MNGI-03. VOXEL-BASED LESION MAPPING TECHNIQUE REVEALS THE SPATIAL DISTRIBUTION OF MENINGIOMAS
Bibliographic record
Abstract
Recent studies have reported strong associations between tumor locations, clinical features and genetic alterations in meningiomas. Although several predilection sites including cerebral convex, parasagittal areas or several skull base regions have been well-documented, those evaluations were traditionally based on descriptive methods. This study aimed to re-evaluate preferential areas of meningiomas more accurately by means of voxel-based lesion mapping (VBLM) and three-dimensional (3D) image rendering techniques. Magnetic resonance images of a consecutive series of 248 cases with treatment naïve 260 meningiomas were retrospectively analyzed. All images were registered to a T1-weighted brain atlas provided by the Montreal Neurological Institute (MNI152), and a lesion frequency map was created followed by 3D volume rendering to visualize the predilection sites of the tumors. In order to evaluate the validity of the cohort, the frequencies of tumor locations were compared with the results of previous publication including the Japan Brain Tumor Registry (JBTR). The 3D lesion frequency map clearly showed the preferential areas of meningiomas including the cerebral convexity and skull base regions. In parasagittal areas, the mid one-third of the superior sagittal sinus was most commonly affected. Substantial lesion accumulation was observed around the leptomeninges covering the central sulcus and the Sylvian fissure while very few lesions were observed at the frontal, parietal and occipital convexities. In skull base areas, parasellar, sphenoid wing and petroclival regions were commonly affected by the tumor. In the descriptive evaluation, frequencies for the tumor incidences in each area including the cerebral convex or falx, parasellar region were comparable with previous reports. The VBLM technique successfully visualized the meningioma predilection areas on the 3D lesion frequency map, and particularly indicated the preference of the mid-third area among the parasagittal regions. These observations warrant further studies for meningioma biology using this technique.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".