ReviewDeutsches Arzteblatt international2014
Radiological diagnosis in lung disease: factoring treatment options into the choice of diagnostic modality.
Review in Deutsches Arzteblatt international, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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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.
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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
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 69 citations in OpenAlex.
- Diagnostic accuracy of lung ultrasound score for bronchopulmonary dysplasia in preterm neonates: a systematic review and meta-analysis.Frontiers in pediatrics · 2025Pooled it
- ERS statement on the multidisciplinary respiratory management of ataxia telangiectasia.European respiratory review : an official journal of the European Respiratory Society · 2015Guideline
- Ultra-high-resolution synchrotron phase-contrast CT enables microstructural pulmonary imaging at clinical dose levels.Respiratory research · 2026Article
- An automated pipeline for computation and analysis of functional ventilation and perfusion lung MRI with matrix pencil decomposition: TrueLung.Zeitschrift fur medizinische Physik · 2025Article
- Deformable image registration of dark-field chest radiographs for functional lung assessment.Medical physics · 2025Article
- Chest radiography in interstitial lung disease: Accuracy and radiological features from a systematic review and meta-analysis.Sarcoidosis, vasculitis, and diffuse lung diseases : official journal of WASOG · 2025Article
- Global trends and developments in pulmonary magnetic resonance imaging research: a bibliometric analysis of the past decade.Quantitative imaging in medicine and surgery · 2025Article
- Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.Journal of imaging informatics in medicine · 2025Article
- An optimized transformer model for efficient detection of thoracic diseases in chest X-rays with multi-scale feature fusion.PloS one · 2025Article
- Intersection of Performance, Interpretability, and Fairness in Neural Prototype Tree for Chest X-Ray Pathology Detection: Algorithm Development and Validation Study.JMIR formative research · 2024Article
- Incidental findings are frequent in shoulder CT and MRI scans and increase with age.Journal of orthopaedics · 2024Article
- Automated Quality Control Solution for Radiographic Imaging of Lung Diseases.Journal of clinical medicine · 2024Article
- Revolutionizing Pulmonary Diagnostics: A Narrative Review of Artificial Intelligence Applications in Lung Imaging.Cureus · 2024Review
- Dynamic Chest Radiograph Simulation Technique with Deep Convolutional Neural Networks: A Proof-of-Concept Study.Cancers · 2023Article
- COVID-19 diagnostics: Molecular biology to nanomaterials.Clinica chimica acta; international journal of clinical chemistry · 2023Review
- EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2022Article
- Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images.Applied soft computing · 2022Article
- Computational Intelligence-Based Method for Automated Identification of COVID-19 and Pneumonia by Utilizing CXR Scans.Computational intelligence and neuroscience · 2022Article
- EGFR-specific single-chain variable fragment antibody-conjugated FeMagma (New York, N.Y.) · 2021Article
- A pilot study: Quantify lung volume and emphysema extent directly from two-dimensional scout images.Medical physics · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundChest X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) each have characteristic advantages and disadvantages that need to be considered in clinical decision-making. This point is discussed in reference to the main types of lung disease that are encountered in practice.
methodA selective literature search was performed in the PubMed and Google Scholar databases. Existing clinical guidelines on the main types of lung disease and studies concerning radiological diagnosis were also con - sidered in this review.
resultsThere have been no more than a few large-scale, controlled comparative trials of different radiological techniques. Chest X-ray provides general orientation as an initial diagnostic study and is especially useful in the diagnosis of pneumonia, cancer, and chronic obstructive pulmonary disease (COPD). Multi-detector CT affords nearly isotropic spatial resolution at a radiation dose of only 0.2-5 mSv, much lower than before. Its main indications, according to current guidelines, are tumors, acute pulmonary embolism, pulmonary hypertension, pulmonary fibrosis, advanced COPD, and pneumonia in a high-risk patient. MRI is used in the diagnosis of cystic fibrosis, pulmonary embolism, pulmonary hypertension, and bronchial carcinoma. The positive predictive value (PPV) of a chest X-ray in outpatients with pneumonia is only 27% (gold standard, CT); in contrast, an initial, non-randomized trial of MRI in nosocomial pneumonia revealed a PPV of 95%. For the staging of mediastinal lymph nodes in bronchial carcinoma, MRI has a PPV of 88% and positron emission tomography with CT (PET/CT) has a PPV of 79%, while CT alone has a PPV of 41% (gold standard, histology).
conclusionThe choice of radiologicalal technique for the detection, staging, follow-up, and quantification of lung disease should be based on the individual clinical options, so that appropriate treatment can be provided without excessive use of diagnostic testing.
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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.