ArticleTranslational lung cancer research2020
Role of chest radiographs in early lung cancer detection.
Article in Translational lung cancer research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
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- Downsampling attention fusion network (DAFNet): a You Only Look Once network for lung nodule detection.Quantitative imaging in medicine and surgery · 2026Article
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- Programmes for pathology: improving health outcomes for low- and middle-income countries in the artificial intelligence paradigm.International health · 2025Article
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- Improving decomposition image quality in dual-energy chest radiography using two-dimensional crisscrossed anti-scatter grid.Medical physics · 2025Article
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- Ensemble learning driven Kolmogorov-Arnold Networks-based Lung Cancer classification.PloS one · 2024Article
- Carboxypeptidase N2 as a Novel Diagnostic and Prognostic Biomarker for Lung Adenocarcinoma.Frontiers in oncology · 2022Article
- Has COVID-19 Affected Cancer Screening Programs? A Systematic Review.Frontiers in oncology · 2021Review
- Diagnostic performance of narrow-band imaging bronchoscopy in assessing tracheobronchial cell morphological abnormalities.Annals of thoracic medicineArticle
- Using Convoluted Neural Networks in Diagnosing Lung Cancer on Computed Tomography Scans.Current health sciences journalArticle
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is the second most common and the most fatal form of cancer. Although annual low-dose computed tomography is used as the primary method of cancer screening, it presents challenges regarding resources as well as potential health risks from radiation exposure. Chest radiography (CXR), though less effective, is used frequently and commonly. Moreover, often in clinical settings, CXR is the first imaging modality used; computed tomography is subsequently performed if abnormalities are detected on CXRs. This study examined whether controlling for distractors and time constraints, as well as side-by-side comparison of multiple CXRs in clinical settings can aid earlier detection of radiological abnormalities indicative of lung cancer lesions.
methodsThirty-two attending physicians in the Republic of Korea examined 1,750 radiographs of 50 lung cancer cases. Using "hot spot" technology, participants indicated the possible locations of cancer lesions on each radiograph. Subsequently, the same radiographs, cropped to focus the anatomical regions where lung cancers were diagnosed, were shown side-by-side to the participants. The participants were asked to identify the radiograph which first enabled the diagnosis of lung cancer and which first showed a possible lesion.
resultsRemoval of systemic constraints alone significantly improved lesion identification by 221.72±9.69 days. Presenting radiographs side-by-side, cropped to relevant areas, had an additional significant and positive impact on cancer detection in both hidden and open areas on CXRs. Also, lesions were detected at smaller sizes and earlier than when actually diagnosed.
conclusionsCXR with improved methods and settings provides an easily accessible and low-risk imaging method for earlier detection of lung cancer compared to current clinical imaging settings. Further, this study demonstrates the potential effectiveness of programs that allow side-by-side comparisons of cropped areas of multiple radiographs to detect radiological abnormalities.
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