An International Study of Factors Affecting Variability of Dosimetry Calculations, Part 4: Impact of Fitting Functions in Estimated Absorbed Doses
Bibliographic record
Abstract
Individualized radiopharmaceutical therapies guided by patient-specific absorbed dose (AD) assessments using nuclear medicine imaging have the potential to improve both efficacy and safety. Understanding sources of variability in AD calculations is critical for standardization. The Society of Nuclear Medicine and Molecular Imaging Dosimetry Task Force launched the 177Lu Dosimetry Challenge to evaluate variability across steps within the dosimetry workflow. This work aimed to assess the variability in ADs due to different fitting and integration methods. Methods: Anonymized datasets from 2 patients treated with 177Lu-DOTATATE, including serial SPECT/CT scans, segmented organs and lesions, and time-integrated activity maps, were made available online. Participants were invited to perform dosimetry calculations and submit their results using standardized submission spreadsheets. Fitting approaches were categorized, and relative AD variability was analyzed using the quartile coefficient of dispersion and interquartile range. Results: The variability in AD due to the fitting step for patient A’s kidneys was less than 1%. In contrast, patient B’s kidneys showed higher variability, with values below 10%. Lesions exhibited more variability in fitting than did kidneys, with variability within 25%. Conclusion: The contribution of variability caused by fitting and integration is small for healthy organs. By following recommendations such as selection of appropriate functions, pharmacokinetic modeling, and sanity checks, this variability can be further reduced.
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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 | 0.038 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".