Background
Lung cancer is often diagnosed in a late stage, and as a result, the 5-year survival rate for lung cancer is only 18%. If lung cancer is detected in an early stage, the prognosis is much better. Therefore, improving early detection is the most promising strategy to reduce lung cancer mortality.
Early stage lung cancer is typically diagnosed after the detection of an incidental lung nodule on CT imaging of the thorax that was ordered for other medical reasons. For this reason, accurate detection and characterization of incidentally detected lung nodules is of great importance.
Aim
In this project, we aim to develop artificial intelligence (AI) algorithms for opportunistic screening for lung cancer in routine CT images. We aim to develop fast and highly reliable detection and risk stratification algorithms for incidentally detected nodules in chest CT, and to validate a novel AI-assisted nodule workflow at two Dutch hospitals.
Funding
This project is funded by a Radboud Institute for Health Sciences Junior Researcher grant, with funding coming from Radboudumc and Jeroen Bosch Ziekenhuis in 's-Hertogenbosch.
Results
The results of this project are described in the PhD thesis of Ward Hendrix.
