Dosimetry and optimal scan time of [18F]SiTATE-PET/CT in patients with neuroendocrine tumours
Bibliographic record
Abstract
Abstract Purpose Radiolabelled somatostatin analogues targeting somatostatin receptors (SSR) are well established for combined positron emission tomography/computer tomography (PET/CT) imaging of neuroendocrine tumours (NET). [18F]SiTATE has recently been introduced showing high image quality, promising clinical performance and improved logistics compared to the clinical reference standard 68Ga-DOTA-TOC. Here we present the first dosimetry and optimal scan time analysis. Methods Eight NET patients received a [18F]SiTATE-PET/CT (250 ± 66 MBq) with repeated emission scans (10, 30, 60, 120, 180 min after injection). Biodistribution in normal organs and SSR-positive tumour uptake were assessed. Dosimetry estimates for risk organs were determined using a combined linear-monoexponential model, and by applying 18F S-values and reference target masses for the ICRP89 adult male or female (OLINDA 2.0). Tumour-to-background ratios were compared quantitatively and visually between different scan times. Results After 1 h, normal organs showed similar tracer uptake with only negligible changes until 3 h post-injection. In contrast, tracer uptake by tumours increased progressively for almost all types of metastases, thus increasing tumour-to-background ratios over time. Dosimetry resulted in a total effective dose of 0.015 ± 0.004 mSv/MBq. Visual evaluation revealed no clinically relevant discrepancies between later scan times, but image quality was rated highest in 60 and 120 min images. Conclusion [18F]SiTATE-PET/CT in NET shows overall high tumour-to-background ratios from 60 to 180 min after injection and an effective dose comparable to 68Ga-labelled alternatives. For clinical use of [18F]SiTATE, the best compromise between image quality and tumour-to-background contrast is reached at 120 min, followed by 60 min after injection.
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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 | 0.001 | 0.002 |
| 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".