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). [ 18 F]SiTATE has recently been introduced showing high image quality, promising clinical performance and improved logistics compared to the clinical reference standard 68 Ga-DOTA-TOC. Here we present the first dosimetry and optimal scan time analysis. Methods Eight NET patients received a [ 18 F]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 18 F 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 [ 18 F]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 68 Ga-labelled alternatives. For clinical use of [ 18 F]SiTATE, the best compromise between image quality and tumour-to-background contrast is reached at 120 min, followed by 60 min after injection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".