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A Validated Clinical Risk Prediction Model for Lung Cancer in Smokers of All Ages and Exposure Types: A HUNT Study.
EBioMedicine. 2018 May; 31:36-46
Publication Type
Clinical Trial
Journal Article
Multicenter Study
Validation Studies
Maria Markaki
Ioannis Tsamardinos
Arnulf Langhammer
Vincenzo Lagani
Kristian Hveem
Oluf Dimitri Røe
Author Affiliation
University of Crete, Department of Computer Science, Voutes Campus, Heraklion, GR 70013, Greece.
EBioMedicine. 2018 May; 31:36-46
Publication Type
Clinical Trial
Journal Article
Multicenter Study
Validation Studies
Aged, 80 and over
Databases, Factual
Follow-Up Studies
Lung Neoplasms - epidemiology
Middle Aged
Models, Biological
Predictive value of tests
Prospective Studies
Risk factors
Smoking - adverse effects - epidemiology
Lung cancer causes >1·6 million deaths annually, with early diagnosis being paramount to effective treatment. Here we present a validated risk assessment model for lung cancer screening. The prospective HUNT2 population study in Norway examined 65,237 people aged >20years in 1995-97. After a median of 15·2years, 583 lung cancer cases had been diagnosed; 552 (94·7%) ever-smokers and 31 (5·3%) never-smokers. We performed multivariable analyses of 36 candidate risk predictors, using multiple imputation of missing data and backwards feature selection with Cox regression. The resulting model was validated in an independent Norwegian prospective dataset of 45,341 ever-smokers, in which 675 lung cancers had been diagnosed after a median follow-up of 11·6years. Our final HUNT Lung Cancer Model included age, pack-years, smoking intensity, years since smoking cessation, body mass index, daily cough, and hours of daily indoors exposure to smoke. External validation showed a 0·879 concordance index (95% CI [0·866-0·891]) with an area under the curve of 0·87 (95% CI [0·85-0·89]) within 6years. Only 22% of ever-smokers would need screening to identify 81·85% of all lung cancers within 6years. Our model of seven variables is simple, accurate, and useful for screening selection.
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PubMed ID
29678673 View in PubMed
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