RT Journal Article
T1 Assessment of Lung Cancer Risk on the Basis of a Biomarker Panel of Circulating Proteins.
A1 Integrative Analysis of Lung Cancer Etiology and Risk (INTEGRAL) Consortium for Early Detection of Lung Cancer, 
A1 Guida, Florence
A1 Sun, Nan
A1 Bantis, Leonidas E
A1 Muller, David C
A1 Li, Peng
A1 Taguchi, Ayumu
A1 Dhillon, Dilsher
A1 Kundnani, Deepali L
A1 Patel, Nikul J
A1 Yan, Qingxiang
A1 Byrnes, Graham
A1 Moons, Karel G M
A1 Tjønneland, Anne
A1 Panico, Salvatore
A1 Agnoli, Claudia
A1 Vineis, Paolo
A1 Palli, Domenico
A1 Bueno-de-Mesquita, Bas
A1 Peeters, Petra H
A1 Agudo, Antonio
A1 Huerta, Jose M
A1 Dorronsoro, Miren
A1 Barranco, Miguel Rodriguez
A1 Ardanaz, Eva
A1 Travis, Ruth C
A1 Byrne, Karl Smith
A1 Boeing, Heiner
A1 Steffen, Annika
A1 Kaaks, Rudolf
A1 Hüsing, Anika
A1 Trichopoulou, Antonia
A1 Lagiou, Pagona
A1 La Vecchia, Carlo
A1 Severi, Gianluca
A1 Boutron-Ruault, Marie-Christine
A1 Sandanger, Torkjel M
A1 Weiderpass, Elisabete
A1 Nøst, Therese H
A1 Tsilidis, Kostas
A1 Riboli, Elio
A1 Grankvist, Kjell
A1 Johansson, Mikael
A1 Goodman, Gary E
A1 Feng, Ziding
A1 Brennan, Paul
A1 Johansson, Mattias
A1 Hanash, Samir M
AB There is an urgent need to improve lung cancer risk assessment because current screening criteria miss a large proportion of cases. To investigate whether a lung cancer risk prediction model based on a panel of selected circulating protein biomarkers can outperform a traditional risk prediction model and current US screening criteria. Prediagnostic samples from 108 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and samples from 216 smoking-matched controls from the Carotene and Retinol Efficacy Trial (CARET) cohort were used to develop a biomarker risk score based on 4 proteins (cancer antigen 125 [CA125], carcinoembryonic antigen [CEA], cytokeratin-19 fragment [CYFRA 21-1], and the precursor form of surfactant protein B [Pro-SFTPB]). The biomarker score was subsequently validated blindly using absolute risk estimates among 63 ever-smoking patients with lung cancer diagnosed within 1 year after blood collection and 90 matched controls from 2 large European population-based cohorts, the European Prospective Investigation into Cancer and Nutrition (EPIC) and the Northern Sweden Health and Disease Study (NSHDS). Model validity in discriminating between future lung cancer cases and controls. Discrimination estimates were weighted to reflect the background populations of EPIC and NSHDS validation studies (area under the receiver-operating characteristics curve [AUC], sensitivity, and specificity). In the validation study of 63 ever-smoking patients with lung cancer and 90 matched controls (mean [SD] age, 57.7 [8.7] years; 68.6% men) from EPIC and NSHDS, an integrated risk prediction model that combined smoking exposure with the biomarker score yielded an AUC of 0.83 (95% CI, 0.76-0.90) compared with 0.73 (95% CI, 0.64-0.82) for a model based on smoking exposure alone (P = .003 for difference in AUC). At an overall specificity of 0.83, based on the US Preventive Services Task Force screening criteria, the sensitivity of the integrated risk prediction (biomarker) model was 0.63 compared with 0.43 for the smoking model. Conversely, at an overall sensitivity of 0.42, based on the US Preventive Services Task Force screening criteria, the integrated risk prediction model yielded a specificity of 0.95 compared with 0.86 for the smoking model. This study provided a proof of principle in showing that a panel of circulating protein biomarkers may improve lung cancer risk assessment and may be used to define eligibility for computed tomography screening.
YR 2018
FD 2018-10-11
LK http://hdl.handle.net/10668/12706
UL http://hdl.handle.net/10668/12706
LA en
DS RISalud
RD Apr 7, 2025