Quantitative assessment of secondary white matter injury in the visual pathway by pituitary adenomas: a multimodal study at 7-Tesla MRI
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate microstructural damage caused by pituitary macroadenomas by performing probabilistic tractography of the optic tracts and radiations using 7-T diffusion-weighted MRI (DWI). These imaging findings were correlated with neuro-ophthalmological results to assess the utility of ultra-high-field MRI for objective evaluation of damage to the anterior and posterior visual pathways. METHODS: Probabilistic tractography employing 7-T DWI was used to reconstruct the optic tracts and radiations in 18 patients with adenomas and in 16 healthy volunteers. Optic chiasm compression was found in 66.7% of the patients and visual defects in 61.1%. Diffusion indices were calculated along the projections and correlated with tumor volumes and results from neuro-ophthalmological examinations. Primary visual cortical thicknesses were also assessed. RESULTS: Fractional anisotropy was reduced by 21.9% in the optic tracts (p < 0.001) and 17.7% in the optic radiations (p < 0.001) in patients with adenomas. Patients showed an 8.5% increase in mean diffusivity of optic radiations compared with healthy controls (p < 0.001). Primary visual cortical thickness was reduced in adenoma patients. Diffusion indices of the visual pathway showed significant correlations with neuro-ophthalmological examination findings. CONCLUSIONS: Imaging-based quantification of secondary neuronal damage from adenomas strongly correlated with neuro-ophthalmological findings. Diffusion characteristics enabled by ultra-high-field DWI may allow preoperative characterization of visual pathway damage in patients with chiasmatic compression and may inform prognosis for vision recoverability.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".