ArticleRespiratory research2017
Densitometric and local histogram based analysis of computed tomography images in patients with idiopathic pulmonary fibrosis.
Article in Respiratory research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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Who cites it
64 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Assessment of fibrosing interstitial lung disease progression: CT visual score on radiological progressive pulmonary fibrosis criteria versus 3D-CT volume-histogram analysis.Japanese journal of radiology · 2026Article
- Attenuation Levels of Lung Fields in Quantitative Computed Tomography Evaluation Are Associated With Overall Survival and the Incidence of Acute Exacerbation in Combined Pulmonary Fibrosis and Emphysema: A Single-Center Retrospective Observational Study.Health science reports · 2026Article
- Quantitative comparative analysis of different CT modalities in systemic sclerosis-associated interstitial lung disease: correlations with lung function, serological biomarkers, and disease stratification.Journal of thoracic disease · 2026Article
- Radiomic profiling of chest CT in a cohort of sarcoidosis cases.Scientific reports · 2026Article
- Development of imaging protocol and radiomics-based nomogram for assessing lesion reversibility in connective tissue disease-associated interstitial lung disease.BMC medical imaging · 2026Article
- Extent of alveolar collapse in expiratory CT as a prognostic marker in idiopathic pulmonary fibrosis.PloS one · 2026Article
- Fully automated Bayesian analysis for quantifying the extent and distribution of pulmonary perfusion changes on CT pulmonary angiography in CTEPH.European radiology · 2025Article
- Visual and Quantitative Interstitial Lung Abnormality Progression in COPDGene.American journal of respiratory and critical care medicine · 2025Article
- Effect of slice thickness on quantitative analysis of interstitial lung disease: a retrospective volumetric chest CT study.La Radiologia medica · 2025Article
- Enhanced Respiratory Sound Classification Using Deep Learning and Multi-Channel Auscultation.Journal of clinical medicine · 2025Article
- Computer-Aided Evaluation of Interstitial Lung Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Integrating Radiomics Signature into Clinical Pathway for Patients with Progressive Pulmonary Fibrosis.Diagnostics (Basel, Switzerland) · 2025Review
- Comparing multi-texture fibrosis analysis versus binary opacity-based abnormality detection for quantitative assessment of idiopathic pulmonary fibrosis.Scientific reports · 2025Article
- Quantitative Assessment of High-resolution Computer Tomography Imaging in a Super-responder to Nintedanib Therapy in a Patient with Idiopathic Pulmonary Fibrosis.Internal medicine (Tokyo, Japan) · 2025Article
- Rheumatoid arthritis, quantitative parenchymal lung features and mortality among smokers.Rheumatology (Oxford, England) · 2025Article
- Fully automatic quantification of pulmonary fat attenuation volume by CT: an exploratory pilot study.European radiology experimental · 2024Observational
- Evaluation of e-Lung automated quantitative computed tomography biomarkers in idiopathic pulmonary fibrosis.ERJ open research · 2024Article
- Quantitative CT analysis of idiopathic pulmonary fibrosis and correlation with lung function study.BMC pulmonary medicine · 2024Article
- From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.The British journal of radiology · 2024Review
4 more citing papers are in PubMed but not listed here.
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18 authors.
Funding
Abstract
backgroundPrior studies of clinical prognostication in idiopathic pulmonary fibrosis (IPF) using computed tomography (CT) have often used subjective analyses or have evaluated quantitative measures in isolation. This study examined associations between both densitometric and local histogram based quantitative CT measurements with pulmonary function test (PFT) parameters and mortality. In addition, this study sought to compare risk prediction scores that incorporate quantitative CT measures with previously described systems.
methodsForty six patients with biopsy proven IPF were identified from a registry of patients with interstitial lung disease at Brigham and Women's Hospital in Boston, MA. CT scans for each subject were visually scored using a previously published method. After a semi-automated method was used to segment the lungs from the surrounding tissue, densitometric measurements including the percent high attenuating area, mean lung density, skewness and kurtosis were made for the entirety of each patient's lungs. A separate, automated tool was used to detect and quantify the percent of lung occupied by interstitial lung features. These analyses were used to create clinical and quantitative CT based risk prediction scores, and the performance of these was compared to the performance of clinical and visual analysis based methods.
resultsAll of the densitometric measures were correlated with forced vital capacity and diffusing capacity, as were the total amount of interstitial change and the percentage of interstitial change that was honeycombing measured using the local histogram method. Higher percent high attenuating area, higher mean lung density, lower skewness, lower kurtosis and a higher percentage of honeycombing were associated with worse transplant free survival. The quantitative CT based risk prediction scores performed similarly to the clinical and visual analysis based methods.
conclusionsBoth densitometric and feature based quantitative CT measures correlate with pulmonary function test measures and are associated with transplant free survival. These objective measures may be useful for identifying high risk patients and monitoring disease progression. Further work will be needed to validate these measures and the quantitative imaging based risk prediction scores in other cohorts.
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