PhD candidate 'Using artificial intelligence for improving the efficiency of lung cancer screening'

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.