Texture Analysis of T2-Weighted MR Images to Assess Acute Inflammation in Brain MS Lesions

Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques

INTRODUCTION

Gadolinium-based T1-weighted MRI sequence is the gold standard for the detection of active multiple sclerosis (MS) lesions. The performance of machine learning (ML) and deep learning (DL) models in the classification of active and non-active MS lesions from the T2-weighted MRI images has been investigated in this study.

METHODS

107 Features of 75 active and 100 non-active MS lesions were extracted by using SegmentEditor and Radiomics modules of 3D slicer software. Sixteen ML and one sequential DL models were created using the 5-fold cross-validation method and each model with its special optimized parameters trained using the training-validation datasets. Models' performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score.

RESULTS

The sequential DL model achieved the highest AUC of 95.60% on the test dataset, demonstrating its superior ability to distinguish between active and non-active plaques. Among traditional ML models, the Hybrid Gradient Boosting Classifier (HGBC) demonstrated a commendable test AUC of 86.75%, while the Gradient Boosting Classifier (GBC) excelled in cross-validation with an AUC of 87.92%.

CONCLUSION

The performance of sixteen ML and one sequential DL models in the classification of active and non-active MS lesions was evaluated. The results of the study highlight the effectiveness of sequential DL approach and ensemble methods in achieving robust predictive performance, underscoring their potential applications in classifying MS plaques.

Alleviating rheumatoid arthritis with a photo-pharmacotherapeutic glycan-integrated nanogel complex for advanced percutaneous delivery

The prospective of percutaneous drug delivery (PDD) mechanisms to address the limitations of oral and injectable treatment for rheumatoid arthritis (RA) is increasing. These limitations encompass inadequate compliance among patients and acute gastrointestinal side effects. However, the skin's intrinsic layer can frequently hinder the percutaneous dispersion of RA medications, thus mitigating the efficiency of drug delivery. To circumvent this constraint, we developed a strontium ranelate (SrR)-loaded alginate (ALG) phototherapeutic hydrogel to assess its effectiveness in combating RA. Our studies revealed that this SrR-loaded ALG hydrogel incorporating photoelectrically responsive molybdenum disulfide nanoflowers (MoS2 NFs) and photothermally responsive polypyrrole nanoparticles (Ppy NPs) to form ALG@SrR-MoS2 NFs-Ppy NPs demonstrated substantial mechanical strength, potentially enabling delivery of hydrophilic therapeutic agents into the skin and significantly impeding the progression of RA. Comprehensive biochemical, histological, behavioral, and radiographic analyses in an animal model of zymosan-induced RA demonstrated that the application of these phototherapeutic ALG@SrR-MoS2 NFs-Ppy NPs effectively reduced inflammation, increased the presence of heat shock proteins, regulatory cluster of differentiation M2 macrophages, and alleviated joint degeneration associated with RA. As demonstrated by our findings, treating RA and possibly other autoimmune disorders with this phototherapeutic hydrogel system offers a distinctive, highly compliant, and therapeutically efficient method.

Use of gadolinium-based contrast agents in multiple sclerosis: a review by the ESMRMB-GREC and ESNR Multiple Sclerosis Working Group

Magnetic resonance imaging (MRI) is the most sensitive technique for detecting inflammatory demyelinating lesions in multiple sclerosis (MS) and plays a crucial role in diagnosis and monitoring treatment effectiveness, and for predicting the disease course. In clinical practice, detection of MS lesions is mainly based on T2-weighted and contrast-enhanced T1-weighted sequences. Contrast-enhancing lesions (CEL) on T1-weighted sequences are related to (sub)acute inflammation, while new or enlarging T2 lesions reflect the permanent footprint from a previous acute inflammatory demyelinating event. These two types of MRI features provide redundant information, at least in regular monitoring of the disease. Due to the concern of gadolinium deposition after repetitive injections of gadolinium-based contrast agents (GBCAs), scientific organizations and regulatory agencies in Europe and North America have proposed that these contrast agents should be administered only if clinically necessary. In this article, we provide data on the mode of action of GBCAs in MS, the indications of the use of these agents in clinical practice, their value in MS for diagnostic, prognostic, and monitoring purposes, and their use in specific populations (children, pregnant women, and breast-feeders). We discuss imaging strategies that achieve the highest sensitivity for detecting CELs in compliance with the safety regulations established by different regulatory agencies. Finally, we will briefly discuss some alternatives to the use of GBCA for detecting blood-brain barrier disruption in MS lesions. CLINICAL RELEVANCE STATEMENT: Although use of GBCA at diagnostic workup of suspected MS is highly valuable for diagnostic and prognostic purposes, their use in routine monitoring is not mandatory and must be reduced, as detection of disease activity can be based on the identification of new or enlarging lesions on T2-weighted images. KEY POINTS: • Both the EMA and the FDA state that the use of GBCA in medicine should be restricted to clinical scenarios in which the additional information offered by the contrast agent is required. • The use of GBCA is generally recommended in the diagnostic workup in subjects with suspected MS and is generally not necessary for routine monitoring in clinical practice. • Alternative MRI-based approaches for detecting acute focal inflammatory MS lesions are not yet ready to be used in clinical practice.

Radiomics derived from T2-FLAIR: the value of 2- and 3-classification tasks for different lesions in multiple sclerosis

Background

White matter (WM) lesions can be classified into contrast enhancement lesions (CELs), iron rim lesions (IRLs), and non-iron rim lesions (NIRLs) based on different pathological mechanism in relapsing-remitting multiple sclerosis (RRMS). The application of radiomics established by T2-FLAIR to classify WM lesions in RRMS is limited, especially for 3-class classification among CELs, IRLs, and NIRLs.

