Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging
Review article, 2024

Prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) has emerged as an important imaging technique for prostate cancer. The use of PSMA PET/CT is rapidly increasing, while the number of nuclear medicine physicians and radiologists to interpret these scans is limited. Additionally, there is variability in interpretation among readers. Artificial intelligence techniques, including traditional machine learning and deep learning algorithms, are being used to address these challenges and provide additional insights from the images. The aim of this scoping review was to summarize the available research on the development and applications of AI in PSMA PET/CT for prostate cancer imaging. A systematic literature search was performed in PubMed, Embase and Cinahl according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 26 publications were included in the synthesis. The included studies focus on different aspects of artificial intelligence in PSMA PET/CT, including detection of primary tumor, local recurrence and metastatic lesions, lesion classification, tumor quantification and prediction/prognostication. Several studies show similar performances of artificial intelligence algorithms compared to human interpretation. Few artificial intelligence tools are approved for use in clinical practice. Major limitations include the lack of external validation and prospective design. Demonstrating the clinical impact and utility of artificial intelligence tools is crucial for their adoption in healthcare settings. To take the next step towards a clinically valuable artificial intelligence tool that provides quantitative data, independent validation studies are needed across institutions and equipment to ensure robustness.


S. L. Belal

Skåne University Hospital

Lund University

Sophia Frantz

Lund University

Skåne University Hospital

David Minarik

Lund University

Skåne University Hospital

Olof Enqvist

Imaging and Image Analysis

Eigenvision AB

Erik Wikström

Skåne University Hospital

L. Edenbrandt

University of Gothenburg

E. Tragardh

Skåne University Hospital

Lund University

Seminars in Nuclear Medicine

0001-2998 (ISSN) 15584623 (eISSN)

Vol. 54 1 141-149

Subject Categories

Urology and Nephrology

Radiology, Nuclear Medicine and Medical Imaging

Cancer and Oncology



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