Identifying the white matter impairments among ART-naïve HIV patients: a multivariate pattern analysis of DTI data

Tensor-valued diffusion MRI detects brain microstructure changes in HIV infected individuals with cognitive impairment

Despite advancements, the prevalence of HIV-associated neurocognitive impairment remains at approximately 40%, attributed to factors like pre-cART (combination antiretroviral therapy) irreversible brain injury. People with HIV (PWH) treated with cART do not show significant neurocognitive changes over relatively short follow-up periods. However, quantitative neuroimaging may be able to detect ongoing subtle microstructural changes. This study aimed to investigate the sensitivity of tensor-valued diffusion encoding in detecting such changes in brain microstructural integrity in cART-treated PWH. Additionally, it explored relationships between these metrics, neurocognitive scores, and plasma levels of neurofilament light (NFL) chain and glial fibrillary acidic protein (GFAP). Using MRI at 3T, 24 PWH and 31 healthy controls underwent cross-sectional examination. The results revealed significant variations in b-tensor encoding metrics across white matter regions, with associations observed between these metrics, cognitive performance, and blood markers of neuronal and glial injury (NFL and GFAP). Moreover, a significant interaction between HIV status and imaging metrics was observed, particularly impacting total cognitive scores in both gray and white matter. These findings suggest that b-tensor encoding metrics offer heightened sensitivity in detecting subtle changes associated with axonal injury in HIV infection, underscoring their potential clinical relevance in understanding neurocognitive impairment in PWH.

Probing bundle-wise abnormalities in patients infected with human immunodeficiency virus using fixel-based analysis: new insights into neurocognitive impairments

BACKGROUND

Changes in white matter (WM) underlie the neurocognitive damages induced by a human immunodeficiency virus (HIV) infection. This study aimed to examine using a bundle-associated fixel-based analysis (FBA) pipeline for investigating the microstructural and macrostructural alterations in the WM of the brain of HIV patients.

METHODS

This study collected 93 HIV infected patients and 45 age/education/handedness matched healthy controls (HCs) at the Beijing Youan Hospital between January 1, 2016 and December 30, 2016.All HIV patients underwent neurocognitive evaluation and laboratory testing followed by magnetic resonance imaging (MRI) scanning. In order to detect the bundle-wise WM abnormalities accurately, a specific WM bundle template with 56 tracts of interest was firstly generated by an automated fiber clustering method using a subset of subjects. Fixel-based analysis was used to investigate bundle-wise differences between HIV patients and HCs in three perspectives: fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC). The between-group differences were detected by a two-sample t -test with the false discovery rate (FDR) correction ( P <0.05). Furthermore, the covarying relationship in FD, FC and FDC between any pair of bundles was also accessed by the constructed covariance networks, which was subsequently compared between HIV and HCs via permutation t -tests. The correlations between abnormal WM metrics and the cognitive functions of HIV patients were explored via partial correlation analysis after controlling age and gender.

RESULTS

Among FD, FC and FDC, FD was the only metric that showed significant bundle-wise alterations in HIV patients compared to HCs. Increased FD values were observed in the bilateral fronto pontine tract, corona radiata frontal, left arcuate fasciculus, left corona radiata parietal, left superior longitudinal fasciculus III, and right superficial frontal parietal (SFP) (all FDR P <0.05). In bundle-wise covariance network, HIV patients displayed decreased FD and increased FC covarying patterns in comparison to HC ( P <0.05) , especially between associated pathways. Finally, the FCs of several tracts exhibited a significant correlation with language and attention-related functions.

CONCLUSIONS

Our study demonstrated the utility of FBA on detecting the WM alterations related to HIV infection. The bundle-wise FBA method provides a new perspective for investigating HIV-induced microstructural and macrostructural WM-related changes, which may help to understand cognitive dysfunction in HIV patients thoroughly.

