Diffusion tensor imaging for assessment of microstructural changes associate with treatment outcome at one-year after radiofrequency Rhizotomy in trigeminal neuralgia.

BACKGROUND: Trigeminal neuralgia (TN) is characterized by facial pain that may be sudden, intense, and recurrent. Neurosurgical interventions, such as radiofrequency rhizotomy, can relieve TN pain, but their mechanisms and effects are unknown. The aim of the present study was to investigate the microstructural tissue changes of the trigeminal nerve (TGN) in patients with TN after they underwent radiofrequency rhizotomy. METHODS: Thirty-seven patients with TN were recruited, and diffusion tensor imaging was obtained before and two weeks after radiofrequency rhizotomy. By manually selecting the cisternal segment of the TGN, we measured the volume of the TGN, fractional anisotropy (FA), apparent diffusion coefficient (ADC), axial diffusivity (AD), and radial diffusivity (RD). The TGN volume and mean value of the DTI metrics of the post-rhizotomy lesion side were compared with those of the normal side and those of the pre-rhizotomy lesion side, and they were correlated to the post-rhizotomy visual analogue scale (VAS) pain scores after a one-year follow-up. RESULTS: The alterations before and after rhizotomy showed a significantly increased TGN volume and FA, and a decreased ADC, AD, and RD. The post-rhizotomy lesion side showed a significantly decreased TGN volume, FA, and AD compared with the normal side; however, no significant difference in the ADC and RD were found between the groups. The TGN volume was significantly higher in the non-responders than in the responders (P = 0.016). CONCLUSION: Our results may reflect that the effects of radiofrequency rhizotomy in TN patients include axonal damage with perineural edema and that prolonged swelling associated with recurrence might be predicted by MRI images. Further studies are necessary to understand how DTI metrics can quantitatively represent the pathophysiology of TN and to examine the application of DTI in the treatment of TN.

Diffusivity parameters of diffusion tensor imaging and apparent diffusion coefficient as imaging markers for predicting the treatment response of patients with trigeminal neuralgia.

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.

Automatic Machine-Learning-Based Outcome Prediction in Patients With Primary Intracerebral Hemorrhage.

Background: A predictive model can provide physicians, relatives, and patients the accurate information regarding the severity of disease and its predicted outcome. In this study, we used an automated machine-learning-based approach to construct a prognostic model to predict the functional outcome in patients with primary intracerebral hemorrhage (ICH). Methods: We retrospectively collected data on demographic characteristics, laboratory studies and imaging findings of 333 patients with primary ICH. The functional outcomes at the 1st and 6th months after ICH were defined by the modified Rankin scale. All of the attributes were used for preprocessing and for automatic model selection with Automatic Waikato Environment for Knowledge Analysis. Confusion matrix and areas under the receiver operating characteristic curves (AUC) were used to test the predictive performance. Results: Among the models tested, the random forest provided the best predictive performance for functional outcome. The overall accuracy for predicting the 1st month outcome was 83.1%, with 77.4% sensitivity and 86.9% specificity, and the AUC was 0.899. The overall accuracy for predicting the 6th month outcome was 83.9%, with 72.5% sensitivity and 90.6% specificity, and the AUC was 0.917. Conclusions: Using an automatic machine learning technique to predict functional outcome after ICH is feasible, and the random forest model provides the best predictive performance across all tested models. This prediction model may provide information regarding functional outcome for clinicians that will help provide appropriate medical care for patients and information for their caregivers.