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Prostate cancer is the most common type of cancer in Norwegian men, but many tumors are slow-growing and do not require treatment. Today, MRI is good at detecting suspicious lesions, yet it cannot reliably distinguish aggressive tumors from low-grade ones. As a result, many men undergo repeated invasive biopsies. New PET tracers targeting PSMA improve tumor localization and may correlate with cancer aggressiveness, offering potential for better assessment. This project aims to develop a method to predict Gleason Score non-invasively by applying machine learning to PET and MRI data. The work involves early static and dynamic PSMA PET imaging, tracer kinetic modelling, deep learning, and validation of PET-based measurements of PSMA internalization using ex-vivo cellular methods. If successful, the project could reduce the number of biopsies, improve diagnostic accuracy, offer full 3D ass
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