Feasibility of single-time-point dosimetry for radiopharmaceutical therapies
Bibliographic record
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 177Lu-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 177Lu-DOTATATE was approximately 72 h p.i., whereas 48 h p.i. was better for 177Lu-PSMA. Conclusion: Simplified STP dosimetry methods may compromise the accuracy of dose estimates, with some exceptions, such as for 177Lu-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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".