Feasibility of different meningioma delineation approaches on [18F]SiTATE PET/CT imaging
Bibliographic record
Abstract
BACKGROUND: Somatostatin receptor (SSTR)-targeted PET is valuable for meningioma imaging due to high SSTR expression. [18F]SiTATE, a novel tracer, is not only promising for imaging neuroendocrine tumors but also for meningiomas. Standardized delineation methods on [18F]SiTATE PET are lacking. This study correlates CT-based volumes with PET-based delineation approaches to identify a threshold for standardized [18F]SiTATE PET volume assessment. METHODS: Patients with well-delineated, extraosseous meningioma on CT (≥ 1mL) who underwent [18F]SiTATE PET/CT were included. Volumes were assessed on contrast-enhanced CT and correlated with PET-based delineation approaches: (I) fixed SUV threshold, (II) isocontour thresholding relative to SUVmax (SUV%), and thresholds relative to (III) bone marrow (SUVBM), (IV) parotid gland (SUVparotis) and (V) pituitary gland (SUVsella). RESULTS: 19 meningiomas in 17 PET/CT scans (16 patients) were included. A fixed SUV of 4.0 (r = 0.783, p < 0.001) showed good correlation with CT volumes without skewed distribution on Bland-Altman-Plot analysis. Using isocontour-based thresholds, 45% SUVmax (r = 0.496, p = 0.031) showed the highest concordance. Best reference-based approaches were achieved by 150% SUVBM (r = 0.859, p < 0.001), 250% SUVparotis (r = 0.460, p = 0.047) and 70% SUVsella (r = 0.819, p < 0.001). However, background-based approaches showed a trend towards overestimation of PET-volumes in larger meningiomas as assessed on Bland-Altman-Plot analyses. Uptake intensities of reference tissues (SUVBM, SUVparotis and SUVsella) were not inter-correlated (p > 0.05 each). CONCLUSION: A fixed SUV threshold of 4.0 showed strong agreement with CT-based volumes in well-delineated, extraosseous meningiomas which offers a simple, clinically applicable method without technical requirements. Reference tissue-based methods showed similar correlations but tended to overestimate volumes in larger lesions.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".