Lung cancer is by far the most deadly cancer in men and women. Contrary to other cancers, by the time a patient develops symptoms of lung cancer, the tumor is typically already several centimeters in size and in an advanced stage. As a result, treatment options are limited and prognosis is poor. With computed tomography (CT) scans, it is possible to detect lung cancer early, when the tumor is much smaller and curative treatment is still possible. The positive outcome in 2011 of the National Lung Screening Trial is the first clear scientific evidence that screening with low dose CT substantially reduces lung cancer mortality and even overall mortality. Major organizations have therefore updated their guidelines and now recommend screening.
In CT lung cancer screening high risk individuals, for example heavy (former) smokers between 50 and 75 years old, receive low dose CT scans, for example every year. But if radiologists would have to read all these 3D scans to locate pulmonary nodules, this would put a massive burden on our health care system. A conservative estimate would have 20% of all radiologists in The Netherlands reading chest CT scans full time. Clearly this is not feasible.
The goal of this project that will run through 2018, with funding by NWO and other sources, is therefore to automate lung CT screening as much as possible. Ultimately, computers will be superior to humans in analyzing three-dimensional high resolution images of the lungs. We already have extensive experience with automated analysis of chest CT scans and we are closely collaborating with researchers of the Nelson screening trial, the largest European lung cancer CT trial that started in 2004. We have developed algorithms for the automated detection of pulmonary nodules, for segmenting them to assess precisely how large they are and how fast they are growing, and for a variety of other tasks that need to be performed automatically if we want to reach the goal of the project.
We are looking for excellent and enthusiastic researchers to join our team and work on various aspects of this project:
Nijmegen is the oldest Dutch city with a rich history and one of the liveliest city centers in the Netherlands. Radboud University has over 17,000 students. Radboud University Nijmegen Medical Centre (RUNMC) is a leading academic center for medical science, education and health care with over 8,500 staff and 3,000 students.
The project will be carried out in the Diagnostic Image Analysis Group (DIAG) at the department of Radiology. DIAG investigates and develops automated image analysis methods to support clinicians in a broad range of medical imaging applications. Research topics include image analysis, image segmentation, machine learning, and the design of decision support systems. Application areas include lung, breast, prostate, and retinal imaging. Currently the group consists of around 35 researchers, including some 20 PhD students (http://www.diagnijmegen.nl).
The chest CT team currently consists of four PhD students, three with a technical background and one medical doctor, three senior researchers including a world-class thoracic radiologist, and three affiliated researchers from Fraunhofer MEVIS.
You will be appointed as a PhD student at the Radboud University Medical Center Nijmegen, department of Radiology, with the standard salary and secondary conditions for PhD students in the Netherlands. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by 3 years and the research should result in a PhD thesis.
You will be appointed as a postdoctoral researcher at the Radboud University Medical Center Nijmegen, department of Radiology, with salary and secondary conditions depending on your experience. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by 2 to 4 years. Part of your tasks is co-supervision of PhD students. For excellent postdoctoral researchers with the capacity and ambition to establish their own research group a tenure track system is in place at the Department of Radiology.
For further information please contact Bram van Ginneken.
Send applications as a single pdf file to firstname.lastname@example.org. In this pdf file the following should be included: CV, for prospective PhD students a list of followed courses and grades, a letter of motivation, and a reprint or, preferably, links to your Master or PhD thesis or any publications in English you have written.