Abstract
Because of challenges in performing routine personalized dosimetry in radiopharmaceutical therapies, interest in single-time-point (STP) dosimetry, particularly using only a single SPECT scan, is on the rise. Meanwhile, there are questions about the reliability of STP dosimetry, with limited independent validations. In the present work, we analyzed 2 STP dosimetry methods and evaluated dose errors for several radiopharmaceuticals based on effective half-life distributions. Methods: We first challenged the common assumption that radiopharmaceutical effective half-lives across the population are gaussian-distributed (i.e., follow a normal distribution). Then, dose accuracy was estimated using 2 STP dosimetry methods for a wide range of potential post injection (p.i.) scan time points for different radiopharmaceuticals applied to neuroendocrine tumors and prostate cancer. The accuracy and limitations of each of the STP methods were discussed. Results: A lognormal distribution was more appropriate for capturing effective half-life distributions. The STP framework was promising for dosimetry of Lu-177-DOTATATE and for kidney dosimetry of different radiopharmaceuticals (errors < 30%). Meanwhile, for some radiopharmaceuticals, STP accuracy was compromised (e.g., in bone marrow and tumors for (177)-labeled prostate-specific membrane antigen [PSMA])). The optimal SPECT scanning time for Lu-177-DOTATATE was approximately 72 h p.i., whereas 48 h p.i. was better for Lu-177-PSMA. Conclusion: Simplified STP dosimetry methods may compromise the accuracy of dose estimates, with some exceptions, such as for Lu-177-DOTATATE and for kidney dosimetry in different radiopharmaceuticals. Simplified personalized dosimetry in the clinic continues to be challenging. On the basis of our results, we make suggestions and recommendations for improved personalized dosimetry using simplified imaging schemes.
Item Type: | Journal article |
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Faculties: | Medicine |
Subjects: | 600 Technology > 610 Medicine and health |
ISSN: | 0161-5505 |
Language: | English |
Item ID: | 99086 |
Date Deposited: | 05. Jun 2023, 15:30 |
Last Modified: | 17. Oct 2023, 15:00 |