The Impact of Model Assumptions on Personalized Lung Cancer Screening Recommendations.
Kevin Ten HaafKoen de NijsGiulia SimoniAndres AlbanPianpian CaoZhuolu SunJean H E YongJihyoun JeonIakovos ToumazisSummer S HanG Scott GazelleChung Ying KongSylvia K PlevritisRafael MezaHarry J de KoningPublished in: Medical decision making : an international journal of the Society for Medical Decision Making (2024)
Natural-history models are increasingly used to inform lung cancer screening, but causes for variations between models are difficult to assess.This is the first evaluation of these causes and their impact on personalized screening recommendations through easily interpretable metrics.Models vary regarding sojourn times, stage-specific sensitivities, and screening-induced lung cancer mortality reductions.Model outcomes were similar in predicting greater screening benefits for women and potentially light smokers. Longer screening intervals may be feasible for women and light smokers.