sort | chest ct in pulmonary fibrosis a narrative review of imaging patterns and their prognostic significance |
| topic | ct scan ild ai ipf nsip |
| url | https://jctm.mums.ac.ir/article_26359_86666ae9128bcde8249a6240fd208fc4.pdf |
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