Comparison of the averaged biodistribution and pharmacokinetics between [99mTc]Tc-HYNIC-TOC and [177Lu]Lu-DOTA-TATE in neuroendocrine tumor patients
Bibliographic record
Abstract
BACKGROUND: Accurate pre-therapeutic dosimetry can potentially help optimize peptide receptor radionuclide therapy in patients with neuroendocrine tumors. While positron emission tomography (PET) imaging with [68Ga]Ga-DOTA-peptides has been previously studied for predicting therapeutic absorbed dose, it is limited by its single-time-point nature. In contrast, [99mTc]Tc-HYNIC-TOC offers advantages such as lower cost and multi-time-point imaging capability, but its potential to predict [177Lu]Lu-DOTA-TATE biodistribution remains underexplored. The study objective was to evaluate pharmacokinetics, isolating biological clearance from physical decay effects, and compare relative organ uptake ratios between the compounds. This work lays the foundation for future studies exploring [99mTc]Tc-HYNIC-TOC as a predictive tool for treatment planning - particularly in regions where [68Ga] Ga-DOTA-peptides are less accessible. MATERIAL AND METHODS: The study compared the biodistribution and pharmacokinetics of [99mTc]Tc-HYNIC-TOC and [177Lu]Lu-DOTA-TATE in neuroendocrine tumor (NET) patients by modeling pharmacokinetics, analyzing time-integrated activity coefficients, and biological half-lives in the kidneys, liver, spleen, and lesions from 11 diagnostic and 8 therapeutic studies. RESULTS: [177Lu]Lu-DOTA-TATE showed significantly higher kidney-to-spleen and lesion-to-spleen uptake ratios compared to [99mTc]Tc-HYNIC-TOC, indicating greater relative accumulation in these organs. There was no statistically significant difference in liver-to-spleen uptake between the two compounds. The biological half-life in the kidneys was longer for [99mTc]Tc-HYNIC-TOC than for [177Lu]Lu-DOTA-TATE, suggesting faster renal clearance of the therapeutic agent. Liver and spleen kinetics were comparable between the two tracers. CONCLUSIONS: While [99mTc]Tc-HYNIC-TOC and [177Lu]Lu-DOTA-TATE show similar biodistribution patterns in the liver and spleen, they differ in renal and lesion uptake and clearance kinetics. The ability of [99mTc]Tc-HYNIC-TOC to capture dynamic behavior through multi-time-point imaging may offer improved prediction of [177Lu]Lu-DOTA-TATE absorbed dose.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".