Lung Cancer Screening: Simulations of Effects of Imperfect Detection on Temporal Dynamics

Abstract

To use a mathematic model to demonstrate effects of imperfect detection on temporal dynamics of radiologic lung cancer screening. Monte Carlo simulations of lung cancer screening programs were performed in subjects at high risk for developing cancer. The effects of detection probabilities, symptomatic presentation of tumors, tumor volume doubling time, and time between screenings were examined. Computed tomography (CT) and chest radiography models were used. For imperfect detection probabilities, the percentage of subjects with cancers detected with repeated screenings decreased to a steady-state value. The transition period was the period during which screenings were performed and detection rates decreased. At steady-state repeat screening, the proportion of subjects with cancers diagnosed at screening or by means of symptomatic presentation was determined by the annual probability of developing cancer and not by the sensitivity of the screening modality. The sensitivity of the screening technique did affect detected cancer size, number of interval cancers, and total number of cancers observed. CT was used to detect more total cancers over the course of the screening program and cancers with a smaller average size; moreover, fewer interval cancers were observed with CT screening than with chest radiography screening. Lung cancer screening with imperfect detection has a transition period between baseline screening and steady-state behavior of annual screenings. Advantages of CT screening include a decrease in the average cancer size at detection, a decrease in the number of observed interval cancers, and an increase in the total number of cancers observed. Steady-state behavior indicates that long-term trials of screening may not be necessary.