Three-Dimensional In Vivo Modeling of Vestibular Schwannomas and Surrounding Cranial Nerves With Diffusion Imaging Tractography
Bibliographic record
Abstract
BACKGROUND: Preservation of cranial nerves (CNs) is of paramount concern in the treatment of vestibular schwannomas, particularly in large tumors with thinned and distorted CN fibers. However, imaging of the CN fibers surrounding vestibular schwannomas has been limited with 2-dimensional imaging alone. OBJECTIVE: To assess whether tractography of the CN combined with anatomic magnetic resonance imaging of the tumor can provide superior 3-dimensional (3D) visualization of tumor/CN complexes. METHODS: Magnetic resonance imaging at 3 T, including diffusion tensor imaging and anatomic images, were analyzed in 3 subjects with vestibular schwannomas using 3D Slicer software. The diffusion tensor images were used to track the courses of trigeminal, abducens, facial, and vestibulocochlear nerves. The anatomic images were used to model the 3D volume reconstruction of the tumor. The 2 sets of images were then superimposed through the use of linear registration. RESULTS: Combined 3D tumor modeling and CN tractography can effectively and consistently reconstruct the 3D spatial relationship of CN/tumor complexes and allows superior visualization compared with 2-dimensional imaging. Lateral and superior distortion of the trigeminal nerve was observed in all cases. The position of the facial nerve was primarily anteriorly and inferiorly. The gasserian ganglion and early postganglionic branches could also be visualized. CONCLUSION: Tractography and anatomic imaging were successfully combined to demonstrate the precise location of surrounding CN fibers. This technique can be useful in both neuronavigation and radiosurgical planning. Because knowledge of the course of these fibers is of important clinical interest, implementation of this technique may help decrease injury to CNs during treatment of these lesions.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".