The default mode network is affected in the early stage of simian immunodeficiency virus infection: a longitudinal study

Acquired immune deficiency syndrome infection can lead to cognitive dysfunction represented by changes in the default mode network. Most recent studies have been cross-sectional and thus have not revealed dynamic changes in the default mode network following acquired immune deficiency syndrome infection and antiretroviral therapy. Specifically, when brain imaging data at only one time point are analyzed, determining the duration at which the default mode network is the most effective following antiretroviral therapy after the occurrence of acquired immune deficiency syndrome. However, because infection times and other factors are often uncertain, longitudinal studies cannot be conducted directly in the clinic. Therefore, in this study, we performed a longitudinal study on the dynamic changes in the default mode network over time in a rhesus monkey model of simian immunodeficiency virus infection. We found marked changes in default mode network connectivity in 11 pairs of regions of interest at baseline and 10 days and 4 weeks after virus inoculation. Significant interactions between treatment and time were observed in the default mode network connectivity of regions of interest pairs area 31/V6.R and area 8/frontal eye field (FEF). L, area 8/FEF.L and caudal temporal parietal occipital area (TPOC).R, and area 31/V6.R and TPOC.L. ART administered 4 weeks after infection not only interrupted the progress of simian immunodeficiency virus infection but also preserved brain function to a large extent. These findings suggest that the default mode network is affected in the early stage of simian immunodeficiency virus infection and that it may serve as a potential biomarker for early changes in brain function and an objective indicator for making early clinical intervention decisions.

Altered white matter integrity in the corpus callosum in adults with HIV: a systematic review of diffusion tensor imaging studies

We systematically reviewed studies comparing differences in the integrity of the corpus callosum in adults with HIV compared to healthy controls, using Diffusion Tensor Imaging (DTI), using search engines Science Direct, Web of Science and PubMed. The search terms used were "HIV", "corpus callosum", and a variation of either "DTI" or "Diffusion Tensor Imaging" with or without the term "adults". We specifically examined the corpus callosum as it is the largest white matter tract in the brain, plays a primary role in cognition, and has been shown to be morphologically altered in people living with HIV. Lower fractional anisotropy (FA) was consistently found in the corpus callosum in people with HIV compared to controls. As most studies used only FA as a measure of diffusion, it would be informative for future research if other DTI metrics, such as mean diffusivity (MD), were also investigated as these metrics may be more sensitive markers of HIV-related neuropathology.

Altered gray matter structural covariance networks in drug-naïve and treated early HIV-infected individuals

Background

While regional brain structure and function alterations in HIV-infected individuals have been reported, knowledge about the topological organization in gray matter networks is limited. This research aims to investigate the effects of early HIV infection and combination antiretroviral therapy (cART) on gray matter structural covariance networks (SCNs) by employing graph theoretical analysis.

Methods

Sixty-five adult HIV+ individuals (25–50 years old), including 34 with cART (HIV+/cART+) and 31 medication-naïve (HIV+/cART–), and 35 demographically matched healthy controls (HCs) underwent high-resolution T1-weighted images. A sliding-window method was employed to create “age bins,” and SCNs (based on cortical thickness) were constructed for each bin by calculating Pearson's correlation coefficients. The group differences of network indices, including the mean nodal path length (Nlp), betweenness centrality (Bc), number of modules, modularity, global efficiency, local efficiency, and small-worldness, were evaluated by ANOVA and post-hoc tests employing the network-based statistics method.

Results

Relative to HCs, less efficiency in terms of information transfer in the parietal and occipital lobe (decreased Bc) and a compensated increase in the frontal lobe (decreased Nlp) were exhibited in both HIV+/cART+ and HIV+/cART– individuals (P &lt; 0.05, FDR-corrected). Compared with HIV+/cART– and HCs, less specialized function segregation (decreased modularity and small-worldness property) and stronger integration in the network (increased Eglob and little changed path length) were found in HIV+/cART+ group (P &lt; 0.05, FDR-corrected).

Conclusion

Early HIV+ individuals exhibited a decrease in the efficiency of information transmission in sensory regions and a compensatory increase in the frontal lobe. HIV+/cART+ showed a less specialized regional segregation function, but a stronger global integration function in the network.

