Correlation between Cranial Nerve Microstructural Characteristics and Vestibular Schwannoma Tumor Volume
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Vestibular schwannomas are common cerebellopontine angle tumors arising from the vestibulocochlear nerve and can result in cranial nerve dysfunction. Conventional MR imaging does not provide information that could correlate with cranial nerve compression symptoms of hearing loss or imbalance. We used multitensor tractography to evaluate the relationship between the WM microstructural properties of cranial nerves and tumor volume in a cohort of patients with vestibular schwannomas. MATERIALS AND METHODS: A retrospective study was performed in 258 patients with vestibular schwannomas treated at the Gamma Knife clinic at Toronto Western Hospital between 2014 and 2018. 3T MR images were analyzed in 160 surgically naïve patients with unilateral vestibular schwannomas. Multitensor tractography was used to extract DTI-derived metrics (fractional anisotropy and radial, axial, and mean diffusivities of the bilateral facial and vestibulocochlear nerves [cranial nerves VII/VIII]). ROIs were placed in the transition between cisternal and intracanalicular segments, and images were analyzed using the eXtended Streamline Tractography reconstruction method. Diffusion metrics were correlated with 3D tumor volume derived from the Gamma Knife clinic. RESULTS: < .01). CONCLUSIONS: Multitensor tractography allows the quantification of cranial nerve VII and VIII WM microstructural alterations in patients with vestibular schwannomas. Our findings support the hypothesis that tumor volume may cause microstructural alterations of the affected cranial nerves VII and VIII. This type of advanced imaging may represent a possible avenue to correlate diffusivities with cranial nerve function.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".