Screening for lung cancer using low dose computed tomography

Abstract

Screening for lung cancer with low dose computed tomography can reduce mortality from the disease by 20% in high risk smokers. This review covers the state of the art knowledge on several aspects of implementing a screening program. The most important are to identify people who are at high enough risk to warrant screening and the appropriate management of lung nodules found at screening. An accurate risk prediction model is more efficient than age and pack years of smoking alone at identifying those who will develop lung cancer and die from the disease. Algorithms are available for assessing people who screen positive to determine who needs additional imaging or invasive investigations. Concerns about low dose computed tomography screening include false positive results, overdiagnosis, radiation exposure, and costs. Further work is needed to define the frequency and duration of screening and to refine risk prediction models so that they can be used to assess the risk of lung cancer in special populations. Another important area is the use of computer vision software tools to facilitate high throughput interpretation of low dose computed tomography images so that costs can be reduced and the consistency of scan interpretation can be improved. Sufficient data are available to support the implementation of screening programs at the population level in stages that can be expanded when found to perform well to improve the outcome of patients with lung cancer.