ReviewChest2023
Quantitative CT Scan Imaging of the Airways for Diagnosis and Management of Lung Disease.
Review in Chest, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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
21 citing papers in PubMed, 29 citations in OpenAlex.
- Chest CT imaging toward personalized management of chronic obstructive pulmonary disease, asthma, and bronchiectasis: pulmonologist perspectives.Japanese journal of radiology · 2026Review
- Airway segmentation on CT - A systematic review of machine learning tools.European journal of radiology open · 2026Article
- 3D computed tomography airway geometry for predicting bronchoscopic accessibility in peripheral pulmonary nodules: a prospective study.Translational lung cancer research · 2026Article
- Comparison of Pi10 and PiSlope calculation methods and association with lung function.European radiology · 2026Article
- Automated identification of chronic obstructive pulmonary disease and asthma using impulse oscillometry combined with quantitative computed tomography parameters and radiomics features.Journal of thoracic disease · 2026Article
- Review
- Evaluation of Quantitative Computed Tomography Indices in Patients with Pneumonia and Acute Respiratory Failure in the Intensive Care Unit (ICU).Diagnostics (Basel, Switzerland) · 2026Article
- Pathogenesis, Diagnostic Advances, and Therapeutic Management of Chronic Obstructive Pulmonary Disease: A Narrative Review.International journal of chronic obstructive pulmonary disease · 2026Review
- Article
- Quantitative CT-derived lobar inter-tapering as a candidate imaging feature associated with airway-predominant non-emphysematous COPD.Frontiers in medicine · 2026Article
- Global trends and hotspots in AI applications for CT detection of chronic obstructive pulmonary disease: A bibliometric analysis from 2012 to 2024.Lasers in medical science · 2025Review
- Micro-CT for the differentiation between live birth and stillbirth: A pilot study.Journal of forensic sciences · 2025Article
- Quantitative Computed Tomographic Clusters in C-BIOPRED Asthma Cohort: Association with Sputum Proteomics.MedComm · 2025Article
- An online explainable ensemble machine learning model for predicting epidermal growth factor receptor mutation status in lung adenocarcinoma.Translational lung cancer research · 2025Article
- Analysis of upper airway CT-based radiomics in adult obstructive sleep apnea.Frontiers in medicine · 2025Article
- Article
- Differentiating Emphysema From Emphysema-Dominated COPD Patients with CT Imaging Feature and Machine Learning.International journal of chronic obstructive pulmonary disease · 2025Article
- Airway tapering in COPD.The European respiratory journal · 2024Article
- 3D airway geometry analysis of factors in airway navigation failure for lung nodules.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease.Journal of medical imaging (Bellingham, Wash.) · 2024Article
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 at 2 institutions in 2 countries.
Funding
Abstract
CT scan imaging provides high-resolution images of the lungs in patients with chronic respiratory diseases. Extensive research over the last several decades has focused on developing novel quantitative CT scan airway measurements that reflect abnormal airway structure. Despite many observational studies demonstrating that associations between CT scan airway measurements and clinically important outcomes such as morbidity, mortality, and lung function decline, few quantitative CT scan measurements are applied in clinical practice. This article provides an overview of the relevant methodologic considerations for implementing quantitative CT scan airway analyses and provides a review of the scientific literature involving quantitative CT scan airway measurements used in clinical or randomized trials and observational studies of humans. We also discuss emerging evidence for the clinical usefulness of quantitative CT scan imaging of the airways and discuss what is required to bridge the gap between research and clinical application. CT scan airway measurements continue to improve our understanding of disease pathophysiologic features, diagnosis, and outcomes. However, a literature review revealed a need for studies evaluating clinical benefit when quantitative CT scan imaging is applied in the clinical setting. Technical standards for quantitative CT scan imaging of the airways and high-quality evidence of clinical benefit from management guided by quantitative CT scan imaging of the airways are required.
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.