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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".