Using scans and computer analysis to judge prostate cancer aggressiveness
Tests whether PSMA PET and machine learning can predict prostate tumor aggressiveness without biopsy.
Plain-language title generated from the registry record
Registry title: Virtual Biopsy of Prostate Cancer Using PSMA PET and AI
Key facts
- Where
- Tromsø, NorwaySee the site
Where in Norway
- Universitetssykehuset Nord Norge, TromsøTromsø, NorwayTaking people
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- University Hospital of North Norway
- Trial code
- NCT07266129
- Record updated
- 5 December 2025
- Original description
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.Read full original descriptionShow less
Prostate Cancer Malignancy Grading Using Prostate Specific Membrane Antigen (PSMA) PET and Machine Learning
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 assessment of the prostate, shorten clinical workflows, and help identify patients who would benefit most from PSMA-based radioligand therapy.
Inclusion: For patients who have undergone biopsies prior to PET:; Patients referred to clinical PET examination; For patients who have not had biopsies prior to PET:; Patients referred to urologist for suspected prostate cancer based on clinical symptoms or elevated PSA-levels.
Exclusion: MRI incompatible implants or other incompatibilities; Prostatektomy; Body weight under 100 kg.