Abstract IA17: Risk prediction modeling in lung cancer: How can we improve?

Screening for lung cancerThe results of the US National Lung Screening Trial (NLST) were published in 2011 and are considered a landmark event in lung cancer research. This randomised study of 53,454 individuals showed that computed tomography (CT) scans are able to reduce lung cancer mortality by 20% through early detection, although with important cost and morbidity due to overdiagnosis and treatment of benign nodule. A number of European pilot trials have reported, we await the NELSON, which is the only statistically powered screening trial in Europe. There are now discussions on how to implement lung cancer screening throughout the world, within differing health care systems. The 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 modelsThus accurate selection of high-risk individuals for lung cancer screening requires robust methods for risk prediction. The discriminative performance of a risk model depends not only on the identification of individual risk factors, but also on the influence of these risk variables in the presence/absence of other variables, how accurately these factors can be measured, and the appropriateness of the population and statistical techniques used for modeling. However, the main practical application of a risk prediction model is its use by non-specialists for selection of suitable high-risk people for lung cancer s...
Source: Cancer Epidemiology Biomarkers and Prevention - Category: Cancer & Oncology Authors: Tags: Improving Cancer Risk Prediction for Prevention and Early Detection: Oral Presentations - Invited Abstracts Source Type: research