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A Whole Body Dosimetry Protocol for Peptide-Receptor Radionuclide Therapy PRRT: 2D Planar Image and Hybrid 2D+3D SPECT/CT Image Methods
Published on: April 24, 2020
Dose Calculations and Dose-Effect Relationships in 177Lu-PSMA I&T Radionuclide Therapy for Metastatic
Sandra Barna1, Alexander R Haug, Markus Hartenbach2
1From the Preclinical Molecular Imaging, AIT Austrian Institute of Technology GmbH, Seibersdorf.
Abstract:
Dose response of 22 patients experiencing mCRPC (metastatic castration-resistant prostate cancer) to Lu-PSMA I&T radionuclide therapy was investigated. Dosimetry calculations are used to assess correlations between dosimetric quantities and biomarker values.
Methods:
The patients' age range was 74 ± 7 years at the time of the investigated treatment cycle, and the mean injected activity was 7416 ± 218 MBq. Planar images at several time points postinjection were used for evaluation of absorbed doses to organs and lesion. Ga-PSMA PET/CT follow-up imaging enabled the determination of individual tumor molecular volume (TMV) shrinkage. Changes in 7 different biomarkers after the first treatment cycle were correlated with the calculated absorbed organ and TMV doses, resulting in a total number of 259 investigated correlations.
Results:
Sixty-three TMVs were identified in the bone, lymph node, and liver tissue with an average reduction of 32.3%, 84.7%, and 72.9%, respectively. Absorbed doses per unit of administered activity for organs and lesions show good agreement with previous works (0.77, 0.71, and 0.27 mGy/MBq for parotid gland, kidneys, and liver as well as 4.38, 5.47, and 4.95 mGy/MBq for bone, lymph node, and liver malignancies, respectively). Only 37 of 259 possible correlations turned out to be statistically significant, 26 of which are associated with the absorbed dose of an organ and the decrease of alkaline phosphatases.
Conclusions:
Although treatment with Lu-PSMA I&T leads to a big reduction of TMV in patients with mCRPC, the lack of correlations calls for studies using voxel-wise dosimetry based on SPECT/CTs.
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