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Diagnostic value of two-time point [68Ga]Ga-PSMA-11 PET/CT in the primary staging of untreated prostate cancer.

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4 Citations (Web of Science)

Abstract

The emerging PET tracer [68Ga]Ga-PSMA-11 has been established for staging in prostate cancer (PCa). Aim was to determine the value of early static imaging in two-phase PET/CT. 100 men with newly diagnosed histopathologically confirmed untreated PCa who underwent [68Ga]Ga-PSMA-11 PET/CT from January 2017 to October 2019 were included. The two-phase imaging protocol consisted of an early static scan of the pelvis (6 min p.i.) and a late total-body scan (60 min p.i). Associations of semi-quantitative parameters derived via volumes of interest (VOI) with Gleason grade group and PSA were investigated. In 94/100 patients (94%) the primary tumor was detected in both phases. In 29/100 patients (29%) metastases were detected at a median PSA level of 32.2 ng/ml (0.41-503 ng/ml). In 71/100 patients (71%) without metastasis a median PSA level of 10.1 ng/ml (0.57-103 ng/ml) was observed (p = < 0.001). Primary tumors demonstrated a median standard uptake value maximum (SUVmax) of 8.2 (3.1-45.3) in early phase versus 12.2 (3.1-73.4) in late phase and a median standard uptake value mean (SUVmean) of 4.2 (1.6-24.1) in early phase versus 5.8 (1.6-39.9) in late phase, significantly increasing over time (p = < 0.001). Higher SUVmax and SUVmean were associated with higher Gleason grade group (p = 0.004 and p = 0.003, respectively) and higher PSA levels (p = < 0.001). In 13/100 patients the semi-quantitative parameters including SUVmax were declining in the late phase compared to early phase. Two-phase [68Ga]Ga-PSMA-11 PET/CT demonstrates a high detection rate for primary tumor of untreated PCa of 94% and improves diagnostic accuracy. Higher PSA levels and Gleason grade group are associated with higher semi-quantitative parameters in the primary tumor. Early imaging provides additional information in a small sub-group with declining semi-quantitative parameters in the late phase.

Original languageEnglish
Article number8297
Pages (from-to)8297
Number of pages7
JournalScientific Reports
Volume13
Issue number1
DOIs
Publication statusPublished - 22 May 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Branches of science

  • 102 Computer Sciences
  • 301 Medical-Theoretical Sciences, Pharmacy
  • 302 Clinical Medicine
  • 305 Other Human Medicine, Health Sciences
  • 104 Chemistry

Research Fields

  • Cancer Research

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