Trial reportEuropean radiology2015
Predictive Accuracy of the PanCan Lung Cancer Risk Prediction Model -External Validation based on CT from the Danish Lung Cancer Screening Trial.
Trial report in European radiology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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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, 1 synthesis or guideline pooled it.
- Impact of low-dose computed tomography (LDCT) screening on lung cancer-related mortality.The Cochrane database of systematic reviews · 2022Pooled it
- A nomogram based on metabolic profiling to discriminate lung cancer among patients with lung nodules.The Journal of international medical research · 2023Trial
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Rationale for the Nodule Management Protocol of the Australian National Lung Cancer Screening Program.Journal of medical imaging and radiation oncology · 2026Review
- Performance of a screening-trained DL model for pulmonary nodule malignancy estimation of incidental clinical nodules.European radiology · 2026Article
- Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups.European radiology · 2025Article
- Lung Cancer Risk Prediction in Patients with Persistent Pulmonary Nodules Using the Brock Model and Sybil Model.Cancers · 2025Article
- Validation of a commercially available CAD-system for lung nodule detection and characterization using CT-scans.European radiology · 2025Article
- Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives.Cancer metastasis reviews · 2025Review
- Screening for lung cancer with computed tomography: protocol for systematic reviews for the Canadian Task Force on Preventive Health Care.Systematic reviews · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Current investigative modalities for detecting and staging lung cancers: a comprehensive summary.Indian journal of thoracic and cardiovascular surgery · 2023Review
- Risk factors for early-onset lung cancer in Korea: analysis of a nationally representative population-based cohort.Epidemiology and health · 2023Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
- A new classifier constructed with platelet features for malignant and benign pulmonary nodules based on prospective real-world data.Journal of Cancer · 2022Article
- Contemporary issues in the implementation of lung cancer screening.European respiratory review : an official journal of the European Respiratory Society · 2021Review
- 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
- Perifissural nodules: ready for application into lung cancer CT screening?Annals of translational medicine · 2020Article
- Typical CT Features of Intrapulmonary Lymph Nodes: A Review.Radiology. Cardiothoracic imaging · 2020Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesLung cancer risk models should be externally validated to test generalizability and clinical usefulness. The Danish Lung Cancer Screening Trial (DLCST) is a population-based prospective cohort study, used to assess the discriminative performances of the PanCan models.
methodsFrom the DLCST database, 1,152 nodules from 718 participants were included. Parsimonious and full PanCan risk prediction models were applied to DLCST data, and also coefficients of the model were recalculated using DLCST data. Receiver operating characteristics (ROC) curves and area under the curve (AUC) were used to evaluate risk discrimination.
resultsAUCs of 0.826-0.870 were found for DLCST data based on PanCan risk prediction models. In the DLCST, age and family history were significant predictors (p = 0.001 and p = 0.013). Female sex was not confirmed to be associated with higher risk of lung cancer; in fact opposing effects of sex were observed in the two cohorts. Thus, female sex appeared to lower the risk (p = 0.047 and p = 0.040) in the DLCST.
conclusionsHigh risk discrimination was validated in the DLCST cohort, mainly determined by nodule size. Age and family history of lung cancer were significant predictors and could be included in the parsimonious model. Sex appears to be a less useful predictor. KEY POINTS: • High accuracy in logistic modelling for lung cancer risk stratification of nodules. • Lung cancer risk prediction is primarily based on size of pulmonary nodules. • Nodule spiculation, age and family history of lung cancer are significant predictors. • Sex does not appear to be a useful risk predictor.
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