Optimizing 131I-mIBG Dosimetry: Validation of Simplified Time-Point and Segmentation Approaches
Bibliographic record
Abstract
Abstract Purpose To evaluate simplified dosimetry methods for 131 I-mIBG therapy that maintain agreement with reference method while reducing clinical workflow burdens, particularly beneficial for pediatric applications. Methods In 24 patients, we implemented a hybrid protocol combining planar whole-body scans (1, 24, 48 h p.i.) with 24 h SPECT/CT for whole-body and liver dosimetry. Three liver segmentation methods were compared on SPECT/CT: whole-organ manual delineation (reference), 4 mL homogeneous sphere, and 4 mL peak sphere. To estimate time-integrated activity coefficients (TIACs) we modeled biokinetics with monoexponential using: full three-time-point data (reference), reduced combinations (1–2, 1–3, 2–3), Hänscheid single-time-point method, and population-based half-lives. Statistical analysis included linear mixed-effects modeling for segmentation comparisons and Bland-Altman analysis for TIAC method validation. Results The median absorbed dose was 0.36 mGy/MBq for liver and 0.09 mGy/MBq for whole-body. Among simplified protocols, dual-time-point 24–48 h imaging demonstrated the least bias, with liver absorbed doses showing − 0.02 mGy/MBq (limits of agreement: -0.08 to 0.04 mGy/MBq) and whole-body absorbed doses 0.001 mGy/MBq (limits of agreement: -0.004 to 0.005 mGy/MBq) compared to the full three-time-point reference results. Among single-time-point methods, for both liver and whole-body, the Hänscheid approach applied at 48 h demonstrated superior performance. The 4 mL peak sphere overestimated absorbed doses by 69% versus whole-organ delineation (0.61 vs. 0.36 mGy/MBq, p < 0.001), and homogeneous spheres underestimated by 8% (0.33 vs. 0.36 mGy/MBq, p = 0.046). Conclusions Clinically feasible 131 I-mIBG dosimetry can be achieved through: (1) dual 24 h/48 h imaging (< 6% mean bias), (2) single 48 h Hänscheid method (< 6% mean bias), and (3) homogeneous sphere liver segmentation ( < 8% mean difference from reference).
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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 | 0.005 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".