Methods

A total of 875 WM lesions (92 CELs, 367 IRLs, 416 NIRLs) were included in this study. The 2-class classification was only performed between IRLs and NIRLs. For the 2- and 3-class classification tasks, all the lesions were randomly divided into training and testing sets with a ratio of 8:2. We used least absolute shrinkage and selection operator (LASSO), reliefF algorithm, and mutual information (MI) for feature selection, then eXtreme gradient boosting (XGBoost), random forest (RF), and support vector machine (SVM) were used to establish discrimination models. Finally, the area under the curve (AUC), accuracy, sensitivity, specificity, and precision were used to evaluate the performance of the models.

Results

For the 2-class classification model, LASSO classifier with RF model showed the best discrimination performance with the AUC of 0.893 (95% CI: 0.838-0.942), accuracy of 0.813, sensitivity of 0.833, specificity of 0.781, and precision of 0.851. However, the 3-class classification model of LASSO with XGBoost displayed the highest performance with the AUC of 0.920 (95% CI: 0.887-0.950), accuracy of 0.796, sensitivity of 0.839, specificity of 0.881, and precision of 0.846.

Conclusions

Radiomics models based on T2-FLAIR images have the potential for discriminating among CELs, IRLs, and NIRLs in RRMS.

Independent and reproducible hippocampal radiomics biomarkers for multisite multiple sclerosis and neuromyelitis optica spectrum disorders

OBJECTIVE

To investigate the abnormal radiomics features of the hippocampus in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) and to explore the clinical implications of these features.

METHODS

752 participants were recruited in this retrospective multicenter study (7 centers), which included 236 MS, 236 NMOSD, and 280 normal controls (NC). Radiomics features of each side of the hippocampus were extracted, including intensity, shape, texture, and wavelet features (N = 431). To identify the variations in these features, two-sample t-tests were performed between the NMOSD vs. NC, MS vs. NC, and NMOSD vs. MS groups at each site. The statistical results from each site were then integrated through meta-analysis. To investigate the clinical significance of the hippocampal radiomics features, we conducted further analysis to examine the correlations between these features and clinical measures such as Expanded Disability Status Scale (EDSS), Brief Visuospatial Memory Test (BVMT), California Verbal Learning Test (CVLT), and Paced Auditory Serial Addition Task (PASAT).

RESULTS

Compared with NC, patients with MS exhibited significant differences in 78 radiomics features (P < 0.05/862), with the majority of these being texture features. Patients with NMOSD showed significant differences in 137 radiomics features (P < 0.05/862), most of which were intensity features. The difference between MS and NMOSD patients was observed in 47 radiomics features (P < 0.05/862), mainly texture features. In patients with MS and NMOSD, the most significant features related to the EDSS were intensity and textural features, and the most significant features related to the PASAT were intensity features. Meanwhile, both disease groups observed a weak correlation between radiomics data and BVMT.

CONCLUSION

Variations in the microstructure of the hippocampus can be detected through radiomics, offering a new approach to investigating the abnormal pattern of the hippocampus in MS and NMOSD.

Bioinspired and self-restorable alginate-tyramine hydrogels with plasma reinforcement for arthritis treatment

Long-standing administration of disease-modifying antirheumatic drugs confirms their clinical value for managing rheumatoid arthritis (RA). Nevertheless, there are emergent worries over unwanted adverse risks of systemic drug administration. Hence, a novel strategy that can be used in a drug-free manner while diminishing side effects is immediately needed, but challenges persist in the therapy for RA. To this end, herein we conjugated tyramine (TYR) with alginate (ALG) to form ALG-TYR and then treated it for 5 min with oxygen plasma (ALG-TYR + P/5 min). It was shown that the ALG-TYR + P/5 min hydrogel exhibited favorable viscoelastic, morphological, mechanical, biocompatible, and cellular heat-shock protein amplification behaviors. A thorough physical and structural analysis was conducted on the ALG-TYR + P/5 min hydrogel, revealing favorable physical characteristics and uniform porous structural features within the hydrogel. Moreover, ALG-TYR + P/5 min not only effectively inhibited inflammation of RA but also potentially regulated lesion immunity. Once ALG-TYR + P/5 min was intra-articularly administered to joints of rats with zymosan-induced arthritis, we observed that ALG-TYR + P/5 min could ameliorate syndromes of RA joint. This bioinspired and self-restorable ALG-TYR + P/5 min hydrogel can thus serve as a promising system to provide prospective outcomes to potentiate RA therapy.

Investigating the Ability of Radiomics Features for Diagnosis of the Active Plaque of Multiple Sclerosis Patients

Background

Multiple sclerosis (MS) is the most common non-traumatic disabling disease.

Objective

The aim of this study is to investigate the ability of radiomics features for diagnosing active plaques in patients with MS from T2 Fluid Attenuated Inversion Recovery (FLAIR) images.

Material and Methods

In this experimental study, images of 82 patients with 122 MS lesions were investigated. Boruta and Relief algorithms were used for feature selection on the train data set (70%). Four different classifier algorithms, including Multi-Layer Perceptron (MLP), Gradient Boosting (GB), Decision Tree (DT), and Extreme Gradient Boosting (XGB) were used as classifiers for modeling. Finally, Performance metrics were obtained on the test data set (30%) with 1000 bootstrap and 95% confidence intervals (95% CIs).

Results

A total of 107 radiomics features were extracted for each lesion, of which 7 and 8 features were selected by the Relief method and Boruta method, respectively. DT classifier had the best performance in the two feature selection algorithms. The best performance on the test data set was related to Boruta-DT with an average accuracy of 0.86, sensitivity of 1.00, specificity of 0.84, and Area Under the Curve (AUC) of 0.92 (95% CI: 0.92-0.92).

