A Risk Model for Lung Cancer Incidence

• Risk models for lung cancer incidence would be useful for prioritising individuals for screening and participation in clinical trials of chemoprevention. We present a risk model for lung cancer built using prospective cohort data from a general population which predicts individual incidence in a given time period.We build separate risk models for current and former smokers utilising 169,035 ever smokers from the multicentre European Prospective Investigation into Cancer and Nutrition (EPIC) and considered a model for never smokers. The data set was split into independent training and test sets. Lung cancer incidence was modelled using survival analysis, stratifying by age started smoking, and for former smokers, also smoking duration. Other risk factors considered were smoking intensity, ten occupational/environmental exposures previously implicated with lung cancer, and SNPs at two loci identified by genome-wide association studies of lung cancer. Individual risk in the test set was measured by the predicted probability of lung cancer incidence in the year preceding last follow-up time, predictive accuracy was measured by the area under the receiver operator characteristic curve (AUC).Utilising smoking information alone gave good predictive accuracy: the AUC and 95% confidence interval in ever smokers was 0.843 (0.810, 0.875), the Bach model applied to the same data gave an AUC of 0.775 (0.737, 0.813). Other risk factors had negligible effect on the AUC, including never smokers for whom prediction was poor.Our model is generalisable and straightforward to implement. Its accuracy can be attributed to its modelling of lifetime exposure to smoking.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

Nyckelord

Risk model

risk factors

lung cancer

cigarette smoke

cohort study

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