Related Experiment Video
Updated: Apr 15, 2026
A Minimally Invasive Method for Generating a Syngeneic Orthotopic Mouse Model of Lung Cancer
Published on: August 19, 2025
A simple model for predicting lung cancer occurrence in a lung cancer screening program: The Pittsburgh Predictor
David O Wilson1, Joel Weissfeld2
1Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Pittsburgh, United States.
Background:
A user-friendly method for assessing lung cancer risk may help standardize selection of current and former smokers for screening. We evaluated a simple 4-factor model, the Pittsburgh Predictor, against two well-known, but more complicated models for predicting lung cancer risk.
Methods:
Trained against outcomes observed in the National Lung Screening Trial (NLST), the Pittsburgh Predictor used four risk factors, duration of smoking, smoking status, smoking intensity, and age, to predict 6-year lung cancer incidence. After calibrating the Bach and PLCOM2012 models to outcomes observed in the low-dose computed tomography arm of the NLST, we compared model calibration, discrimination, and clinical usefulness (net benefit) in the NLST and Pittsburgh Lung Screening Study (PLuSS) populations.
Results:
The Pittsburgh Predictor, Bach, and PLCOM2012 represented risk equally well, except for the tendency of PLCOM2012 to overestimate risk in subjects at highest risk. Relative to the Pittsburgh Predictor, Bach and PLCOM2012 increased the area under the receiver operator characteristic curve by 0.007-0.009 and 0.012-0.021 units, respectively, depending on study population. Across a clinically relevant span of 6-year lung cancer risk thresholds (0.01-0.05), Bach and PLCOM2012 increased net benefit by less than 0.1% in NLST and 0.3% in PLuSS.
Conclusion:
In exchange for a small reduction in prediction accuracy, a simpler lung cancer risk prediction model may facilitate standardized procedures for advising and selecting patients with respect to lung cancer screening.