Conclusion

Radiomics features have the potential for diagnosing MS active plaque by T2 FLAIR image features. Additionally, choosing the feature selection and classifier algorithms plays an important role in the diagnosis of active plaque in MS patients. The radiomics-based predictive models predict active lesions accurately and non-invasively.

Single-timepoint low-dimensional characterization and classification of acute versus chronic multiple sclerosis lesions using machine learning

Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease characterized by the appearance of focal lesions across the central nervous system. The discrimination of acute from chronic MS lesions may yield novel biomarkers of inflammatory disease activity which may support patient management in the clinical setting and provide endpoints in clinical trials. On a single timepoint and in the absence of a prior reference scan, existing methods for acute lesion detection rely on the segmentation of hyperintense foci on post-gadolinium T1-weighted magnetic resonance imaging (MRI), which may underestimate recent acute lesion activity. In this paper, we aim to improve the sensitivity of acute MS lesion detection in the single-timepoint setting, by developing a novel machine learning approach for the automatic detection of acute MS lesions, using single-timepoint conventional non-contrast T1- and T2-weighted brain MRI. The MRI input data are supplemented via the use of a convolutional neural network generating "lesion-free" reconstructions from original "lesion-present" scans using image inpainting. A multi-objective statistical ranking module evaluates the relevance of textural radiomic features from the core and periphery of lesion sites, compared within "lesion-free" versus "lesion-present" image pairs. Then, an ensemble classifier is optimized through a recursive loop seeking consensus both in the feature space (via a greedy feature-pruning approach) and in the classifier space (via model selection repeated after each pruning operation). This leads to the identification of a compact textural signature characterizing lesion phenotype. On the patch-level task of acute versus chronic MS lesion classification, our method achieves a balanced accuracy in the range of 74.3-74.6% on fully external validation cohorts.

Post-concussive mTBI in Student Athletes: MRI Features and Machine Learning

Purpose: To determine and characterize the radiomics features from structural MRI (MPRAGE) and Diffusion Tensor Imaging (DTI) associated with the presence of mild traumatic brain injuries on student athletes with post-concussive syndrome (PCS).

Material and Methods: 122 student athletes (65 M, 57 F), median (IQR) age 18.8 (15–20) years, with a mixed level of play and sports activities, with a known history of concussion and clinical PCS, and 27 (15 M, 12 F), median (IQR) age 20 (19, 21) years, concussion free athlete subjects were MRI imaged in a clinical MR machine. MPRAGE and DTI-FA and DTI-ADC images were used to extract radiomic features from white and gray matter regions within the entire brain (2 ROI) and the eight main lobes of the brain (16 ROI) for a total of 18 analyzed regions. Radiomic features were divided into five different data sets used to train and cross-validate five different filter-based Support Vector Machines. The top selected features of the top model were described. Furthermore, the test predictions of the top four models were ensembled into a single average prediction. The average prediction was evaluated for the association to the number of concussions and time from injury.

Results: Ninety-one PCS subjects passed inclusion criteria (91 Cases, 27 controls). The average prediction of the top four models had a sensitivity of 0.80, 95% CI: [0.71, 0.88] and specificity of 0.74 95%CI [0.54, 0.89] for distinguishing subjects from controls. The white matter features were strongly associated with mTBI, while the whole-brain analysis of gray matter showed the worst association. The predictive index was significantly associated with the number of concussions (p < 0.0001) and associated with the time from injury (p < 0.01).

Conclusion: MRI Radiomic features are associated with a history of mTBI and they were successfully used to build a predictive machine learning model for mTBI for subjects with PCS associated with a history of one or more concussions.

Attention-Based Multi-Scale Generative Adversarial Network for synthesizing contrast-enhanced MRI

In clinical practice, about 35% of MRI scans are enhanced with Gadolinium - based contrast agents (GBCAs) worldwide currently. Injecting GBCAs can make the lesions much more visible on contrast-enhanced scans. However, the injection of GBCAs is high-risk, time-consuming, and expensive. Utilizing a generative model such as an adversarial network (GAN) to synthesize the contrast-enhanced MRI without injection of GBCAs becomes a very promising alternative method. Due to the different features of the lesions in contrast-enhanced images while the single-scale feature extraction capabilities of the traditional GAN, we propose a new generative model that a multi-scale strategy is used in the GAN to extract different scale features of the lesions. Moreover, an attention mechanism is also added in our model to learn important features automatically from all scales for better feature aggregation. We name our proposed network with an attention-based multi-scale contrasted-enhanced-image generative adversarial network (AMCGAN). We examine our proposed AMCGAN on a private dataset from 382 ankylosing spondylitis subjects. The result shows our proposed network can achieve state-of-the-art in both visual evaluations and quantitative evaluations than traditional adversarial training.Clinical Relevance-This study provides a safe, convenient, and inexpensive tool for the clinical practices to get contrast-enhanced MRI without injection of GBCAs.

A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis

BACKGROUND AND PURPOSE

Conventional MR imaging explains only a fraction of the clinical outcome variance in multiple sclerosis. We aimed to evaluate machine learning models for disability prediction on the basis of radiomic, volumetric, and connectivity features derived from routine brain MR images.

MATERIALS AND METHODS

In this retrospective cross-sectional study, 3T brain MR imaging studies of patients with multiple sclerosis, including 3D T1-weighted and T2-weighted FLAIR sequences, were selected from 2 institutions. T1-weighted images were processed to obtain volume, connectivity score (inferred from the T2 lesion location), and texture features for an atlas-based set of GM regions. The site 1 cohort was randomly split into training (n = 400) and test (n = 100) sets, while the site 2 cohort (n = 104) constituted the external test set. After feature selection of clinicodemographic and MR imaging-derived variables, different machine learning algorithms predicting disability as measured with the Expanded Disability Status Scale were trained and cross-validated on the training cohort and evaluated on the test sets. The effect of different algorithms on model performance was tested using the 1-way repeated-measures ANOVA.

