MRI predictors for brain invasion in meningiomas
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND AND PURPOSE: In the 2016 revision of the World Health Organization classification of central nervous system tumours, brain invasion was added as an independent histological criterion for the diagnosis of a World Health Organization grade II atypical meningioma. The aim of this study was to assess whether magnetic resonance imaging characteristics can predict brain invasion for meningiomas. MATERIALS AND METHODS: We conducted a retrospective review of all meningiomas resected at our institution between 2005 and 2016 which had preoperative magnetic resonance imaging and included brain tissue within the pathology specimen. One hundred meningiomas were included in the study, 60 of which had histopathological brain invasion, 40 of which did not. Magnetic resonance imaging characteristics of tumours were evaluated for potential predictors of brain invasion. Tumour location, size, perilesional oedema, contour, cerebrospinal fluid cleft, peritumoral cyst, dural venous sinus invasion, bone invasion, hyperostosis and the presence of enlarged pial arteries and veins were evaluated. Data were analysed using conventional chi-square, Fisher's exact test and logistic regression. RESULTS: The volume of peritumoral oedema was significantly higher in the brain-invasive meningioma group compared to the non-brain-invasive group. The presence of a complete cleft was a rare finding that was only found in non-brain-invasive meningiomas. The presence of enlarged pial feeding arteries was a rare finding that was only found in brain-invasive meningiomas. CONCLUSIONS: An increased volume of perilesional oedema is associated with the likelihood of brain invasion for meningiomas.
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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 | 0.001 | 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