Multimodal magnetic resonance neuroimaging measures characteristic of early cART-treated pediatric HIV: A feature selection approach

Children with perinatally acquired HIV (CPHIV) have poor cognitive outcomes despite early combination antiretroviral therapy (cART). While CPHIV-related brain alterations can be investigated separately using proton magnetic resonance spectroscopy (1 H-MRS), structural magnetic resonance imaging (sMRI), diffusion tensor imaging (DTI), and functional MRI (fMRI), a set of multimodal MRI measures characteristic of children on cART has not been previously identified. We used the embedded feature selection of a logistic elastic-net (EN) regularization to select neuroimaging measures that distinguish CPHIV from controls and measured their classification performance via the area under the receiver operating characteristic curve (AUC) using repeated cross validation. We also wished to establish whether combining MRI modalities improved the models. In single modality analysis, sMRI volumes performed best followed by DTI, whereas individual EN models on spectroscopic, gyrification, and cortical thickness measures showed no class discrimination capability. Adding DTI and 1 H-MRS in basal measures to sMRI volumes produced the highest classification performancevalidation accuracy = 85 % AUC = 0.80 . The best multimodal MRI set consisted of 22 DTI and sMRI volume features, which included reduced volumes of the bilateral globus pallidus and amygdala, as well as increased mean diffusivity (MD) and radial diffusivity (RD) in the right corticospinal tract in cART-treated CPHIV. Consistent with previous studies of CPHIV, select subcortical volumes obtained from sMRI provide reasonable discrimination between CPHIV and controls. This may give insight into neuroimaging measures that are relevant in understanding the effects of HIV on the brain, thereby providing a starting point for evaluating their link with cognitive performance in CPHIV.

Application of Diffusion Tensor Imaging (DTI) in the Diagnosis of HIV-Associated Neurocognitive Disorder (HAND): A Meta-Analysis and a System Review

Background

The patients with HIV-associated neurocognitive disorder (HAND) are often accompanied by white matter structure damage. Diffusion tensor imaging (DTI) is an important tool to detect white matter structural damage. However, the changes in DTI values reported in many studies are diverse in different white matter fiber tracts and brain regions.

Purpose

Our research is dedicated to evaluating the consistency and difference of the correlation between HAND and DTI measures in different studies. Additionally, the value of DTI in HAND evaluation is used to obtain consensus and independent conclusions between studies.

Methods

We searched PubMed and Web of Science to collect relevant studies using DTI for the diagnosis of HAND. After screening and evaluating the search results, meta-analysis is used for quantitative research on data. Articles that cannot collect data but meet the research relevance will be subjected to a system review.

Results

The meta-analysis shows that the HAND group has lower fractional anisotropy (standardized mean difference = −0.57 p < 0.0001) and higher mean diffusivity (standardized mean difference = 0.04 p < 0.0001) than the healthy control group in corpus callosum. In other white matter fibers, we found similar changes in fractional anisotropy (standardized mean difference = −1.18 p < 0.0001) and mean diffusivity (standardized mean difference = 0.69 p < 0.0001). However, the heterogeneity (represented by I²) between the studies is high (in corpus callosum 94, 88%, in other matter fibers 95, 81%). After subgroup analysis, the heterogeneity is obtained as 19.5, 40.7% (FA, MD in corpus callosum) and 0, 0% (FA, MD among other white matter fibers).

Conclusion

The changes in white matter fibers in patients with HAND are statistically significant at the observation level of DTI compared with healthy people. The differences between the studies are mainly derived from demographics, start and maintenance time of antiretroviral therapy, differences in nadir CD4+T cells, and the use of different neurocognitive function scales. As an effective method to detect the changes in white matter fibers, DTI is of great significance for the diagnosis of HAND, but there are still some shortcomings. In the absence of neurocognitive function scales, independent diagnosis remains difficult.

Systematic Review Registration:https://inplasy.com/inplasy-2021-10-0079/.