RESULTS

The selection procedure identified the 9 most informative variables, including age and secondary-progressive course and a subset of radiomic features extracted from the prefrontal cortex, subcortical GM, and cerebellum. The machine learning models predicted disability with high accuracy (r approaching 0.80) and excellent intra- and intersite generalizability (r ≥ 0.73). The machine learning algorithm had no relevant effect on the performance.

CONCLUSIONS

The multidimensional analysis of brain MR images, including radiomic features and clinicodemographic data, is highly informative of the clinical status of patients with multiple sclerosis, representing a promising approach to bridge the gap between conventional imaging and disability.

Prediction of unenhanced lesion evolution in multiple sclerosis using radiomics-based models: a machine learning approach

BACKGROUND

The volume change of multiple sclerosis (MS) lesion is related to its activity and can be used to assess disease progression. Therefore, the purpose of this study was to develop radiomics models for predicting the evolution of unenhanced MS lesions by using different kinds of machine learning algorithms and explore the optimal model.

METHODS

In this prospective observation, 45 follow-up MR images obtained in 36 patients with MS (mean age 32.53±10.91; 23 women, 13 men) were evaluated. The lesions will be defined as interval activity and interval inactivity, respectively, based on the percentage of enlargement or reduction of the lesion >20% in the follow-up MR images. We extracted radiomic features of lesions on FLAIR images, and used recursive feature elimination (RFE), ReliefF algorithm and least absolute shrinkage and selection operator (LASSO) for feature selection, then three classification models including logistic regression, random forest and support vector machine (SVM) were used to build predictive models. The performance of the models were evaluated based on the sensitivity, specificity, precision, negative predictive value (NPV) and receiver operating characteristic curve (ROC) curves analyses.

RESULTS

135 interval inactivity lesions and 110 interval activity lesions were registered in our study. A total of 972 radiomics features were extracted, of which 265 were robust. The consistency and effectiveness of model performance were compared and verified by different combinations of feature selection and machine learning methods in different K-fold cross-validation strategies where K ranges from 5 to 10, thus demonstrating the stability and robustness. SVM classifier with ReliefF algorithm had the best prediction performance with an average accuracy of 0.827, sensitivity of 0.809, specificity of 0.841, precision of 0.921, NPV of 0.948 and the areas under the ROC curves (AUC) of 0.857 (95% CI: 0.812-0.902) in the cohorts.

CONCLUSION

The results demonstrated that the radiomics-based machine learning model has potential in predicting the evolution of MS lesions.

Quantitative MRI texture analysis in chronic active multiple sclerosis lesions

OBJECTIVE

Recently, there has been an increasing interest in "chronic enlarging" or "chronic active" multiple sclerosis (MS) lesions that are associated with clinical disability. However, investigation of dynamic lesion volume changes requires longitudinal MRI data from two or more time points. The aim of this study was to investigate the application of texture analysis (TA) on baseline T1-weighted 3D magnetization-prepared rapid acquisition gradient-echo (MPRAGE) images to differentiate chronic active from chronic stable MS lesions.

MATERIAL AND METHODS

To identify chronic active lesions as compared to non-enhancing stable lesions, two MPRAGE datasets acquired on a 3 T MRI at baseline and after 12 months follow-up were applied to the Voxel-Guided Morphometry (VGM) algorithm. TA was performed on the baseline MPRAGE images, 36 texture features were extracted for each lesion.

RESULTS

Overall, 374 chronic MS lesions (155 chronic active and 219 chronic stable lesions) from 60 MS patients were included in the final analysis. Multiple texture features including "DISCRETIZED_HISTO_Energy", "GLCM_Energy", "GLCM_Contrast" and "GLCM_Dissimilarity" were significantly higher in chronic active as compared to chronic stable lesions. Partial least squares regression yielded an area under the curve of 0.7 to differentiate both lesion types.

CONCLUSION

Our results suggest that multiple texture features extracted from MPRAGE images indicate higher intralesional heterogeneity, however they demonstrate only a fair accuracy to differentiate chronic active from chronic stable MS lesions.

Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions

OBJECTIVES

To evaluate the diagnostic performance of texture analysis (TA) applied on non-contrast-enhanced susceptibility-weighted imaging (SWI) to differentiate acute (enhancing) from chronic (non-enhancing) multiple sclerosis (MS) lesions.

METHODS

We analyzed 175 lesions from 58 patients with relapsing-remitting MS imaged on a 3.0 T MRI scanner and applied TA on T2-w and SWI images to extract texture features. We evaluated the presence or absence of lesion enhancement on T1-w post-contrast images and performed a computational statistical analysis to assess if there was any significant correlation between the texture features and the presence of lesion activity. ROC curves and leave-one-out cross-validation were used to evaluate the performance of individual features and multiparametric models in the identification of active lesions.

RESULTS

Multiple TA features obtained from SWI images showed a significantly different distribution in acute and chronic lesions (AUC, 0.617-0.720). Multiparametric predictive models based on logistic ridge regression and partial least squares regression yielded an AUC of 0.778 and 0.808, respectively. Results from T2-w images did not show any significant predictive ability of neither individual features nor multiparametric models.

CONCLUSIONS

Texture analysis on SWI sequences may be useful to differentiate acute from chronic MS lesions. The good diagnostic performance could help to reduce the need of intravenous contrast agent administration in follow-up MRI studies.

KEY POINTS

• Texture analysis applied on SWI sequences may be useful to differentiate acute from chronic multiple sclerosis lesions • The good diagnostic performance could help to minimize the need of intravenous contrast agent administration in follow-up MRI studies.

