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

Lindgren Belal, Sarah LU orcid ; Frantz, Sophia LU ; Minarik, David LU ; Enqvist, Olof LU ; Wikström, Erik ; Edenbrandt, Lars and Trägårdh, Elin LU (2023) In Seminars in Nuclear Medicine
Abstract
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... (More)
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. (Less)
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Contribution to journal
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published
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in
Seminars in Nuclear Medicine
publisher
Elsevier
external identifiers
  • pmid:37357026
  • scopus:85162915857
ISSN
0001-2998
DOI
10.1053/j.semnuclmed.2023.06.001
language
English
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yes
id
df32f1b0-7977-4793-a883-0b2c7b8653fb
date added to LUP
2023-09-27 11:08:57
date last changed
2023-09-28 04:01:34
@article{df32f1b0-7977-4793-a883-0b2c7b8653fb,
  abstract     = {{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.}},
  author       = {{Lindgren Belal, Sarah and Frantz, Sophia and Minarik, David and Enqvist, Olof and Wikström, Erik and Edenbrandt, Lars and Trägårdh, Elin}},
  issn         = {{0001-2998}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{Elsevier}},
  series       = {{Seminars in Nuclear Medicine}},
  title        = {{Applications of Artificial Intelligence in PSMA PET/CT for Prostate Cancer Imaging}},
  url          = {{http://dx.doi.org/10.1053/j.semnuclmed.2023.06.001}},
  doi          = {{10.1053/j.semnuclmed.2023.06.001}},
  year         = {{2023}},
}