Altered white matter microstructure and neurocognitive function of HIV-infected patients with low nadir CD4

Altered white matter microstructure has been reported repeatedly using diffusion tensor imaging (DTI) in HIV-associated neurocognitive disorders. However, the associations between neurocognitive deficits and impaired white matter remains obscure due to frequent physical and psychiatric comorbidities in the patients. Severe immune suppression, reflected by low nadir CD4 T-cell counts, is reported to be associated with the neurocognitive deficits in the patients. In the present study, we examined white matter integrity using DTI and tract-based spatial statistics (TBSS), and neurocognitive functions using a battery of tests, in 15 HIV-infected patients with low nadir CD4, 16 HIV-infected patients with high nadir CD4, and 33 age- and sex-matched healthy controls. As DTI measures, we analyzed fractional anisotropy (FA) and mean diffusivity (MD). In addition, we investigated the correlation between white matter impairments and neurocognitive deficits. Among the three participant groups, the patients with low nadir CD4 showed significantly lower performance in processing speed and motor skills, and had significantly increased MD in widespread regions of white matter in both hemispheres. In the patients with low nadir CD4, there was a significant negative correlation between motor skills and MD in the right motor tracts, as well as in the corpus callosum. In summary, this study may provide white matter correlates of neurocognitive deficits in HIV-infected patients with past severe immune suppression as legacy effects.

Structural network alterations induced by ART-naive and ART-treated subjects infected with HIV

OBJECTIVE

To investigate how the structural connectivity altered in combined antiretroviral therapy-treated (cART+) HIV patients and cART-naive (cART-) HIV patients by conducting Network analysis of Diffusion Tensor Imaging (DTI) data.

METHODS

We enrolled 22 cART-, 23 cART+ and 28 normal controls (NC) in our current study. Firstly, the DTI imaging data pre-processing was conducted and the asymmetric 90 × 90 matrix for each participant from their DTI data was obtained with the use of PANDA. Then, we applied a graph-theoretical network analysis toolkit, GRETNA v2.0, to calculate metrics such as small-"worldness," characteristic path length, clustering coefficient, global efficiency, local efficiency, and nodal "betweenness". Finally, we took comparisons among the three groups to investigate topological alterations.

RESULTS

Results (1) the regional characteristics (nodal efficiency) were altered in cART- and cART+ patients predominantly in the frontal cortical regions; (2) changes in various network properties in cART+treat and cART-patients were associated with the performance of behavior functions; (3) Hubs redistributed in HIV subjects especially in cART+ patients.

CONCLUSION

The regional characteristics (nodal efficiency) were altered in cART- and cART+ patients predominantly in the frontal cortical region, and changes in various network properties in cART- and cART+ patients were associated with the performance of behavior functions. In addition, Hubs redistributed in HIV subjects especially in cART+ patients.

Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach

To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps. Two-sample t-tests and recursive feature elimination combined with support vector machine method were applied for feature selection and dimensionality reduction. We used support vector machine classifier to construct model and identify the discriminative features. The automated anatomical labeling 90 atlas and fivefold cross-validation were used to evaluate the robustness and generalization of the classifier. We found our model obtained a high classification performance with an accuracy of 78.07%, and AUC, sensitivity, and specificity of 0.8597, 78.80%, and 76.08%, respectively. We detected 7 discriminative brain subregions. The fivefold cross-validation and automated anatomical labeling 90 atlas also got high classification accuracy, and we found Brainnetome 246 atlas achieved a higher classification performance than the automated anatomical labeling 90 atlas both with tenfold and fivefold cross-validation. Our findings may help the early diagnosis of Parkinson's disease and provide support for research on Parkinson's disease mechanisms and clinical evaluation.

Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

Radiomic Signature as a Diagnostic Factor for Classification of Histologic Subtypes of Lung Cancer