Magnetic Resonance-Based Radiomic Analysis of Radiofrequency Lesion Predicts Outcomes After Percutaneous Cordotomy: A Feasibility Study

BACKGROUND

To date, there is limited data on evaluation of the cordotomy lesion and predicting clinical outcome.

OBJECTIVE

To evaluate the utility of magnetic resonance (MR)-based radiomic analysis to quantify microstructural changes created by the cordotomy lesion and predict outcome in patients undergoing percutaneous cordotomy for medically refractory cancer pain.

METHODS

This is a retrospective interpretation of prospectively acquired data in 10 patients (5 males, age range 43-76 yr) who underwent percutaneous computed tomography-guided high cervical cordotomy for medically refractory cancer pain between 2015 and 2016. All patients underwent magnetic resonance imaging (MRI) of the cordotomy lesion on postoperative day 1. After segmentation of T2-weighted images, 310 radiomic features were extracted. Pain outcomes were recorded on postoperative day 1 and day 7 using the visual analog scale. R software was used to build statistical models based on MRI radiomic features for prediction of pain outcomes.

RESULTS

A total of 20 relevant radiomic features were identified using the maximum relevance minimum redundanc method. Radiomics predicted postoperative day 1 pain scores with an accuracy of 90% (P = .046), 100% sensitivity, 75% specificity, 85.7% positive predictive value, and 100% negative predictive value. The radiomics model also predicted if the postoperative day 1 pain score was sustained on postoperative day 7 with an accuracy of 100% (P = .028), 100% sensitivity, 100% specificity, and 100% positive and negative predictive value.

CONCLUSION

MR-based radiomic analysis of the cordotomy lesion was predictive of pain outcomes at 1 wk after percutaneous cordotomy for intractable cancer pain.

Deep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI

Background Enhancing lesions on MRI scans obtained after contrast material administration are commonly thought to represent disease activity in multiple sclerosis (MS); it is desirable to develop methods that can predict enhancing lesions without the use of contrast material. Purpose To evaluate whether deep learning can predict enhancing lesions on MRI scans obtained without the use of contrast material. Materials and Methods This study involved prospective analysis of existing MRI data. A convolutional neural network was used for classification of enhancing lesions on unenhanced MRI scans. This classification was performed for each slice, and the slice scores were combined by using a fully connected network to produce participant-wise predictions. The network input consisted of 1970 multiparametric MRI scans from 1008 patients recruited from 2005 to 2009. Enhanced lesions on postcontrast T1-weighted images served as the ground truth. The network performance was assessed by using fivefold cross-validation. Statistical analysis of the network performance included calculation of lesion detection rates and areas under the receiver operating characteristic curve (AUCs). Results MRI scans from 1008 participants (mean age, 37.7 years ± 9.7; 730 women) were analyzed. At least one enhancing lesion was observed in 519 participants. The sensitivity and specificity averaged across the five test sets were 78% ± 4.3 and 73% ± 2.7, respectively, for slice-wise prediction. The corresponding participant-wise values were 72% ± 9.0 and 70% ± 6.3. The diagnostic performances (AUCs) were 0.82 ± 0.02 and 0.75 ± 0.03 for slice-wise and participant-wise enhancement prediction, respectively. Conclusion Deep learning used with conventional MRI identified enhanced lesions in multiple sclerosis from images from unenhanced multiparametric MRI with moderate to high accuracy. © RSNA, 2019.

MRI-based measures of intracortical myelin are sensitive to a history of TBI and are associated with functional connectivity

Traumatic brain injuries (TBIs) induce persistent behavioral and cognitive deficits via diffuse axonal injury. Axonal injuries are often examined in vivo using diffusion MRI, which identifies damaged and demyelinated regions in deep white matter. However, TBI patients can exhibit impairment in the absence of diffusion-measured abnormalities, suggesting that axonal injury and demyelination may occur outside the deep white matter. Importantly, myelinated axons are also present within the cortex. Cortical myelination cannot be measured using diffusion imaging, but can be mapped in-vivo using the T1-w/T2-w ratio method. Here, we conducted the first work examining effects of TBI on intracortical myelin in living humans by applying myelin mapping to 46 US Military Veterans with a history of TBI. We observed that myelin maps could be created in TBI patients that matched known distributions of cortical myelin. After controlling for age and presence of blast injury, the number of lifetime TBIs was associated with reductions in the T1-w/T2-w ratio across the cortex, most significantly in a highly-myelinated lateral occipital region corresponding with the human MT+ complex. Further, the T1-w/T2-w ratio in this MT+ region predicted resting-state functional connectivity of that region. By contrast, a history of blast TBI did not affect the T1-w/T2-w ratio in either a diffuse or focal pattern. These findings suggest that intracortical myelin, as measured using the T1-w/T2-w ratio, may be a TBI biomarker that is anatomically complementary to diffusion MRI. Thus, myelin mapping could potentially be combined with diffusion imaging to improve MRI-based diagnostic tools for TBI.

Histogram analysis of apparent diffusion coefficient and fluid-attenuated inversion recovery in discriminating between enhancing and nonenhancing lesions in multiple sclerosis

PURPOSE

This study evaluates the diagnostic performance of apparent diffusion coefficient (ADC) and T2 fluid-attenuation inversion recovery (T2 FLAIR) in discriminating between new white matter (WM) enhancing lesions (ELs) and non-enhancing lesions (NELs) in multiple sclerosis (MS) patients.

METHODS

Thirty MS patients with a new solitary WM lesion on brain MRI were analyzed. A region-of-interest was drawn on all lesions and the contralateral normal-appearing WM (NAWM) on T2 FLAIR and ADC maps. Normalized ratios of T2 FLAIR and ADC were calculated by dividing lesion value by the contralateral NAWM. Histogram analysis was performed on the T2 FLAIR, ADC values, and their normalized ratios. Mann-Whitney U test was used to compare histogram parameters and receiver operating characteristic (ROC) analysis determined the area under the curve (AUC).

