A risk model for lung cancer incidence

Clive Hoggart, Paul Brennan, Anne Tjonneland, Ulla Vogel, Kim Overvad, Jane Nautrup Østergaard, Rudolf Kaaks, Federico Canzian, Heiner Boeing, Annika Steffen, Antonia Trichopoulou, Christina Bamia, Dimitrios Trichopoulos, Mattias Johansson, Domenico Palli, Vittorio Krogh, Rosario Tumino, Carlotta Sacerdote, Salvatore Panico, Hendriek BoshuizenH. Bas Bueno-de-Mesquita, Petra H M Peeters, Eiliv Lund, Inger Torhild Gram, Tonje Braaten, Laudina Rodríguez, Antonio Agudo, Emilio Sánchez-Cantalejo, Larraitz Arriola, Maria Dolores Chirlaque, Aurelio Barricarte, Torgny Rasmuson, Kay Tee Khaw, Nicholas Wareham, Naomi E. Allen, Elio Riboli, Paolo Vineis

Research output: Contribution to journalArticlepeer-review

Abstract

Risk models for lung cancer incidence would be useful for prioritizing 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 using 169,035 ever smokers from the multicenter 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 modeled using survival analysis, stratifying by age started smoking, and for former smokers, also smoking duration. Other risk factors considered were smoking intensity, 10 occupational/environmental exposures previously implicated with lung cancer, and single-nucleotide polymorphisms 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). Using 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 forwhomprediction was poor. Our model is generalizable and straightforward to implement. Its accuracy can be attributed to its modeling of lifetime exposure to smoking.

Original languageEnglish
Pages (from-to)834-846
Number of pages13
JournalCancer Prevention Research
Volume5
Issue number6
DOIs
Publication statusPublished - Jun 2012

ASJC Scopus subject areas

  • Cancer Research
  • Oncology

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