PET imaging in patients with meningioma—report of the RANO/PET Group
Bibliographic record
Abstract
Meningiomas are the most frequent nonglial primary brain tumors and represent about 30% of brain tumors. Usually, diagnosis and treatment planning are based on neuroimaging using mainly MRI or, rarely, CT. Most common treatment options are neurosurgical resection and radiotherapy (eg, radiosurgery, external fractionated radiotherapy). For follow-up after treatment, a structural imaging technique such as MRI or CT is used. However, these structural imaging modalities have limitations, particularly in terms of tumor delineation as well as diagnosis of posttherapeutic reactive changes. Molecular imaging techniques such as PET can characterize specific metabolic and cellular features which may provide clinically relevant information beyond that obtained from structural MR or CT imaging alone. Currently, the use of PET in meningioma patients is steadily increasing. In the present article, we provide recommendations for the use of PET imaging in the clinical management of meningiomas based on evidence generated from studies being validated by histology or clinical course.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".