Diffusivity parameters of diffusion tensor imaging and apparent diffusion coefficient as imaging markers for predicting the treatment response of patients with trigeminal neuralgia
Bibliographic record
Abstract
OBJECTIVE: Trigeminal neuralgia (TN) is facial pain that is usually caused by neurovascular compression syndrome and is characterized by suddenly intense and paroxysmal pain. Radiofrequency lesioning (RFL) is one of the major treatments for TN, but the treatment response for RFL is sometimes inconsistent, and the recurrence of TN is not uncommon. This study aimed to estimate the outcome predictors of TN treated with RFL by using the parameters of diffusion tensor imaging (DTI). METHODS: Fifty-one patients with TN who were treated with RFL were enrolled in the study. MRI was performed in all patients within 1 week before surgery. The visual analog scale was used to evaluate symptom severity at three time points: before, 1 week after, and 3 months after RFL. The involved cisternal segment of the trigeminal nerves was manually selected, and the histograms of each of the diffusivity metrics-including the apparent diffusion coefficient (ADC), fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD)-were measured. The differences in the means, as well as the kurtosis and skewness of each of the diffusivity metrics between the nonrecurrent and recurrent groups, were then analyzed using the Mann-Whitney U-test. RESULTS: There were significantly lower kurtosis values (a broader peak of the distributional curves) for both FA and ADC in the recurrent group (p = 0.0004 and 0.015, respectively), compared to the nonrecurrent group. The kurtoses of AD and RD, as well as the mean and skewness of all other diffusivity metrics, did not show significant differences between the two groups. CONCLUSIONS: The pretreatment diffusivity metrics of DTI and ADC may be feasible imaging biomarkers for predicting the outcome of TN after RFL. A clarification of the kurtosis value of FA and ADC is helpful for determining the prognosis of patients after RFL.
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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) | 0.001 | 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 | 0.000 |
| 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".