A Risk Model for Lung Cancer Incidence

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Fältnamn	Indikatorer	Metadata
000		04482naa a2200769 4500
001		oai:DiVA.org:umu-54431
003		SwePub
008		120426s2012 \| \|\|\|\|\|\|\|\|\|\|\|000 \|\|eng\|
024	7	a https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-544312 URI
024	7	a https://doi.org/10.1158/1940-6207.CAPR-11-02372 DOI
040		a (SwePub)umu
041		a engb eng
042		9 SwePub
072	7	a ref2 swepub-contenttype
072	7	a art2 swepub-publicationtype
100	1	a Hoggart, Clive4 aut
245	1 0	a A Risk Model for Lung Cancer Incidence
264	1	a Philadelphia :b American Association for Cancer Research,c 2012
338		a print2 rdacarrier
520		a 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.
650	7	a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Cancer och onkologi0 (SwePub)302032 hsv//swe
650	7	a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Cancer and Oncology0 (SwePub)302032 hsv//eng
653		a Risk model
653		a risk factors
653		a lung cancer
653		a cigarette smoke
653		a cohort study
700	1	a Brennan, Paul4 aut
700	1	a Tjönneland, Anne4 aut
700	1	a Vogel, Ulla4 aut
700	1	a Overvad, Kim4 aut
700	1	a Nautrup Östergaard, Jane4 aut
700	1	a Kaaks, Rudolph4 aut
700	1	a Canzian, Federico4 aut
700	1	a Boeing, Heiner4 aut
700	1	a Steffen, Annika4 aut
700	1	a Trichopoulou, Antonia4 aut
700	1	a Bamia, Christina4 aut
700	1	a Trichopoulos, Dimitrios4 aut
700	1	a Johansson, Mattias4 aut
700	1	a Palli, Domenico4 aut
700	1	a Krogh, Vittorio4 aut
700	1	a Tumino, Rosario4 aut
700	1	a Sacerdote, Carlotta4 aut
700	1	a Panico, Salvatore4 aut
700	1	a Boshuizen, Hendriek C4 aut
700	1	a Bueno-de-Mesquita, H Bas4 aut
700	1	a Peeters, Petra H M4 aut
700	1	a Lund, Eiliv4 aut
700	1	a Gram, Inger Torhild4 aut
700	1	a Braaten, Tonje4 aut
700	1	a Rodríguez, Laudina4 aut
700	1	a Agudo, Antonio4 aut
700	1	a Sanchez-Cantalejo, Emilio4 aut
700	1	a Arriola, Larraitz4 aut
700	1	a Chirlaque, Maria-Dolores4 aut
700	1	a Barricarte, Aurelio4 aut
700	1	a Rasmuson, Torgnyu Umeå universitet,Onkologi4 aut0 (Swepub:umu)tora0001
700	1	a Khaw, Kay-Tee4 aut
700	1	a Wareham, Nicholas J4 aut
700	1	a Allen, Naomi E4 aut
700	1	a Riboli, Elio4 aut
700	1	a Vineis, Paolo4 aut
710	2	a Umeå universitetb Onkologi4 org
773	0	t Cancer Prevention Researchd Philadelphia : American Association for Cancer Researchg 5:6, s. 834-846q 5:6<834-846x 1940-6207x 1940-6215
856	4 8	u https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-54431
856	4 8	u https://doi.org/10.1158/1940-6207.CAPR-11-0237

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