Radiological Prediction of Skull Base Meningioma Consistency for Endoscopic Resection
Bibliographic record
Abstract
Background: Tumor consistency is a critical factor in determining the surgical resectability and risks of skull base meningiomas, particularly during endonasal endoscopic approaches (EEA). This study investigated preoperative imaging characteristics to predict factors associated with meningioma tumor consistency. Methods: We retrospectively reviewed the radiologic and operative data on 21 patients with anterior cranial fossa meningiomas who underwent an EEA at our institution. Magnetic resonance imaging characteristics were reviewed and correlated with intraoperative descriptions of tumor consistency according to a published 5-point grading scale (1 = extremely soft and 5 = extremely hard), as well as to the fraction of collagen content based on postoperative histopathology. Results: Using linear regression analysis, the combined tumor characteristics on T1- and T2-weighted images correlated with the tumor consistency ( R = 0.282). Meningiomas that were hyperintense in both T1 and T2 tended to be hard, whereas tumors showing hypointensity were associated with soft consistency. Harder tumor consistency was also correlated with a heterogeneous pattern of enhancement ( R = -0.200). Collagen content data will also be presented. Conclusion: This study suggests that signal hyperintensity on T1, T2 images and heterogeneous gadolinium enhancement images may predict harder meningioma consistency. These findings may be of relevance when selecting the ideal surgical approach (open vs. endoscopic) on a case by case basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".