RESULTS

T2 FLAIR histogram parameters were not significantly different between ELs and NELs. Several EL ADC histogram parameters, including maximum and mean, were significantly higher than NELs (p = 0.006 to p = 0.031). There was a trend toward significantly higher maximum ADC in ELs after adjusting for multiple comparisons (p = 0.054). The standard deviation of T2 FLAIR (AUC 0.70), maximum ADC (AUC 0.79), and normalized maximum ADC ratio (AUC 0.75) were among histogram parameters with the highest diagnostic performance. A maximum ADC cutoff of 1274 × 10^-6 mm²/s provided a 0.86 sensitivity and 0.75 specificity.

CONCLUSION

In patients with contraindications to gadolinium or concerns with gadolinium brain deposition, consideration may be given to ADC and T2 FLAIR as potential noncontrast methods for the evaluation of active MS lesions.

Diagnostic value of alternative techniques to gadolinium-based contrast agents in MR neuroimaging-a comprehensive overview

Gadolinium-based contrast agents (GBCAs) increase lesion detection and improve disease characterization for many cerebral pathologies investigated with MRI. These agents, introduced in the late 1980s, are in wide use today. However, some non-ionic linear GBCAs have been associated with the development of nephrogenic systemic fibrosis in patients with kidney failure. Gadolinium deposition has also been found in deep brain structures, although it is of unclear clinical relevance. Hence, new guidelines from the International Society for Magnetic Resonance in Medicine advocate cautious use of GBCA in clinical and research practice. Some linear GBCAs were restricted from use by the European Medicines Agency (EMA) in 2017.

This review focuses on non-contrast-enhanced MRI techniques that can serve as alternatives for the use of GBCAs. Clinical studies on the diagnostic performance of non-contrast-enhanced as well as contrast-enhanced MRI methods, both well established and newly proposed, were included. Advantages and disadvantages together with the diagnostic performance of each method are detailed. Non-contrast-enhanced MRIs discussed in this review are arterial spin labeling (ASL), time of flight (TOF), phase contrast (PC), diffusion-weighted imaging (DWI), magnetic resonance spectroscopy (MRS), susceptibility weighted imaging (SWI), and amide proton transfer (APT) imaging.

Ten common diseases were identified for which studies reported comparisons of non-contrast-enhanced and contrast-enhanced MRI. These specific diseases include primary brain tumors, metastases, abscess, multiple sclerosis, and vascular conditions such as aneurysm, arteriovenous malformation, arteriovenous fistula, intracranial carotid artery occlusive disease, hemorrhagic, and ischemic stroke.

In general, non-contrast-enhanced techniques showed comparable diagnostic performance to contrast-enhanced MRI for specific diagnostic questions. However, some diagnoses still require contrast-enhanced imaging for a complete examination.

Radiomics nomogram based on MRI for predicting white matter hyperintensity progression in elderly adults

BACKGROUND

White matter hyperintensity (WMH) is widely observed in aging brain and is associated with various diseases. A pragmatic and handy method in the clinic to assess and follow up white matter disease is strongly in need.

PURPOSE

To develop and validate a radiomics nomogram for the prediction of WMH progression.

STUDY TYPE

Retrospective.

POPULATION

Brain images of 193 WMH patients from the Picture Archiving and Communication Systems (PACS) database in the A Medical Center (Zhejiang Provincial People's Hospital). MRI data of 127 WMH patients from the PACS database in the B Medical Center (Zhejiang Lishui People's Hospital) were included for external validation. All of the patients were at least 60 years old.

FIELD STRENGTH/SEQUENCE

T₁ -fluid attenuated inversion recovery images were acquired using a 3T scanner.

ASSESSMENT

WMH was evaluated utilizing the Fazekas scale based on MRI. WMH progression was assessed with a follow-up MRI using a visual rating scale. Three neuroradiologists, who were blinded to the clinical data, assessed the images independently. Moreover, interobserver and intraobserver reproducibility were performed for the regions of interest for segmentation and feature extraction.

STATISTICAL TESTS

A receiver operating characteristic (ROC) curve, the area under the curve (AUC) of the ROC was calculated, along with sensitivity and specificity. Also, a Hosmer-Lemeshow test was performed.

RESULTS

The AUC of radiomics signature in the primary, internal validation cohort, external validation cohort were 0.886, 0.816, and 0.787, respectively; the specificity were 71.79%, 72.22%, and 81%, respectively; the sensitivity were 92.68%, 87.94% and 78.3%, respectively. The radiomics nomogram in the primary cohort (AUC = 0.899) and the internal validation cohort (AUC = 0.84). The Hosmer-Lemeshow test showed no significant difference between the primary cohort and the internal validation cohort (P > 0.05). The AUC of the radiomics nomogram, radiomics signature, and hyperlipidemia in all patients from the primary and internal validation cohort was 0.878, 0.848, and 0.626, respectively.

DATA CONCLUSION

This multicenter study demonstrated the use of a radiomics nomogram in predicting the progression of WMH with elderly adults (an age of at least 60 years) based on conventional MRI.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:535-546.

