ArticleEuropean journal of radiology open2020
Interobserver variability in high-resolution CT of the lungs.
Article in European journal of radiology open, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.European journal of medical research · 2025Pooled it
- Meta-Analysis of Interobserver Agreement in Assessment of Interstitial Lung Disease Using High-Resolution CT.Radiology · 2024Pooled it
- Evolving landscape of imaging-based evaluation in systemic autoimmune rheumatic disease-associated interstitial lung disease: from visual assessment to quantitative artificial intelligence-assisted evaluation.Journal of rheumatic diseases · 2026Review
- Quantitative CT Patterns and Functional Loss Related to Welding Fume Exposure, While Handling the Confounding Effect of Cigarette Smoking.Canadian respiratory journal · 2026Article
- Risk factors and prognostic indicators for progressive fibrosing interstitial lung disease: a deep learning-based CT quantification approach.European radiology · 2025Article
- Associations of interstitial lung disease subtype and CT pattern with lung function and survival.Thorax · 2025Article
- Influence of content-based image retrieval on the accuracy and inter-reader agreement of usual interstitial pneumonia CT pattern classification.European radiology · 2025Article
- AI-based HRCT quantification reveals DLCO and TLC as key determinants of ILD severity in connective tissue diseases.RMD open · 2025Article
- Fibrotic and vascular abnormalities quantified by an AI-based model are associated with outcomes in patients with idiopathic pulmonary fibrosis.BMC pulmonary medicine · 2025Article
- Prospective validation of an artificial intelligence assessment in a cohort of applicants seeking financial compensation for asbestosis (PROSBEST).European radiology experimental · 2025Article
- What role does artificial intelligence-driven quantitative analysis of chest computed tomography play in providing pulmonary function for idiopathic pulmonary fibrosis patients undergoing pirfenidone treatment?Quantitative imaging in medicine and surgery · 2025Article
- Effect of slice thickness on quantitative analysis of interstitial lung disease: a retrospective volumetric chest CT study.La Radiologia medica · 2025Article
- Evaluating the robustness of deep learning models trained to diagnose idiopathic pulmonary fibrosis using a retrospective study.Medical physics · 2025Article
- Article
- Automated AI-based image analysis for quantification and prediction of interstitial lung disease in systemic sclerosis patients.Respiratory research · 2025Article
- Ultra-low-dose vs. standard-of-care-dose CT of the chest in patients with post-COVID-19 conditions-a prospective intra-patient multi-reader study.European radiology · 2024Article
- Artificial intelligence-based quantification of pulmonary HRCT (AIqpHRCT) for the evaluation of interstitial lung disease in patients with inflammatory rheumatic diseases.Rheumatology international · 2024Article
- Phenotyping of COPD with MRI in comparison to same-day CT in a multi-centre trial.European radiology · 2024Article
- From pixels to prognosis: unlocking the potential of deep learning in fibrotic lung disease imaging analysis.The British journal of radiology · 2024Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo quantify the interobserver variability among the most frequently encountered parenchymal patterns in High Resolution CT (HRCT) and to compare the interobserver variability in the application of the 2011 and 2018 usual interstitial pneumonia (UIP) criteria according to the joint guidelines from international thoracic and respiratory societies. MATERIAL AND
methodsTwo observers independently evaluated 126 HRCT, with examples of most common parenchymal patterns, and noted the presence of each pattern. The readers also noted whether the findings met the 2011 criteria for UIP. In a second reading, the same readers noted whether the HRCT met the UIP criteria according to the 2018 UIP update.
resultsThe kappa values for interobserver variability for the different patterns ranged from 0.28 (intralobular lines) to 0.85 (tree-in-bud nodules). The kappa value for UIP pattern was similar for 2011 and 2018 criteria, 0.58 and 0.69, respectively. Compared to the 2011 UIP criteria, there was no statistically significant difference in the number of HRCT classified as UIP using the 2018 criteria.
conclusionsThere is a substantial variation in interobserver agreement between the different parenchymal patterns, which suggests that some patterns a more easily identified than others. There is also a considerable reader variation in the assessment of UIP applying the 2011 UIP criteria as well as applying the 2018 UIP update.
Indexed as
Identifiers
What OpenQuestion holds
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.