<sup>68</sup>Ga-DOTATATE-positron emission tomography imaging in spinal meningioma
Bibliographic record
Abstract
Imaging with positron emission tomography (PET) and (68)Ga-DOTA peptides is a promising method in intracranial meningiomas. Especially in recurrent meningioma discrimination between scar tissue and recurrent tumor tissue in magnetic resonance imaging (MRI) is often difficult. We report the first case of (68)Ga-DOTATATE-PET/computed tomography (PET/CT) imaging in recurrent spinal meningioma. A 64-year-old Caucasian female patient was referred to our department with the second recurrence of thoracic meningothelial meningioma. In MRI, it remained unclear if the multiple enhancements seen represented scar tissue or vital tumor. We offered (68)Ga-DOTATATE-PET/CT imaging in order to evaluate the best strategy. (68)Ga-DOTATATE-PET/CT imaging revealed strong tracer uptake in parts of the lesions. The pattern did distinctly differ from MRI enhancement. Multiple biopsies were performed in the PET-positive and PET-negative regions. Histological results confirmed the prediction of (68)Ga-DOTATATE-PET with vital tumor in PET-positive regions and scar tissue in PET-negative regions. Differentiating scar tissue from tumor can be challenging in recurrent spinal meningioma with MRI alone. In the presented case, (68)Ga-DOTATATE-PET imaging was able to differentiate noninvasively between tumor and scar.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".