Radiation Dosimetry of Theragnostic Pairs for Isotopes of Iodine in IAZA
Bibliographic record
Abstract
Theragnostic pairs of isotopes are used to infer radiation dosimetry for a therapeutic radiopharmaceutical from a diagnostic imaging study with the same tracer molecule labelled with an isotope better suited for the imaging task. We describe the transfer of radiation dosimetry from the diagnostic radioiodine isotope 123I, labelled for the hypoxia tracer molecule iodoazomycin arabinoside ([123I]IAZA), to isotopes 131I (therapeutic) and 124I (PET imaging). Uncertainties introduced by the dissimilar isotope half-lives are discussed in detail. Radioisotope dosimetries for [123I]IAZA were obtained previously. These data are used here to calculate residence times for 131I and 124I and their uncertainties. We distinguish two cases when extrapolating to infinity: purely physical decay (case A) and physical decay plus biological washout (case B). Organ doses were calculated using the MIRD schema with the OLIDNA/EXM code. Significant increases in some organ doses (in mSv per injected activity) were found for 131I and 124I. The most affected organs were the intestinal walls, thyroid, and urinary bladder wall. Uncertainty remained similar to 123I for case A but considerably greater for case B, especially for long biological half-lives (GI tract). Normal tissue dosimetries for IAZA must be considered carefully when substituting isotope species. A long biological half-life can significantly increase dosimetric uncertainties. These findings are relevant when considering PET imaging studies with [124I]IAZA or therapeutic administration of [131I]IAZA.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".