Novel risk models based on screening history results and timing of lung cancer diagnosis: Post hoc analysis of the National Lung Cancer Screening Trial

BackgroundOur group previously reported that lung cancer (LC) screening history results and subsequent timing of diagnosis are associated with significant differences in survival outcomes. As a follow-up study, we sought to develop novel personalized risk models that considered screening history for incidence cancers, interval LCs, and prevalence LCs. MethodsUsing data from the CT-arm of the NLST, four independent case-control analyses were conducted to develop parsimonious risk models. Controls (n=26,038) were those never diagnosed with LC. The four LC case groups were 270 prevalence LCs, 44 interval LCs, 206 screen-detected LCs (SDLCs) that had a baseline positive screen, and 164 SDLCs that had a baseline negative screen. For each case-control analysis, univariable analyses identified statistically significant covariates from 48 variables and then significant covariates were included into a stepwise backward selection approach to identify a model with the most informative covariates. ResultsFor prevalence LCs, the model (AUC=0.711) included age, pack-years smoked, BMI, smoking status, smoking onset age, personal history of cancer, family history of LC, alcohol consumption, and milling occupation. For interval LCs, the model (AUC=0.734) included age, smoking status, smoking onset age, cigar smoking, marital status, and asbestos occupation. For baseline positive SDLCs, the model (AUC=0.685) included age, pack-years smoked, BMI, emphysema, chemicals/plastics exposure, and milling occupation. For baseline negative SDLCs, the model (AUC=0.701) included age, pack-years smoked, BMI, smoking status, emphysema, sarcoidosis, and sandblasting occupation. ConclusionsBesides smoking and age, which are inclusion criteria for screening, these models identified other important risk factors which could be used to provide personalized LC risk assessment and screening management.