Year: 2021
Type: conference-abstract
Abstract: RATIONALE: Current recommendations for lung cancer screening are based on categorical age, pack years, and smoking quit-time.However, lung cancer risk prediction models may help select the highest risk individuals for lung cancer screening.Adding biomarkers of tobacco exposure into such models may improve their predictive ability by incorporating a biological measure of smoking that integrates aspects of tobacco uptake and metabolism not captured by historical measures.METHODS: In a case-cohort study of 72 lung cancer cases and 115 controls drawn from current smokers in the screening arm of the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO), we used baseline measures of serum cotinine ,3,2,3,, and a risk score combining all variables from the validated PLCO m2012 model to develop a logistic regression model estimating six year risk of lung cancer diagnosis.Internal validation of the model was performed with cross validation.Performance metrics included receiver operating characteristic (ROC) curve and area under the curve (AUC).RESULTS: Smoking pack years and tobacco biomarker levels were higher in lung cancer cases than in controls (mean pack years: cases 66.5; controls 57.2).In the logistic regression model, the PLCO m2012 score was a significant predictor of lung cancer (OR: 1.92; 95% CI: 1.23 to 3.07; p-value: 0.005) but cotinine (OR: 1.01; 95% CI: 0.61 to 1.73; p-value: 0.964), PheT (OR: 1.28; 95% CI: 0.91 to 1.83; p-value: 0.164), and NNAL (OR: 1.20; 95% CI: 0.66 to 2.20; p-value: 0.547) were not.There was poor discrimination between lung cancer cases and controls using the PLCO m2012 model (AUC: 0.65; 95% CI 0.57 to 0.72).The addition of tobacco biomarkers did not improve the performance of the model (AUC: 0.65; 95% CI 0.56 to 0.73).CONCLUSIONS: Tobacco biomarker levels were increased in PLCO participants who developed lung cancer within six years of randomization compared to controls, but were not significantly associated with increased lung cancer risk.Addition of tobacco biomarker measures did not improve the prediction of lung cancer risk compared to the original PLCO m2012 model.Heavy smoking within both case and control groups may have limited the biomarker effect.Further studies with larger sample sizes and additional tobacco smoke related biomarkers are necessary to achieve a predictive model for lung cancer.
Authors: C. Lambert, M. Tammemagi, S. Hecht, S. Murphy, K. Tessier, A. Petersen, A. Joseph
Institutions: University of Minnesota, Brock University, Masonic Cancer Center
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4246726997