ArticleEuropean radiology2017
Malignancy risk estimation of screen-detected nodules at baseline CT: comparison of the PanCan model, Lung-RADS and NCCN guidelines.
Article in European radiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Insights for Management of Ground-Glass Opacities From the National Lung Screening Trial.Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2019Trial
- Radiologist review at the time of lung nodule referrals saves clinic appointments and fast tracks investigations: results of the outpatient respiratory nodule management (Ornament) study.Internal medicine journal · 2026Article
- Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model.Cancers · 2025Article
- Article
- New developments in the imaging of lung cancer.Breathe (Sheffield, England) · 2024Review
- Clinical effect of Almonertinib in treating epidermal growth factor receptor mutation-positive residual ground-glass opacities after stage I lung cancer resection.American journal of translational research · 2024Article
- Clinical Adjuncts to Lung Cancer Screening: A Narrative Review.Thoracic surgery clinics · 2023Review
- Computed tomography-guided biopsy for sub-centimetre pulmonary nodules: a meta-analysis.Kardiochirurgia i torakochirurgia polska = Polish journal of cardio-thoracic surgery · 2023Article
- Developing an understanding of artificial intelligence lung nodule risk prediction using insights from the Brock model.European radiology · 2022Article
- Ultrashort echo time MRI of the lung in children and adolescents: comparison with non-enhanced computed tomography and standard post-contrast T1w MRI sequences.European radiology · 2022Article
- Contemporary issues in the implementation of lung cancer screening.European respiratory review : an official journal of the European Respiratory Society · 2021Review
- A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification.The British journal of radiology · 2021Article
- External validation and comparison of the Brock model and Lung-RADS for the baseline lung cancer CT screening using data from the Korean Lung Cancer Screening Project.European radiology · 2021Article
- Artificial intelligence for detection and characterization of pulmonary nodules in lung cancer CT screening: ready for practice?Translational lung cancer research · 2021Review
- Screen-detected solid nodules: from detection of nodule to structured reporting.Translational lung cancer research · 2021Review
- Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML).Diagnostics (Basel, Switzerland) · 2021Review
- Validation of the BRODERS classifier (BenignThe European respiratory journal · 2021Article
- Observational
- 2020 Innovation-Based Optimism for Lung Cancer Outcomes.The oncologist · 2021Review
- Implications of the updated Lung CT Screening Reporting and Data System (Lung-RADS version 1.1) for lung cancer screening.Journal of thoracic disease · 2020Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo compare the PanCan model, Lung-RADS and the 1.2016 National Comprehensive Cancer Network (NCCN) guidelines for discriminating malignant from benign pulmonary nodules on baseline screening CT scans and the impact diameter measurement methods have on performances.
methodsFrom the Danish Lung Cancer Screening Trial database, 64 CTs with malignant nodules and 549 baseline CTs with benign nodules were included. Performance of the systems was evaluated applying the system's original diameter definitions: D
resultsPanCan performed superiorly to Lung-RADS and NCCN (AUC 0.874 vs. 0.813, p = 0.003; 0.874 vs. 0.836, p = 0.010), using the original diameter specifications. When uniformly applying D
conclusionsWithout follow-up information, the PanCan model performs significantly superiorly to Lung-RADS and the 1.2016 NCCN guidelines for discriminating benign from malignant nodules. The NCCN guidelines are most sensitive to nodule size definition. KEY POINTS: • PanCan model outperforms Lung-RADS and 1.2016 NCCN guidelines in identifying malignant pulmonary nodules. • Nodule size definition had no significant impact on Lung-RADS and PanCan model. • 1.2016 NCCN guidelines were significantly superior when using mean diameter to longest diameter. • Longest diameter achieved lowest performance for all models. • Mean diameter performed equivalently when derived from axial sections and from volumetry.
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