ArticleJournal of clinical medicine2020
Automated CT Analysis of Major Forms of Interstitial Lung Disease.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- The effect of CALIPER-derived parameters for idiopathic pulmonary fibrosis in predicting prognosis, progression, and mortality: a systematic review.European radiology · 2023Pooled it
- AI-based CT quantification reveals small airway loss and vascular simplification in rheumatoid arthritis-associated lung disease.Clinical rheumatology · 2026Article
- Quantitative CT analysis in interstitial pneumonia with autoimmune features: diagnostic and prognostic insights from a retrospective cohort study.Insights into imaging · 2026Article
- Absolute and lung volume-normalized deep learning-derived pulmonary vascular measures in idiopathic pulmonary fibrosis: distinct clinical associations and the role of lung volume in prognosis.Frontiers in medicine · 2026Article
- A Quantitative Imaging Measure of Progressive Pulmonary Fibrosis.American journal of respiratory and critical care medicine · 2025Article
- The potential of semi-quantitative and quantitative methods in predicting progression in rheumatoid arthritis-associated interstitial lung disease.Clinical rheumatology · 2025Observational
- Warrick score in rheumatoid-arthritis interstitial lung disease: a promising tool for assessing the extent and progression of lung involvement.Advances in rheumatology (London, England) · 2025Article
- Quantitative chest computed tomography predicts mortality in systemic sclerosis: A longitudinal study.PloS one · 2024Article
- Present and future perspectives in early diagnosis and monitoring for progressive fibrosing interstitial lung diseases.Frontiers in medicine · 2023Article
- Quantitative CT and machine learning classification of fibrotic interstitial lung diseases.European radiology · 2022Article
- 3D Single-Breath Chemical Shift Imaging Hyperpolarized Xe-129 MRI of Healthy, CF, IPF, and COPD Subjects.Tomography (Ann Arbor, Mich.) · 2022Article
- Computed Tomography Imaging in ILD: New Trends for the Clinician.Journal of clinical medicine · 2022Article
- Reticulation pattern without honeycombing on high-resolution CT is associated with the risk of disease progression in interstitial lung diseases.BMC pulmonary medicine · 2022Article
- A Novel Method for Lung Image Processing Using Complex Networks.Tomography (Ann Arbor, Mich.) · 2022Article
- Correlation of respiratory oscillometry with CT image analysis in a prospective cohort of idiopathic pulmonary fibrosis.BMJ open respiratory research · 2022Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 1 country.
Funding
Abstract
This study aimed to determine diagnostic and prognostic differences in major forms of interstitial lung disease using quantitative CT imaging. A retrospective study of 225 subjects with a multidisciplinary diagnosis of idiopathic pulmonary fibrosis (IPF), interstitial pneumonia with autoimmune features (IPAF), connective tissue disease (CTD), or chronic hypersensitivity pneumonitis (cHP) was conducted. Non-contrast CT scans were analyzed using the Computer Aided Lung Informatics for Pathology Evaluation and Rating (CALIPER) program. Resulting data were analyzed statistically using ANOVA and Student's
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.