ArticleTranslational lung cancer research2021
Performance of various risk prediction models in a large lung cancer screening cohort in Gdańsk, Poland-a comparative study.
Article in Translational lung cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 citing papers in PubMed, 2 syntheses or guidelines pooled it, 13 citations in OpenAlex.
- Use and impact of risk-based eligibility models in low-dose computed tomography lung cancer screening: a systematic review.Public health reviews · 2026Pooled it
- A systematic review and meta-analysis of lung cancer risk prediction models.Acta oncologica (Stockholm, Sweden) · 2025Pooled it
- Late-Stage Presentation and Diagnostic Gaps in Romanian Lung Cancer Patients: A Retrospective Tertiary Center Analysis with Implications for Scalable Screening.Diagnostics (Basel, Switzerland) · 2026Article
- Beyond the Scan: Adapting Multimodal Lung Cancer Screening for Central and Eastern Europe-Overcoming Systemic Barriers and Epidemiological Confounders.Medical sciences (Basel, Switzerland) · 2026Review
- From Lung Cancer Predictive Models toJournal of clinical medicine · 2026Review
- Head-to-head comparisons of risk discrimination by questionnaire-based lung cancer risk prediction models: a systematic review and meta-analysis.EClinicalMedicine · 2025Article
- Lung Cancer Detection Using Bayesian Networks: A Retrospective Development and Validation Study on a Danish Population of High-Risk Individuals.Cancer medicine · 2025Article
- Optimal Pathways to Lung Cancer Screening in Primary Care Settings: A Scoping Review.Current oncology (Toronto, Ont.) · 2024Article
- Risk Model-Based Lung Cancer Screening : A Cost-Effectiveness Analysis.Annals of internal medicine · 2023Article
- See Lung Cancer with an AI.Cancers · 2023Review
- Recalibrating Risk Prediction Models by Synthesizing Data Sources: Adapting the Lung Cancer PLCO Model for Taiwan.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2022Article
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOptimal selection criteria for the lung cancer screening programme remain a matter of an open debate. We performed a validation study of the three most promising lung cancer risk prediction models in a large lung cancer screening cohort of 6,631 individuals from a single European centre.
methodsA total of 6,631 healthy volunteers (aged 50-79, smoking history ≥30 pack-years) were enrolled in the MOLTEST BIS programme between 2016 and 2018. Each participant underwent a low-dose computed chest tomography scan, and selected participants underwent a further diagnostic work-up. Various lung cancer prediction models were applied to the recruited screenees, i.e., (I) Tammemagi's Prostate, Colorectal, and Ovarian Cancer Screening Trial 2012 (PLCO
resultsLung cancer was diagnosed in 154 (2.3%) participants. Based on the risk estimates by PLCO
conclusionsLung cancer screening enrollment based on the risk prediction models is superior to NCCN Group 1 selection criteria and offers a clinically significant reduction of screenees with a comparable proportion of detected lung cancer cases. Tammemagi's risk prediction model reduces the proportion of patients eligible for inclusion to a screening programme with a minimal loss of detected lung cancer cases.
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