Feasibility of different meningioma delineation approaches on [18F]SiTATE PET/CT imaging
Why this work is in the frame
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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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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 it