Histopathologically Validated Diagnostic Accuracy of PSMA-PET/CT in the Primary and Secondary Staging of Prostate Cancer and the Impact of PSMA-PET/CT on Clinical Management: A Systematic Review and Meta-analysis.


Journal

Seminars in nuclear medicine
ISSN: 1558-4623
Titre abrégé: Semin Nucl Med
Pays: United States
ID NLM: 1264464

Informations de publication

Date de publication:
09 2023
Historique:
received: 14 02 2023
revised: 16 02 2023
accepted: 17 02 2023
medline: 11 8 2023
pubmed: 3 4 2023
entrez: 2 4 2023
Statut: ppublish

Résumé

Prostate-specific membrane antigen (PSMA) is a highly expressed protein in prostate cancer (PCa) and has become an increasingly popular target for molecular imaging in recent years. PSMA based positron-emission-tomography/computed tomography (PET/CT) is a well characterised hybrid imaging modality that combines the high sensitivity of PET with the high spatial resolution of CT imaging. The combination of these two imaging modalities provides an accurate tool for detecting and managing PCa. Several diagnostic accuracy and clinical management studies investigating the role of PSMA PET/CT in PCa have been published recently. This study aimed to perform an updated systematic review and meta-analysis to evaluate the diagnostic performance of PSMA PET/CT in localised, lymph node metastatic (LNM) and recurrent PCa patients and assess its impact on the clinical management of primary and recurrent PCa. Using Medline, Embase, PubMed and Cochrane Library databases, studies reporting the diagnostic accuracy and clinical management of PSMA PET/CT were analysed based on the PRISMA guidelines. Statistical analyses were conducted using random-effects models, and meta-regression explored observed heterogeneity. Results indicate that the sensitivity and specificity of PSMA PET/CT for localised PCa were 71.0% (95% confidence interval (CI): 58.0, 81.0) and 92.0% (95% CI: 86.0, 96.0), respectively (N = 10; n = 404 patients). Sensitivity and specificity in LNM were 57.0% (95% CI: 49.0, 64.0) and 96.0% (95% CI: 95.0, 97.0) (N = 36; n = 3,659 patients). For patients with biochemical recurrence (BCR), sensitivity was 84.0% (95% CI: 74.0, 90.0), and specificity was 97.0% (95% CI: 88.0, 99.0) (N = 9; n = 818 patients). The pooled proportion of management changes in primary (N = 16; n = 1,099 patients) and recurrent (N = 40; n = 5,398 patients) PCa was 28.0% (95% CI: 23.0, 34.0) and 54.0% (95% CI: 50.0, 58.0), respectively. In conclusion, PSMA PET/CT shows moderate sensitivity and high specificity in localised and LNM disease, while the accuracy in BCR patients was high. PSMA PET/CT also had a large impact on the clinical management of PCa patients. This is the most extensive and first systematic review to include three subgroups of PCa with histologically verified diagnostic accuracy and clinical management change reported separately in primary and recurrent disease settings.

Identifiants

pubmed: 37005145
pii: S0001-2998(23)00012-0
doi: 10.1053/j.semnuclmed.2023.02.006
pii:
doi:

Substances chimiques

Gallium Radioisotopes 0

Types de publication

Meta-Analysis Systematic Review Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

706-718

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Auteurs

Varinder Jeet (V)

Centre for the Health Economy, Macquarie University Business School, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia. Electronic address: varinder.jeet@mq.edu.au.

Bonny Parkinson (B)

Centre for the Health Economy, Macquarie University Business School, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.

Rachel Song (R)

Centre for the Health Economy, Macquarie University Business School, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.

Rajan Sharma (R)

Centre for the Health Economy, Macquarie University Business School, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.

Martin Hoyle (M)

Centre for the Health Economy, Macquarie University Business School, Australian Institute of Health Innovation, Macquarie University, Sydney, NSW, Australia.

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Classifications MeSH