Abstract
BACKGROUND: Existing targeted lung cancer screening programmes are either annual or biennial. We tested the hypothesis that existing risk models can inform personalised screening intervals to improve cost-effectiveness and reduce interval cancers. Spirometric airflow obstruction is a biomarker of lung cancer risk available in some cohorts, so we also explored whether this could add predictive value for stratification. METHODS: We tested the performance of risk models to predict next-round risk among baseline-negative participants in annual screening in Manchester and National Lung Screening Trial (NLST) cohorts. Models were adapted in the Eastern Cooperative Oncology Group and American College of Radiology Imaging Network (ECOG-ACRIN) sub-study of the NLST to investigate whether incorporating the spirometric percentage of predicted forced expiratory volume in 1 second (FEV(1) % pred) improved discrimination and screening efficiency at next-round screens. RESULTS: In Manchester, where a Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial model 2012 (PLCO(m2012)) score of >1.51% was used to determine eligibility, cancer was detected in 1.1% of those with a negative baseline scan after a 1-year interval (45 out of 4136), and in 2.5% of those with a risk of ≥6% (27 out of 1082). The same trend was seen in the NLST, but with lower risk overall. The PLCO(m2012) model discriminated next-round risk with an area under the curve (AUC) of 0.72 (95% CI 0.65-0.79), whereas the Lung Cancer Risk Assessment Tool + CT model for negative CT results (LCRAT+CT(neg)) was potentially superior, with an AUC of 0.75 (95% CI 0.68-0.82) on external validation. The addition of FEV(1) % pred as a predictor variable modestly improved discrimination (ΔAUC 0.01-0.04). DISCUSSION: Baseline risk could be used to extend intervals for substantial numbers of lower-risk participants, thereby reducing cost and screening harms, while focusing resources on those at highest risk to avoid delayed detection. Adding FEV(1) % pred is unlikely to meaningfully contribute to next-round risk prediction.