Trial reportBMJ open respiratory research2019
'Reduced' HUNT model outperforms NLST and NELSON study criteria in predicting lung cancer in the Danish screening trial.
Trial report in BMJ open respiratory research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 1 synthesis or guideline 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
- Differentiating benign and malignant solitary pulmonary nodules through exhaled breath analysis.ERJ open research · 2026Article
- Assessment and recalibration of seventeen lung cancer risk prediction models in approximately one million Chinese population utilising healthcare big data: a retrospective cohort analysis.The Lancet regional health. Western Pacific · 2025Article
- Evaluation of risk prediction models to select lung cancer screening participants in Europe: a prospective cohort consortium analysis.The Lancet. Digital health · 2024Article
- The HUNT lung-SNP model: genetic variants plus clinical variables improve lung cancer risk assessment over clinical models.Journal of cancer research and clinical oncology · 2024Article
- Improving Lung Cancer Screening Selection: The HUNT Lung Cancer Risk Model for Ever-Smokers Versus the NELSON and 2021 United States Preventive Services Task Force Criteria in the Cohort of Norway: A Population-Based Prospective Study.JTO clinical and research reports · 2024Article
- The promises and challenges of early non-small cell lung cancer detection: patient perceptions, low-dose CT screening, bronchoscopy and biomarkers.Molecular oncology · 2021Review
- Potential Disparities by Sex and Race or Ethnicity in Lung Cancer Screening Eligibility Rates.Chest · 2021Observational
- Democratic and ethical problem of lung cancer screening: exclusion of true high-risk populations. Can it be fixed? Yes.BMJ open respiratory research · 2020Review
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hypothesis: We hypothesise that the validated HUNT Lung Cancer Risk Model would perform better than the NLST (USA) and the NELSON (Dutch-Belgian) criteria in the Danish Lung Cancer Screening Trial (DLCST). Methods: The DLCST measured only five out of the seven variables included in validated HUNT Lung Cancer Model. Therefore a 'Reduced' model was retrained in the Norwegian HUNT2-cohort using the same statistical methodology as in the original HUNT model but based only on age, pack years, smoking intensity, quit time and body mass index (BMI), adjusted for sex. The model was applied on the DLCST-cohort and contrasted against the NLST and NELSON criteria. Results: Among the 4051 smokers in the DLCST with 10 years follow-up, median age was 57.6, BMI 24.75, pack years 33.8, cigarettes per day 20 and most were current smokers. For the same number of individuals selected for screening, the performance of the 'Reduced' HUNT was increased in all metrics compared with both the NLST and the NELSON criteria. In addition, to achieve the same sensitivity, one would need to screen fewer people by the 'Reduced' HUNT model versus using either the NLST or the NELSON criteria (709 vs 918, p=1.02e-11 and 1317 vs 1668, p=2.2e-16, respectively). Conclusions: The 'Reduced' HUNT model is superior in predicting lung cancer to both the NLST and NELSON criteria in a cost-effective way. This study supports the use of the HUNT Lung Cancer Model for selection based on risk ranking rather than age, pack year and quit time cut-off values. When we know how to rank personal risk, it will be up to the medical community and lawmakers to decide which risk threshold will be set for screening.
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