Can 3T MR Angiography Replace DSA for the Identification of Arteries Feeding Intracranial Meningiomas?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: For identifying the arterial feeders of meningiomas, the usefulness of 3D TOF MRA at 3T has not been systematically investigated. This study was intended to assess whether unenhanced 3D TOF MRA at 3T can replace DSA for the identification of arteries feeding intracranial meningiomas and whether it is useful for assessing their dural attachment. MATERIALS AND METHODS: Twenty-one consecutive patients with intracranial meningiomas (18 women, 3 men; aged 42-77 years, mean 57 years) underwent DSA, conventional MR imaging, and 3D TOF MRA. Two neuroradiologists independently evaluated the primary and secondary feeders of each tumor on maximum-intensity-projection and source MRA images. They also identified the location of dural attachments based on information from MR imaging/MRA images. Interobserver and intermodality agreement was determined by calculating the κ coefficient. RESULTS: For the identification of primary and secondary feeders on MRA images, interobserver agreement was very good (κ=0.83; 95% CI, 0.66-1.00) and moderate (κ=0.58; 95% CI, 0.34-0.82) and intermodality agreement (consensus reading of MRA versus DSA findings) was excellent (κ=0.94; 95% CI, 0.84-1.00) and good (κ=0.72; 95% CI, 0.51-0.93), respectively. With respect to the dural attachment of meningiomas, interobserver agreement was very good (κ=0.95; 95% CI, 0.84-1.00). The agreement in the diagnosis between MR imaging/MRA and surgery was excellent (κ=1.00). CONCLUSIONS: Unenhanced 3D TOF MRA at 3T cannot at present supplant DSA for the identification of the feeding arteries of intracranial meningiomas. This information may be useful for evaluating their dural attachment.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".