For the project 'Using artificial intelligence for improving the efficiency of lung cancer screening' at the department of Medical Imaging at Radboud University Medical Center, Nijmegen (The Netherlands), we are seeking an enthusiastic PhD student with a clear interest in the potential that AI techniques may have in the field of radiology. This is an excellent opportunity to assess the value of this cutting-edge technology and investigate its potential to more personalized lung cancer screening.

We offer a fully-funded PhD position that focuses on improving the efficiency of lung cancer screening by using artificial intelligence (AI). This is a PhD position within a larger consortium project: NELSON-POP. In this consortium, the unique expertise and data from the various NELSON sites and associated research groups are combined to leverage various unexplored data sources, in order to identify the factors most predictive of lung cancer. Using multi-source data, the consortium aims to maximize lung cancer screening efficiency, by developing prediction models to

• Optimize screenee selection.
• Limit unnecessary nodule work-up.

This PhD position focuses on the use of existing AI algorithms for lung cancer screening. AI algorithms based on deep learning have great potential to perform more reproducible and more objective pattern recognition and thereby may increase the accuracy and consistency of malignancy probability estimation of pulmonary nodules. This increased accuracy can be used to develop optimized follow-up protocols, leading to less unnecessary follow-up CTs and unnecessary referrals in lung cancer screening.

Therefore, the aim of this PhD project is to accurately determine the probability of lung cancer of screen-detected pulmonary nodules using artificial intelligence in order to reduce the number of unnecessary repeat scans and unnecessary referrals, all contributing to reduction of radiation exposure, financial expenses, workload, invasive procedures, and screenee anxiety. You will use existing AI algorithms developed and validated by the Radboudumc group. You will validate the accuracy of the AI algorithms on the NELSON cohort and investigate how these can be used to develop novel optimized nodule management guidelines. The research should result in a PhD thesis.

Tasks and responsibilities
Within this project you will:

• Validate the predictive power of AI algorithms on the NELSON cohort.
• Compare the AI algorithms with a panel of expert radiologists and existing risk models for estimating lung cancer risk.
• Determine an optimized nodule management algorithm by including the AI risk score.
• Investigate how AI biomarkers can contribute to the final predictive model of the consortium, which also includes environmental factors, polygenic risk scores, and biomarkers for COPD, coronary artery disease, and emphysema to create a personalized risk of lung cancer.

You should be a creative and enthusiastic researcher with a MSc in a relevant field, such as Medicine, Technical medicine, Clinical epidemiology, or similar.

You have an interest in medical imaging, with a particular focus on oncologic (lung) imaging and artificial intelligence. You also have an interest in and preferably experience with scientific research.

Fixed-term contract: 4 years.

Starting date is preferably 1 December 2021. The project will appoint a PhD candidate for 4 years in total. Your performance will be evaluated after 1 year. If the evaluation is positive, the contract will be extended by another 3 years. The salary depends on education. Scale 10 will be offered to MSc medical graduates; those holding degrees in other disciplines will be offered scale 10A.

Working at Radboud university medical center means that you are ahead of the curve and working together on the healthcare of the future. And there is more. Our secondary terms of employment are impressive. These are fully tailored to you thanks to our Employment Conditions Selection Model. At Radboud university medical center, you will be given trust, and you will take the responsibility to handle everything together. We provide annual courses, both professional and personal.

• In addition to your monthly salary and an annual vacation allowance of 8%, you will receive an end-of-year bonus of 8.3%.
• If you work irregular hours, you will receive an allowance.
• As a full-time employee (36 hours per week), you are entitled to approximately 168 vacation hours (over 23 days) per year.
• Radboud university medical center pays 70% of the pension premium. You pay the rest of the premium with your gross salary.
• You get a discount on health insurance as well: you can take advantage of two group health insurance plans. UMC Zorgverzekering and CZ collectief.

In addition to our terms of employment, we also offer employees various other attractive facilities, such as childcare and sports facilities. Want to learn more? Take a look at the Cao UMC.

Radboudumc

The Diagnostic Image Analysis Group (DIAG) is a research group of the Department of Medical Imaging of the Radboud University Medical Center. We develop, validate and deploy novel AI algorithms in healthcare. This PhD position falls with the lung cancer image analysis research line led by Colin Jacobs. We actively collaborate with many international players in the field of lung cancer screening, and have a strong focus on integration of our AI algorithms into clinical practice.

You will work in close collaboration with a team of technical PhD candidates, research software engineers, and radiologists. Your daily supervision will be performed by Colin Jacobs. Your PhD supervision team will also involve supervisors from the other NELSON sites, to contribute to the synergy necessary to reach the goals of the consortium.

Read what it is like to do a PhD at the Radboud University Medical Center.

Radboudumc
Radboud university medical center is a university medical center for patient care, scientific research, and education in Nijmegen. Radboud university medical center strives to be at the forefront of shaping the healthcare of the future. We do this in a person-centered and innovative way, and in close collaboration with our network. We want to have a significant impact on healthcare. We want to improve with each passing day, continuously working towards better healthcare, research, and education. And gaining a better understanding of how diseases arise and how we can prevent, treat, and cure them, day in and day out. This way, every patient always receives the best healthcare, now and in the future. Because that is why we do what we do.

Read more about our strategy and what working at Radboud university medical center means. Our colleagues would be happy to tell you about it .#weareradboudumc