Objectives: This retrospective study aimed to evaluate the role of Ga-PSMA-I&T PET/CT in the primary staging of newly diagnosed prostate cancer (PCa), with a focus on the detection of metastatic nodal disease. Correlation of the rate of detection of metastatic disease by Ga-PSMA-I&T PET/CT with the Gleason score (GS) and serum prostate-specific antigen (PSA) was performed to determine the GS and PSA criteria defining patients who would benefit from Ga-PSMA-I&T PET/CT imaging for staging, risk stratification and therapy optimization.
Patients And Methods: Patient data and images from 70 patients with a recent diagnosis of prostate cancer who had undergone Ga-PSMA-I&T PET/CT were analysed retrospectively. Data and images were analysed for the rate of detection of primary and metastatic PCa, and correlation with PSA and GS.
Results: The rate of detection of primary tumour by Ga-PSMA-I&T for patients with serum PSA less than 5 ng/ml was 73%. The corresponding rate was 90% for patients with PSA 5-10 ng/ml and 97% for patients with PSA more than 10 ng/ml. Metastatic PCa and/or infiltrative disease was detected in 24/70 study patients in total: 1/11 patients with PSA less than 5 ng/ml and 23/59 patients with serum PSA at least 5 ng/ml. The rate of detection of metastatic PCa was greater in patients with GS 9 or more (48%) relative to those with GS 8 (32%) or GS ≤7 (18%).
Conclusion: A role for Ga-PSMA-I&T PET/CT in primary PCa staging of high-grade disease (GS 8 or more and PSA >10 ng/ml) has been shown. There was a low rate of detection of PSMA-avid metastases in low-grade disease (GS 7 or less and PSA <5 ng/ml), suggesting that there is a limited role for this modality in such cases.
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http://dx.doi.org/10.1097/MNM.0000000000000738 | DOI Listing |
Curr Radiopharm
July 2024
Nuclear Medicine Department, Institut Régional du Cancer de Montpellier (ICM), University of Montpellier, Montpellier, France.
Background: Prostate-specific membrane antigen (PSMA) is an ideal target for molecular imaging and targeted radionuclide therapy in prostate cancer. Consequently, various PSMA ligands were developed. Some of these molecules are functionalized with a chelator that can host radiometals, such as Ga for PET imaging.
View Article and Find Full Text PDFNucl Med Biol
February 2024
Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, Alberta T6G 1Z2, Canada; Cancer Research Institute of Northern Alberta, University of Alberta, Edmonton, Alberta T6G 2E1, Canada. Electronic address:
Introduction: Copper-64 (Cu, t = 12.7 h) is a positron emitter well suited for theranostic applications with beta-emitting Cu for targeted molecular imaging and radionuclide therapy. The present work aims to evaluate the radionuclidic purity and radiochemistry of Cu produced via the Zn(p,nα)Cu nuclear reaction.
View Article and Find Full Text PDFCent European J Urol
July 2023
Martini-Klinik Prostate Cancer Center, University Hospital Hamburg-Eppendorf, Hamburg, Germany.
We present the case of a patient who underwent an open radical prostatectomy with pelvic lymph node dissection (Gleason 4+3, pT3a pN1 R0) in March 2017. In November 2020, prostate-specific membrane antigen (PSMA)-radioguided salvage lymph node dissection was planned due to a single left para-rectal lymph node at a [Ga] Ga-PSMA-I&T PET. In January 2022, the [Ga] Ga-PSMA-I&T PET showed an isolated liver lesion.
View Article and Find Full Text PDFAnn Nucl Med
February 2024
Department of Nuclear Medicine, University Hospital Würzburg, Oberdürrbacher Straße 6, 97080, Würzburg, Germany.
Z Med Phys
May 2024
University Hospital Würzburg, Department of Nuclear Medicine, Oberdürrbacher Str. 6, 97080 Würzburg, Germany.
Unlabelled: For dosimetry of radiopharmaceutical therapies, it is essential to determine the volume of relevant structures exposed to therapeutic radiation. For many radiopharmaceuticals, the kidneys represent an important organ-at-risk. To reduce the time required for kidney segmentation, which is often still performed manually, numerous approaches have been presented in recent years to apply deep learning-based methods for CT-based automated segmentation.
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