Prostate cancer affects approximately 1 in 8 men during their lifetime. This number is expected to increase substantially due to the aging population and excessive use of PSA-testing, and new clinical tools are urgently needed.

Magnetic resonance imaging (MRI) has become a key component in the diagnostic workup. However, the interpretation of MRI images relies on the manual reading by experienced radiologists, which is a time and cost-intensive resource. Moreover, this process underuses the quantitative nature of the data. Our hypothesis is that better diagnostic performance is achievable by providing the radiologist with a decision support system based on artificial intelligence (AI). For such a system to work in clinical practice, it needs to be accurate as well as transparent and interpretable. Development of transparent and interactive processes for responsible research and innovation (RRI) is an integrated part of the project.

We propose to develop a decision support system that combines transparent AI methods, deep learning and model-based imaging features, and clinical information to provide the radiologist with a new set of interpretable tools to more accurately and efficiently detect prostate cancer, differentiate between high-risk and low-risk disease, and target prostate biopsies. The foundation of this project is formed by a unique Norwegian dataset of >1600 patients with MRI examinations and clinical variables, and an interdisciplinary project team with dedicated experience in MRI, AI, urology and radiology. Our collaboration with international experts in the field ensures access to similar data from The Netherlands and Taiwan, enabling solutions that also cover challenges related to demographic and multi-center variance.

The project has the potential to substantially reduce health care costs, alleviate the demand on medical personnel, and obtain better treatment stratification, less side-effects, and improved quality of life in a relatively short timeframe.