ArticleRadiology2018
Radiologic Features of Small Pulmonary Nodules and Lung Cancer Risk in the National Lung Screening Trial: A Nested Case-Control Study.
Article in Radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.
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Who cites it
53 citing papers in PubMed, 2 syntheses or guidelines pooled it, 85 citations in OpenAlex.
- Potential clinical utility of liquid biopsy in early-stage non-small cell lung cancer.BMC medicine · 2022Pooled it
- Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy.Seminars in cancer biology · 2022Pooled it
- Missed Incidental Lung Cancer on Neck CT: Determinants and Impact on Diagnostic Delay and Stage Shift.Korean journal of radiology · 2026Article
- Emphysema or Bronchiectasis with Pulmonary Nodules: Impact on The Risk of Malignancy.Thoracic research and practice · 2026Article
- Innovative approaches for lung cancer screening and interception.Nature reviews. Clinical oncology · 2026Review
- Exploring the key clinical and computed tomography features for distinguishing high-grade lung cancers from morphologically similar benign tumors: a two-center case-control study.BMC medical imaging · 2026Article
- Using AI system to detect active tuberculosis in a high-prevalence setting on CT scans: a multi-center study.Scientific reports · 2025Article
- Longitudinal variability of CT imaging features for predicting pulmonary nodule invasiveness: A multicenter study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2025Article
- Article
- Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.Journal of cancer research and clinical oncology · 2025Article
- Multi-omics model is an effective means to diagnose benign and malignant pulmonary nodules.Clinics (Sao Paulo, Brazil) · 2025Article
- A clinical-information-free method for early diagnosis of lung cancer from the patients with pulmonary nodules based on backpropagation neural network model.Computational and structural biotechnology journal · 2024Article
- Improving lung nodule segmentation in thoracic CT scans through the ensemble of 3D U-Net models.International journal of computer assisted radiology and surgery · 2024Article
- A prediction model based on computed tomography characteristics for identifying malignant from benign sub-centimeter solid pulmonary nodules.Journal of thoracic disease · 2024Article
- Association of Lung Cancer Risk With the Presence of Both Lung Nodules and Emphysema in a Lung Cancer Screening Trial.World journal of oncology · 2024Article
- A Synthesizing Semantic Characteristics Lung Nodules Classification Method Based on 3D Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- A CT-based radiomics integrated model for discriminating pulmonary cryptococcosis granuloma from lung adenocarcinoma-a diagnostic test.Translational lung cancer research · 2023Article
- Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules.European radiology · 2023Article
- Article
- A triple-classification for the evaluation of lung nodules manifesting as pure ground-glass sign: a CT-based radiomic analysis.BMC medical imaging · 2022Article
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Authors and funding
11 authors at 1 institution in 2 countries.
Funding
Abstract
Purpose To extract radiologic features from small pulmonary nodules (SPNs) that did not meet the original criteria for a positive screening test and identify features associated with lung cancer risk by using data and images from the National Lung Screening Trial (NLST). Materials and Methods Radiologic features in SPNs in baseline low-dose computed tomography (CT) screening studies that did not meet NLST criteria to be considered a positive screening examination were extracted. SPNs were identified for 73 incident case patients who were given a diagnosis of lung cancer at either the first or second follow-up screening study and for 157 control subjects who had undergone three consecutive negative screening studies. Multivariable logistic regression was used to assess the association between radiologic features and lung cancer risk. All statistical tests were two sided. Results Nine features were significantly different between case patients and control subjects. Backward elimination followed by bootstrap resampling identified a reduced model of highly informative radiologic features with an area under the receiver operating characteristic curve of 0.932 (95% confidence interval [CI]: 0.88, 0.96), a specificity of 92.38% (95% CI: 52.22%, 84.91%), and a sensitivity of 76.55% (95% CI: 87.50%, 95.35%) that included total emphysema score (odds ratio [OR] = 1.71; 95% CI: 1.39, 2.01), attachment to vessel (OR = 2.41; 95% CI: 0.99, 5.81), nodule location (OR = 3.25; 95% CI: 1.09, 8.55), border definition (OR = 7.56; 95% CI: 1.89, 30.8), and concavity (OR = 2.58; 95% CI: 0.89, 5.64). Conclusion A set of clinically relevant radiologic features were identified that that can be easily scored in the clinical setting and may be of use to determine lung cancer risk among participants with SPNs.
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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.