Quantifying changes on susceptibility weighted images in amyotrophic lateral sclerosis using MRI texture analysis

Objective: Susceptibility-weighted imaging (SWI) has been used to identify neurodegeneration in amyotrophic lateral sclerosis (ALS) through qualitative gross visual comparison of signal intensity. The aim of this study was to quantitatively identify cerebral degeneration in ALS on SWI using texture analysis. Methods: SW images were acquired from 17 ALS patients (58.4 ± 10.3 years, 13M/4F, ALSFRS-R 41.2 ± 4.1) and 18 healthy controls (56.3 ± 17.6 years, 9M/9F) at 4.7 tesla. Textures were computed within the precentral gyrus and basal ganglia and compared between patients and controls using ANCOVA with age and gender as covariates. Texture features were correlated with clinical measures in patients. Texture features found to be significantly different between patients and controls in the precentral gyrus were then used in a whole-brain 3D texture analysis. Results: The texture feature autocorrelation was significantly higher in ALS patients compared to healthy controls in the precentral gyrus and basal ganglia (p < 0.05). Autocorrelation correlated significantly with clinical measures such as disease progression rate and finger tapping speed (p < 0.05). Whole brain 3D texture analysis using autocorrelation revealed differences between ALS patients and controls within the precentral gyrus on SWI images (p < 0.001). Conclusion: Texture analysis on SWI can quantitatively identify cerebral differences between ALS patients and controls.

Magnetic resonance texture analysis utility in differentiating intraparenchymal neurosarcoidosis from primary central nervous system lymphoma: a preliminary analysis

PURPOSE

Neurosarcoidosis and primary central nervous system lymphomas, although distinct disease entities, can both have overlapping neuroimaging findings. The purpose of our preliminary study was to assess if magnetic resonance texture analysis can differentiate parenchymal mass-like neurosarcoidosis granulomas from primary central nervous system lymphomas.

METHODS

A total of nine patients was evaluated, four with parenchymal neurosarcoidosis granulomas and five with primary central nervous system lymphomas. Magnetic resonance texture analysis was performed with commercial software using a filtration histogram technique. Texture features of different sizes and variations in signal intensity were extracted at six different spatial scale filters, followed by feature quantification using statistical and histogram parameters and 36 features were analysed for each sequence (T1-weighted, T2-weighted, fluid-attenuated inversion recovery, diffusion-weighted, apparent diffusion coefficient, T1-post contrast). The non-parametric Mann-Whitney test was used to evaluate the differences between different texture parameters.

RESULTS

The differences in distribution of entropy on T2-weighted imaging, apparent diffusion coefficient and T1-weighted post-contrast images were statistically significant on all spatial scale filters. Magnetic resonance texture analysis using medium and coarse spatial scale filters was especially useful in discriminating neurosarcoidosis from primary central nervous system lymphomas for mean, mean positive pixels, kurtosis, and skewness on diffusion-weighted imaging ( P < 0.004-0.030). At spatial scale filter 5, entropy on T2-weighted imaging ( P = 0.001) was the most useful discriminator with a cut-off value of 6.12 ( P = 0.001, area under the curve (AUC)-1, sensitivity (Sn)-100%, specificity (Sp)-100%), followed by kurtosis and skewness on diffusion-weighted imaging with a cut-off value of -0.565 ( P = 0.011, AUC-0.97, Sn-100%, Sp-83%) and-0.365 ( P = 0.008, AUC-0.98, Sn-100%, Sp-100%) respectively.

CONCLUSION

Filtration histogram-based magnetic resonance texture analysis appears to be a promising modality to distinguish parenchymal neurosarcoidosis granulomas from primary central nervous system lymphomas.

Whole-volume ADC Histogram and Texture Analyses of Parotid Glands as an Image Biomarker in Evaluating Disease Activity of Primary Sjögren's Syndrome

Diffusion weighted imaging (DWI) has proven to be sensitive for detecting early injury to the parotid gland in pSS (primary Sjögren's syndrome). Here, we explored the application of ADC histogram and texture analyses for evaluating the disease activity of pSS. A total of 55 patients with pSS who met the classification criteria of the 2002 AECG criteria prospectively underwent 3.0-T magnetic resonance imaging (MRI) including DWI (b = 0 and 1000 s/mm2). According to the ESSDAI score, 35 patients were categorized into the low-activity group (ESSDAI < 5) and 20 into the moderate-high-activity group (ESSDAI ≥ 5). Via analysis of the whole-volume ADC histogram, the ADCmean, skewness, kurtosis, and entropy values of the bilateral parotid glands were determined. Multivariate analysis was used to identify independent risk factors for predicting disease activity. The diagnostic performance of the indexes was evaluated via receiver operating characteristic (ROC) analysis. ROC analysis showed that the anti-SSB, lip biopsy, MRI morphology, ADC, ADCmean, and entropy values were able to categorize the disease into two groups, particularly the entropy values. The multivariate model, which included anti-SSB, MRI morphology and entropy, had an area under the ROC curve of 0.923 (P < 0.001). The parotid entropy value distinguished disease activity in patients with pSS, especially combined with anti-SSB and MRI morphology.

Multiple Immune-Inflammatory and Oxidative and Nitrosative Stress Pathways Explain the Frequent Presence of Depression in Multiple Sclerosis

Patients with a diagnosis of multiple sclerosis (MS) or major depressive disorder (MDD) share a wide array of biological abnormalities which are increasingly considered to play a contributory role in the pathogenesis and pathophysiology of both illnesses. Shared abnormalities include peripheral inflammation, neuroinflammation, chronic oxidative and nitrosative stress, mitochondrial dysfunction, gut dysbiosis, increased intestinal barrier permeability with bacterial translocation into the systemic circulation, neuroendocrine abnormalities and microglial pathology. Patients with MS and MDD also display a wide range of neuroimaging abnormalities and patients with MS who display symptoms of depression present with different neuroimaging profiles compared with MS patients who are depression-free. The precise details of such pathology are markedly different however. The recruitment of activated encephalitogenic Th17 T cells and subsequent bidirectional interaction leading to classically activated microglia is now considered to lie at the core of MS-specific pathology. The presence of activated microglia is common to both illnesses although the pattern of such action throughout the brain appears to be different. Upregulation of miRNAs also appears to be involved in microglial neurotoxicity and indeed T cell pathology in MS but does not appear to play a major role in MDD. It is suggested that the antidepressant lofepramine, and in particular its active metabolite desipramine, may be beneficial not only for depressive symptomatology but also for the neurological symptoms of MS. One clinical trial has been carried out thus far with, in particular, promising MRI findings.

