@misc{10481/82947,
year = {2023},
month = {6},
url = {https://hdl.handle.net/10481/82947},
abstract = {Background
We sought to develop a proteomics-based risk model for lung cancer and evaluate its risk-discriminatory performance in comparison with a smoking-based risk model (PLCOm2012) and a commercially available autoantibody biomarker test.

Methods
We designed a case-control study nested in 6 prospective cohorts, including 624 lung cancer participants who donated blood samples at most 3 years prior to lung cancer diagnosis and 624 smoking-matched cancer free participants who were assayed for 302 proteins. We used 470 case-control pairs from 4 cohorts to select proteins and train a protein-based risk model. We subsequently used 154 case-control pairs from 2 cohorts to compare the risk-discriminatory performance of the protein-based model with that of the Early Cancer Detection Test (EarlyCDT)-Lung and the PLCOm2012 model using receiver operating characteristics analysis and by estimating models’ sensitivity. All tests were 2-sided.

Results
The area under the curve for the protein-based risk model in the validation sample was 0.75 (95% confidence interval [CI] = 0.70 to 0.81) compared with 0.64 (95% CI = 0.57 to 0.70) for the PLCOm2012 model (Pdifference = .001). The EarlyCDT-Lung had a sensitivity of 14% (95% CI = 8.2% to 19%) and a specificity of 86% (95% CI = 81% to 92%) for incident lung cancer. At the same specificity of 86%, the sensitivity for the protein-based risk model was estimated at 49% (95% CI = 41% to 57%) and 30% (95% CI = 23% to 37%) for the PLCOm2012 model.

Conclusion
Circulating proteins showed promise in predicting incident lung cancer and outperformed a standard risk prediction model and the commercialized EarlyCDT-Lung.},
organization = {US NCI (INTEGRAL program U19
CA203654},
organization = {R03 CA245979), l’Institut National Du Cancer
(2019-1-TABAC-01},
organization = {Foundation of Northern Sweden (AMP19-962), an early detection
of cancer development grant},
organization = {Swedish Department of
Health ministry, and Cancer Research UK [C18281/A29019]. RJH
is supported by the Canada Research Chair of the Canadian
Institute of Health Research.},
publisher = {Oxford University Press},
title = {Lung cancer risk discrimination of prediagnostic proteomics measurements compared with existing prediction tools},
doi = {10.1093/jnci/djad071},
author = {Feng, Xiaoshuang and Sánchez Pérez, María José},
}