To be able to detect lung cancer in an early stage, screening of high-risk subjects using low-dose CT has been proposed. In 2011, the National Lung Screening Trial (NLST) was the first multicenter randomized controlled trial (RCT) to demonstrate that three rounds of annual screening of a high-risk population using low-dose chest computed tomography (CT) lead to 20% fewer lung cancer deaths after seven years of follow-up, compared to annual screening with chest radiography. Over 53,000 participants were included in this landmark study. The Dutch-Belgian NELSON trial – the second largest RCT with 15,789 participants – recently published their results and showed a 24% mortality reduction in a high-risk population of men compared to no screening. Based on the results of these trials, several countries have started the implementation of lung cancer screening, and other countries are conducting pilot trials.
AI holds great potential to assist in many of the detection and characterization tasks that have to be performed by a radiologist, and may be able to play an important role in reducing costs and improving the efficiency of screening.
In this project, we aim to develop algorithms that will improve the accuracy and cost-effectiveness of lung cancer screening. Much of our work is about extending and improving our current nodule CAD algorithms and automating lung cancer screening. Next to that, we alos have a strong focus on performing clinical research in this area. To help us achieve our goals, we have developed a high-throughput workstation for lung cancer screening (see more info below), which incorporates many of the algorithms we develop. This project is done in close collaboration with MeVis Medical Solutions AG. This project has led to the release of Veolity, an optimized workstation solution for lung cancer screening.