Blood-Brain Barrier Leakage during Early Epileptogenesis Is Associated with Rapid Remodeling of the Neurovascular Unit

Increased permeability of the blood-brain barrier (BBB) following cerebral injury results in regional extravasation of plasma proteins and can critically contribute to the pathogenesis of epilepsy. Here, we comprehensively explore the spatiotemporal evolution of a main extravasation component, albumin, and illuminate associated responses of the neurovascular unit (NVU) contributing to early epileptogenic neuropathology. We applied translational in vivo MR imaging and complementary immunohistochemical analyses in the widely used rat pilocarpine post-status epilepticus (SE) model. The observed rapid BBB leakage affected major epileptogenesis-associated brain regions, peaked between 1 and 2 d post-SE, and rapidly declined thereafter, accompanied by cerebral edema generally following the same time course. At peak of BBB leakage, serum albumin colocalized with NVU constituents, such as vascular components, neurons, and brain immune cells. Surprisingly, astroglial markers did not colocalize with albumin, and aquaporin-4 (AQP4) was clearly reduced in areas of leaky BBB, indicating a severe disturbance of astrocyte-mediated endothelial-neuronal coupling. In addition, a distinct adaptive reorganization process of the NVU vasculature apparently takes place at sites of albumin presence, substantiated by reduced immunoreactivity of endothelial and changes in vascular basement membrane markers. Taken together, degenerative events at the level of the NVU, affecting vessels, astrocytes, and neurons, seem to outweigh reconstructive processes. Considering the rapidly occurring BBB leakage and subsequent impairment of the NVU, our data support the necessity of a prompt BBB-restoring treatment as one component of rational therapeutic intervention to prevent epileptogenesis and the development of other detrimental sequelae of SE.

Identification of ghost artifact using texture analysis in pediatric spinal cord diffusion tensor images

PURPOSE

Ghost artifacts are a major contributor to degradation of spinal cord diffusion tensor images. A multi-stage post-processing pipeline was designed, implemented and validated to automatically remove ghost artifacts arising from reduced field of view diffusion tensor imaging (DTI) of the pediatric spinal cord.

METHOD

A total of 12 pediatric subjects including 7 healthy subjects (mean age=11.34years) with no evidence of spinal cord injury or pathology and 5 patients (mean age=10.96years) with cervical spinal cord injury were studied. Ghost/true cords, labeled as region of interests (ROIs), in non-diffusion weighted b0 images were segmented automatically using mathematical morphological processing. Initially, 21 texture features were extracted from each segmented ROI including 5 first-order features based on the histogram of the image (mean, variance, skewness, kurtosis and entropy) and 16s-order feature vector elements, incorporating four statistical measures (contrast, correlation, homogeneity and energy) calculated from co-occurrence matrices in directions of 0°, 45°, 90° and 135°. Next, ten features with a high value of mutual information (MI) relative to the pre-defined target class and within the features were selected as final features which were input to a trained classifier (adaptive neuro-fuzzy interface system) to separate the true cord from the ghost cord.

RESULTS

The implemented pipeline was successfully able to separate the ghost artifacts from true cord structures. The results obtained from the classifier showed a sensitivity of 91%, specificity of 79%, and accuracy of 84% in separating the true cord from ghost artifacts.

CONCLUSION

The results show that the proposed method is promising for the automatic detection of ghost cords present in DTI images of the spinal cord. This step is crucial towards development of accurate, automatic DTI spinal cord post processing pipelines.

The Use of Noncontrast Quantitative MRI to Detect Gadolinium-Enhancing Multiple Sclerosis Brain Lesions: A Systematic Review and Meta-Analysis

BACKGROUND

Concerns have arisen about the long-term health effects of repeat gadolinium injections in patients with multiple sclerosis and the incomplete characterization of MS lesion pathophysiology that results from relying on enhancement characteristics alone.

PURPOSE

Our aim was to perform a systematic review and meta-analysis analyzing whether noncontrast MR imaging biomarkers can distinguish enhancing and nonenhancing brain MS lesions.

DATA SOURCES

Our sources were Ovid MEDLINE, Ovid Embase, and the Cochrane data base from inception to August 2016.

STUDY SELECTION

We included 37 journal articles on 985 patients with MS who had MR imaging in which T1-weighted postcontrast sequences were compared with noncontrast sequences obtained during the same MR imaging examination by using ROI analysis of individual MS lesions.

DATA ANALYSIS

We performed random-effects meta-analyses comparing the standard mean difference of each MR imaging metric taken from enhancing-versus-nonenhancing lesions.

DATA SYNTHESIS

DTI-based fractional anisotropy values are significantly different between enhancing and nonenhancing lesions (P = .02), with enhancing lesions showing decreased fractional anisotropy compared with nonenhancing lesions. Of the other most frequently studied MR imaging biomarkers (mean diffusivity, magnetization transfer ratio, or ADC), none were significantly different (P values of 0.30, 0.47, and 0.19. respectively) between enhancing and nonenhancing lesions. Of the limited studies providing diagnostic accuracy measures, gradient-echo-based quantitative susceptibility mapping had the best performance in discriminating enhancing and nonenhancing MS lesions.

LIMITATIONS

MR imaging techniques and patient characteristics were variable across studies. Most studies did not provide diagnostic accuracy measures. All imaging metrics were not studied in all 37 studies.

CONCLUSIONS

Noncontrast MR imaging techniques, such as DTI-based FA, can assess MS lesion acuity without gadolinium.