Validation of SUV thresholds in [¹⁸F]SiTATE PET/CT for accurate meningioma segmentation
Bibliographic record
Abstract
Abstract Purpose Somatostatin receptor (SSTR)-targeted PET/CT provides valuable clinical insights beyond standard imaging in meningioma patients. Due to its excellent diagnostic capabilities and favorable logistics, the 18 F-labeled SSTR-targeting peptide SiTATE is increasingly in demand. We aimed to validate a recently proposed standard uptake value (SUV) threshold for accurate meningioma delineation in a clinically diverse patient cohort, including complex anatomical locations and lesions with prior surgical intervention. Methods Consecutive patients with known or suspected meningioma who underwent [ 18 F]SiTATE PET/CT and contrast enhanced cerebral MRI were included. Lesions were semi-automatically segmented on PET images using an individualized minimal SUV (SUV min ) within a manually defined volume of interest. Correlative CT and MRI images were used to refine segmentations for each lesion, identifying the optimal lesion-specific SUV min to accurately capture the true volume of the meningioma. All lesions were additionally segmented using the recently proposed threshold of 4.0, and resulting volumes were compared. Results 61 patients with 109 lesions were analyzed: 40 (37%) extraosseous, 32 (29%) partial trans-osseous, and 37 (34%) predominantly intraosseous. The median optimal SUV min for lesion delineation was 4.2. Osseous involvement did not significantly affect the median SUV min ( p = 0.1). Individualized SUV volumes showed excellent absolute agreement with those obtained using the fixed threshold of 4.0 (ICC[A,1] = 0.967; 95% CI: 0.952–0.977; p < 0.0001). However, 17 lesions (SUV max < 4.2) were not captured by the fixed threshold. Conclusion The proposed SUV threshold of 4.0 showed promising results, supporting its suitability for clinical practice. Although limitations were evident, with 16% of lesions — primarily very small — showing reduced uptake and therefore not captured by this threshold, the study underscores its applicability in clinical practice.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".