Feasibility of Single-Time-Point Dosimetry for Radiopharmaceutical Therapies.

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 ¹⁷⁷ Lu-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 ¹⁷⁷ Lu-DOTATATE was approximately 72 h p.i., whereas 48 h p.i. was better for ¹⁷⁷ Lu-PSMA. Conclusion: Simplified STP dosimetry methods may compromise the accuracy of dose estimates, with some exceptions, such as for ¹⁷⁷ Lu-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.
(© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)