<sec> <title>Objectives:</title> To discuss the application of radiomics using Computerized Tomography (CT) analysis, for improving its diagnostic efficacy in lung, specifically in distinguishing Squamous Cell Carcinoma (SCC), lung Adenocarcinoma (ADC), and Small Cell Lung Cancer (SCLC). </sec> <sec> <title>Methods:</title> The pathology of 189 identified cases of lung cancer was analyzed, retrospectively (60 patients with SCC, 69 patients with lung ADC and 60 patients with SCLC). A neural network was used to determine whether the pulmonary or mediastinal window was selected to extract effective radiomic features. The key features of radiomic signature were retrieved by a Least Absolute Shrinkage and Selection Operator (LASSO) multiple logistic regression model. Next, receiver operating characteristic curve and Area Under the Curve (AUC) analysis were used to evaluate the performance of the radiomic signature in both, training(129 patients) and validation cohorts (60 patients). </sec> <sec> <title>Results:</title> About 295 features were extracted from a manually outlined tumor region. Features extracted from mediastinal window CT scans had a better prognostic ability than pulmonary window scans. The average accuracy for mediastinal window scans was 0.933. Our analysis revealed that the radiomic features extracted from mediastinal window scans had the potential to build a prediction model for distinguishing between SCC, lung ADC, and SCLC. The performance of the radiomic signature to diagnose SCC and SCLC in validation cohorts proved effective, with AUC values of 0.869 and 0.859, respectively. </sec> <sec> <title>Conclusions:</title> A unique radiomic signature was constructed as a diagnostic factor for different histologic subtypes of lung cancer. Patients with lung cancer may benefit from this proposed radiomic signature. </sec>

Radiomics and Digital Image Texture Analysis in Oncology (Review)

One of the most promising areas of diagnosis and prognosis of diseases is radiomics, a science combining radiology, mathematical modeling, and deep machine learning. The main concept of radiomics is image biomarkers (IBMs), the parameters characterizing various pathological changes and calculated based on the analysis of digital image texture. IBMs are used for quantitative assessment of digital imaging results (CT, MRI, ultrasound, PET). The use of IBMs in the form of “virtual biopsy” is of particular relevance in oncology.

The article provides the basic concepts of radiomics identifying the main stages of obtaining IBMs: data collection and preprocessing, tumor segmentation, data detection and extraction, modeling, statistical processing, and data validation. The authors have analyzed the possibilities of using IBMs in oncology, describing the currently known features and advantages of using radiomics and image texture analysis in the diagnosis and prognosis of cancer. The limitations and problems associated with the use of radiomics data are considered.

Although the novel effective tool for performing virtual biopsy of human tissue is at the development stage, quite a few projects have already been implemented, and medical software packages for radiomics analysis of digital images have been created.

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy. However, the conventional ML which concatenated the features into a longer feature vector could not be sufficiently effective to combine different features from different modalities. There are considerable controversies over the use of global signal regression (GSR), and few studies have explored the role of GSR in ML in diagnosing neurological diseases. The current study utilized fMRI and sMRI data to implement a new method named multimodal imaging and multilevel characterization with multiclassifier (M3) to classify SZs and healthy controls (HCs) and investigate the influence of GSR in SZ classification. We found that when we used Brainnetome 246 atlas and without performed GSR, our method obtained a classification accuracy of 83.49%, with a sensitivity of 68.69%, a specificity of 93.75%, and an AUC of 0.8491, respectively. We also got great classification performances with different processing methods (with/without GSR and different brain parcellation schemes). We found that the accuracy and specificity of the models without GSR were higher than that of the models with GSR. Our findings indicate that the M3 method is an effective tool to distinguish SZs from HCs, and it can identify discriminative regions to detect SZ to explore the neural mechanisms underlying SZ. The global signal may contain important neuronal information; it can improve the accuracy and specificity of SZ detection.

Classification of Unmedicated Bipolar Disorder Using Whole-Brain Functional Activity and Connectivity: A Radiomics Analysis

The aim of this study was to develop and validate a method of disease classification for bipolar disorder (BD) by functional activity and connectivity using radiomics analysis. Ninety patients with unmedicated BD II as well as 117 healthy controls underwent resting-state functional magnetic resonance imaging (rs-fMRI). A total of 4 types of 7018 features were extracted after preprocessing, including mean regional homogeneity (mReHo), mean amplitude of low-frequency fluctuation (mALFF), resting-state functional connectivity (RSFC), and voxel-mirrored homotopic connectivity (VMHC). Then, predictive features were selected by Mann-Whitney U test and removing variables with a high correlation. Least absolute shrinkage and selection operator (LASSO) method was further used to select features. At last, support vector machine (SVM) model was used to estimate the state of each subject based on the selected features after LASSO. Sixty-five features including 54 RSFCs, 7 mALFFs, 1 mReHo, and 3 VMHCs were selected. The accuracy and area under curve (AUC) of the SVM model built based on the 65 features is 87.3% and 0.919 in the training dataset, respectively, and the accuracy and AUC of this model validated in the validation dataset is 80.5% and 0.838, respectively. These findings demonstrate a valid radiomics approach by rs-fMRI can identify BD individuals from healthy controls with a high classification accuracy, providing the potential adjunctive approach to clinical diagnostic systems.

