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Search: onr:"swepub:oai:research.chalmers.se:88f69325-ac26-40bd-8357-9f001aa7d948" > Applications of Art...

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Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging

Lindgren Belal, Sarah (author)
Lund University,Lunds universitet,Klinisk fysiologi och nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Clinical Physiology and Nuclear Medicine, Malmö,Lund University Research Groups,LUCC: Lund University Cancer Centre,Other Strong Research Environments,Skåne University Hospital,Skånes universitetssjukhus (SUS)
Frantz, Sophia (author)
Lund University,Lunds universitet,Klinisk fysiologi och nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Clinical Physiology and Nuclear Medicine, Malmö,Lund University Research Groups,Skåne University Hospital,Skånes universitetssjukhus (SUS)
Minarik, David (author)
Lund University,Lunds universitet,Klinisk fysiologi och nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Clinical Physiology and Nuclear Medicine, Malmö,Lund University Research Groups,Skåne University Hospital,Skånes universitetssjukhus (SUS)
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Enqvist, Olof, 1981 (author)
Gothenburg University,Göteborgs universitet,Chalmers University of Technology,Lund University,Lunds universitet,Klinisk fysiologi och nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Clinical Physiology and Nuclear Medicine, Malmö,Lund University Research Groups,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine,Chalmers tekniska högskola
Wikström, Erik (author)
Skånes universitetssjukhus (SUS),Skåne University Hospital
Edenbrandt, Lars, 1957 (author)
Gothenburg University,Göteborgs universitet,University of Gothenburg,Sahlgrenska Academy,Institutionen för medicin, avdelningen för molekylär och klinisk medicin,Institute of Medicine, Department of Molecular and Clinical Medicine
Trägårdh, Elin (author)
Lund University,Lunds universitet,Nuklearmedicin, Malmö,Forskargrupper vid Lunds universitet,Klinisk fysiologi och nuklearmedicin, Malmö,WCMM- Wallenberg center för molekylär medicinsk forskning,Medicinska fakulteten,Nuclear medicine, Malmö,Lund University Research Groups,Clinical Physiology and Nuclear Medicine, Malmö,WCMM-Wallenberg Centre for Molecular Medicine,Faculty of Medicine,Skåne University Hospital,Skånes universitetssjukhus (SUS)
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 (creator_code:org_t)
2024
2024
English.
In: Seminars in Nuclear Medicine. - 1558-4623 .- 0001-2998. ; 54:1, s. 141-149
  • Research review (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Urologi och njurmedicin (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Urology and Nephrology (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Radiologi och bildbehandling (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Radiology, Nuclear Medicine and Medical Imaging (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Cancer och onkologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Cancer and Oncology (hsv//eng)

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