MRI Texture Analysis Reveals Brain Abnormalities in Medically Refractory Trigeminal Neuralgia

Evaluation of Basal Ganglia in Paediatric Patients With Primary Nephrotic Syndrome by Brain Magnetic Resonance Histogram Analysis

BACKGROUND

Primary nephrotic syndrome is an important cause of chronic renal failure in childhood. Important neuronal complications may develop during the disease.

AIMS

This study aims to demonstrate basal ganglia involvement in children with nephrotic syndrome by texture analysis.

METHODS

Brain MRI images of 22 paediatric patients with primary nephrotic syndrome and 40 healthy children of similar age groups were analysed. Brain MRI T2-weighted images were extracted from the thalamus, lentiform nucleus and nucleus caudatus and texture analysis was performed.

RESULTS

The images of 22 children with primary nephrotic syndrome and 40 children in the control group were evaluated. There were no notable distinctions identified in terms of age and gender between the patient and control groups (P value 0,410; 0,516, respectively). Accordingly, a significant difference was found between mean, 1.P, 10.P, 50.P, 90.P, 99.P values of histogram parameters obtained from thalamus (P values were 0.001; 0.000; 0.001; 0.002; 0.004; 0.009, respectively). A significant difference was found between mean, 1.P, 10.P, 50.P, 90.P, 99.P values of histogram parameters obtained from lentiform nuclei (P values were 0.031; 0.019; 0.006; 0.006; 0.003; 0.003; 0.001; 0.002, respectively). A significant difference was found between the mean, 1.P, 10.P, 50.P, 90.P, 99.P values of the histogram parameters obtained from the nucleus caudatus (P values 0,002; 0,005; 0,002; 0,002; 0,002; 0,003; 0,003, respectively).

CONCLUSION

Texture analysis may be helpful in demonstrating brain parenchymal involvement in paediatric patients with primary nephrotic syndrome by showing changes that are not recognised on conventional images.

Predicting long-term outcomes in patients with classical trigeminal neuralgia following microvascular decompression with an MRI-based radiomics nomogram: a multicentre study

OBJECTIVES

This study aimed to develop a clinical-radiomics nomogram to predict the long-term outcomes of patients with classical trigeminal neuralgia (CTN) following microvascular decompression (MVD).

MATERIALS AND METHODS

This retrospective study included 455 patients with CTN who underwent MVD from three independent institutions A total of 2030 radiomics features from the cistern segment of the trigeminal nerve were extracted computationally from the three-dimensional steady-state free precession and three-dimensional time-of-flight magnetic resonance angiography sequences. Using the least absolute shrinkage and selection operator regression, 16 features were chosen to develop radiomics signatures. A clinical-radiomics nomogram was subsequently developed in the development cohort of 279 patients via multivariate Cox regression. The predictive performance and clinical application of the nomogram were assessed in an external cohort consisting of 176 patients.

RESULTS

Sixteen highly outcome-related radiomics features extracted from multisequence images were used to construct the radiomics model, with concordance indices (C-index) of 0.804 and 0.796 in the development and test cohorts, respectively. Additionally, a clinical-radiomics nomogram was developed by incorporating both radiomics features and clinical characteristics (i.e., pain type and degree of neurovascular compression) and yielded higher C-indices of 0.865 and 0.834 in the development and test cohorts, respectively. K‒M survival analysis indicated that the nomogram successfully stratified patients with CTN into high-risk and low-risk groups for poor outcomes (hazard ratio: 37.18, p < 0.001).

CONCLUSION

Our study findings indicated that the clinical-radiomics nomogram exhibited promising performance in accurately predicting long-term pain outcomes following MVD.

CLINICAL RELEVANCE STATEMENT

This model had the potential to aid clinicians in making well-informed decisions regarding the treatment of patients with CTN.

KEY POINTS

Trigeminal neuralgia recurs in about one-third of patients after undergoing MVD. The clinical-radiomics nomogram stratified patients into high- and low-risk groups for poor surgical outcomes. Using this nomogram could better inform patients of recurrence risk and allow for discussion of alternative treatments.

Analyzing the risk factors of unilateral trigeminal neuralgia under neurovascular compression

Background

This study aimed to explore the risk factors and potential causes of unilateral classical or idiopathic trigeminal neuralgia (C-ITN) by comparing patients and healthy controls (HCs) with neurovascular compression (NVC) using machine learning (ML).

Methods

A total of 84 C-ITN patients and 78 age- and sex-matched HCs were enrolled. We assessed the trigeminal pons angle and identified the compressing vessels and their location and severity. Machine learning was employed to analyze the cisternal segment of the trigeminal nerve (CN V).

Results

Among the C-ITN patients, 53 had NVC on the unaffected side, while 25 HCs exhibited bilateral NVC, and 24 HCs showed unilateral NVC. By comparing the cisternal segment of CN V between C-ITN patients on the affected side and HCs with NVC, we identified the side of NVC, the compressing vessel, and certain texture features as risk factors for C-ITN. Additionally, four texture features differed in the structure of the cisternal segment of CN V between C-ITN patients on the unaffected side and HCs with NVC.

Conclusion

Our findings suggest that the side of NVC, the compressing vessel, and the microstructure of the cisternal segment of CN V are associated with the risk of C-ITN. Furthermore, microstructural changes observed in the cisternal segment of CN V on the unaffected side of C-ITN patients with NVC indicate possible indirect effects on the CN V to some extent.

