The potential for using risk models in future lung cancer screening trials

Abstract

Computed tomography screening for early diagnosis of lung cancer is one of the more potentially useful strategies, aside from smoking cessation programmes, for reducing mortality and improving the current poor survival from this disease. The long-term success of lung cancer screening will be dependent upon identifying populations at sufficient risk in order to maximise the benefit-to-harm ratio of the intervention. Risk prediction models could potentially play a major role in the selection of high-risk individuals who would benefit most from screening intervention programmes for the early detection of lung cancer. Improvements of developed lung cancer risk prediction models (through incorporation of objective clinical factors and genetic and molecular biomarkers for precise and accurate estimation of risks), demonstration of their clinical usefulness in decision making, and their use in future screening programmes are the focus of current research.

Figure 1.. Lung cancer risk model decision cascade

Mirror images of shared aspects of the cascades of lung cancer risk assessment (left) and early detection (right) are shown. These cascades move from clinical and epidemiological assessments to molecular epidemiological assessments to biomarker assessments in non-lung samples and finally to biomarker assessments in lung samples. They merge in the middle with the identification of the highest-risk individuals who need computed tomography (CT) screening. Individuals with a positive CT scan will be entered into clinical workup and treatment protocols. Those with a negative CT scan are clearly at a high risk and should be considered for prevention research studies. SNP, single-nucleotide polymorphism. Image originally published in Field, Cancer Prev Res 2008 [16]. Copyright © 2008 American Association of Cancer Research.