Tumor Effects on Cerebral White Matter as Characterized by Diffusion Tensor Tractography
Bibliographic record
Abstract
BACKGROUND: Diffusion tensor MRI fiber tractography (DTT) is the first non-invasive in vivo technique for delineating specific white matter (WM) tracts. In cerebral neoplasm, DTT can be used to illustrate the relationship of the tumor with respect to adjacent WM trajectories. METHODS: Fiber tractography was used in this study to assess tumor-induced changes in WM trajectories in three cases of cerebral neoplasm: glioblastoma multiforme, meningioma, and anaplastic astrocytoma. RESULTS: Three patterns of WM alteration were identified: 1) disruption, 2) displacement, and 3) infiltration. Tumor disruption of WM tracts was observed in glioblastoma multiforme, which terminated fibers crossing the corpus callosum. In meningioma, DTT illustrated bulk displacement of the corticospinal tract in the affected hemisphere as well as preservation of the deviated axons. In anaplastic astrocytoma, fiber tracking demonstrated disruption of WM tracts at the tumor origin as well as intact axons through areas of tumor infiltration. CONCLUSIONS: Fiber tracking results correlated with the clinical and histopathological features of the tumor. Larger case series will be required to determine if fiber tracking can add accuracy to existing imaging methods for grading tumors.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".