Independent association of the Meckel's cave with trigeminal neuralgia and development of a screening tool

PURPOSE

To 1) investigate the association of the properties of the Meckel's cave (MC) with TN occurrence (i.e., affected vs. unaffected nerves) and whether such association was independent of neurovascular contact (NVC); and 2) develop an objective screening tool for TN.

MATERIALS AND METHODS

Two hundred and nineteen trigeminal nerves were included. (The severity of) NVC was identified for individual nerve, and a set of 107 radiomic features were extracted to characterize various properties of each MC. Both procedures were primarily based on magnetic resonance imaging sequences. A radiomic score (Rad-score) was constructed for each MC to integrate the features associated with TN occurrence. Independent t-test and logistic regression were conducted to assess the association and develop the screening tool mentioned above.

RESULTS

Twelve features were selected to build the Rad-score, with the Inverse Difference Moment Normalized (IDMN) having the greatest weight. The Rad-score was significantly (p ≤ 0.05) higher in the affected compared to the unaffected nerves, irrespective of NVC. The Rad-score and NVC were incorporated in the regression model/screening tool, which demonstrated an acceptable discriminating ability (C-statistic = 0.84).

CONCLUSION

This study has identified a potential association of the properties/features of the MC with TN occurrence, probably involving the demyelination and axonal injury of the trigeminal ganglion within the MC as suggested by the IDMN. Such association may be independent of NVC. This finding may provide new insight into the etiology and/or pathophysiology of TN. The screening tool, which demonstrated an acceptable discriminating ability, may contribute to an improvement in its diagnosis.

Risk Factors for Unilateral Trigeminal Neuralgia Based on Machine Learning

Purpose

Neurovascular compression (NVC) is considered as the main factor leading to the classical trigeminal neuralgia (CTN), and a part of idiopathic TN (ITN) may be caused by NVC (ITN-nvc). This study aimed to explore the risk factors for unilateral CTN or ITN-nvc (UC-ITN), which have bilateral NVC, using machine learning (ML).

Methods

A total of 89 patients with UC-ITN were recruited prospectively. According to whether there was NVC on the unaffected side, patients with UC-ITN were divided into two groups. All patients underwent a magnetic resonance imaging (MRI) scan. The bilateral cisternal segment of the trigeminal nerve was manually delineated, which avoided the offending vessel (Ofv), and the features were extracted. Dimensionality reduction, feature selection, model construction, and model evaluation were performed step-by-step.

Results

Four textural features with greater weight were selected in patients with UC-ITN without NVC on the unaffected side. For UC-ITN patients with NVC on the unaffected side, six textural features with greater weight were selected. The textural features (rad_score) showed significant differences between the affected and unaffected sides (p < 0.05). The nomogram model had optimal diagnostic power, and the area under the curve (AUC) in the training and validation cohorts was 0.76 and 0.77, respectively. The Ofv and rad_score were the risk factors for UC-ITN according to nomogram.

Conclusion

Besides NVC, the texture features of trigeminal-nerve cisternal segment and Ofv were also the risk factors for UC-ITN. These findings provided a basis for further exploration of the microscopic etiology of UC-ITN.

Spatiotemporal dynamics of brain function during the natural course in a dental pulp injury model

Purpose

Toothache, a common disorder afflicting most people, shows distinct features at different clinical stages. This study aimed to depict metabolic changes in brain and investigate the potential mechanism involved in the aberrant affective behaviors during the natural process of toothache.

Methods

We investigated the spatiotemporal patterns of brain function during the natural course of toothache in a rat model of dental pulp injury (DPI) by using positron emission tomography (PET).

Results

Glucose metabolism peaked on the 3rd day and gradually decreased in several brain regions after DPI, which was in line with the behavioral and histological results. PET imaging showed that visual pathway was involved in the regulation of toothache. Meanwhile, the process of emotional regulation underlying toothache was mediated by N-methyl-D-aspartic receptor subunit 2B (NR2B) in the caudal anterior cingulate cortex (cACC).

Conclusion

Our results revealed the spatiotemporal neurofunctional patterns during toothache process and preliminarily elucidated the role of NR2B in cACC in the regulation of toothache-related affective behaviors.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00259-022-05764-2.

Identifying symptomatic trigeminal nerves from MRI in a cohort of trigeminal neuralgia patients using radiomics

INTRODUCTION

Trigeminal neuralgia (TN) is a devastating neuropathic condition. This work tests whether radiomics features derived from MRI of the trigeminal nerve can distinguish between TN-afflicted and pain-free nerves.

METHODS

3D T1- and T2-weighted 1.5-Tesla MRI volumes were retrospectively acquired for patients undergoing stereotactic radiosurgery to treat TN. A convolutional U-net deep learning network was used to segment the trigeminal nerves from the pons to the ganglion. A total of 216 radiomics features consisting of image texture, shape, and intensity were extracted from each nerve. Within a cross-validation scheme, a random forest feature selection method was used, and a shallow neural network was trained using the selected variables to differentiate between TN-affected and non-affected nerves. Average performance over the validation sets was measured to estimate generalizability.

RESULTS

A total of 134 patients (i.e., 268 nerves) were included. The top 16 performing features extracted from the masks were selected for the predictive model. The average validation accuracy was 78%. The validation AUC of the model was 0.83, and sensitivity and specificity were 0.82 and 0.76, respectively.

CONCLUSION

Overall, this work suggests that radiomics features from MR imaging of the trigeminal nerves correlate with the presence of pain from TN.