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD (n = 59) and healthy controls (HCs; n = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain. Machine learning methods were used in the primary set for data dimensionality reduction, feature selection, model construction, and model performance evaluation. The model performance was further validated in the validation set. After feature data dimension reduction and feature selection, 23 of a total of 1,276 features were entered in the model. The brain regions of the selected features included the frontal, temporal, parietal, occipital, and limbic lobes, as well as the cerebellum and the thalamus. In the primary set, the area under the curve (AUC) of the model was 0.974, the sensitivity was 93.8%, the specificity was 90.6%, and the accuracy was 93.8%. In the validation set, the AUC, sensitivity, specificity, and accuracy were 0.980, 90.9%, 88.9%, and 90.0%, respectively. ALFF-based histogram analysis can be used to classify patients with PD and HCs and to effectively identify abnormal brain function regions in PD patients.

Machine learning prediction of neurocognitive impairment among people with HIV using clinical and multimodal magnetic resonance imaging data

Diagnosis of HIV-associated neurocognitive impairment (NCI) continues to be a clinical challenge. The purpose of this study was to develop a prediction model for NCI among people with HIV using clinical- and magnetic resonance imaging (MRI)-derived features. The sample included 101 adults with chronic HIV disease. NCI was determined using a standardized neuropsychological testing battery comprised of seven domains. MRI features included gray matter volume from high-resolution anatomical scans and white matter integrity from diffusion-weighted imaging. Clinical features included demographics, substance use, and routine laboratory tests. Least Absolute Shrinkage and Selection Operator Logistic regression was used to perform variable selection on MRI features. These features were subsequently used to train a support vector machine (SVM) to predict NCI. Three different classification tasks were performed: one used only clinical features; a second used only selected MRI features; a third used both clinical and selected MRI features. Model performance was evaluated by area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity with a tenfold cross-validation. The SVM classifier that combined selected MRI with clinical features outperformed the model using clinical features or MRI features alone (AUC: 0.83 vs. 0.62 vs. 0.79; accuracy: 0.80 vs. 0.65 vs. 0.72; sensitivity: 0.86 vs. 0.85 vs. 0.86; specificity: 0.71 vs. 0.37 vs. 0.52). Our results provide preliminary evidence that combining clinical and MRI features can increase accuracy in predicting NCI and could be developed as a potential tool for NCI diagnosis in HIV clinical practice.

The cingulum in very preterm infants relates to language and social-emotional impairment at 2 years of term-equivalent age

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Maturation of specific WM tracts in preterm individuals differs from those of term controls.

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The elastic net logistic regression model was used to identify altered white matter tracts in the preterm brain.

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The alteration of the cingulum in the preterm at near-term correlate with neurodevelopmental scores at 18–22 months of age.

Background

Relative to full-term infants, very preterm infants exhibit disrupted white matter (WM) maturation and problems related to development, including motor, cognitive, social-emotional, and receptive and expressive language processing.

Objective

The present study aimed to determine whether regional abnormalities in the WM microstructure of very preterm infants, as defined relative to those of full-term infants at a near-term age, are associated with neurodevelopmental outcomes at the age of 18–22 months.

Methods

We prospectively enrolled 89 very preterm infants (birth weight < 1500 g) and 43 normal full-term control infants born between 2016 and 2018. All infants underwent a structural brain magnetic resonance imaging scan at near-term age. The diffusion tensor imaging (DTI) metrics of the whole-brain WM tracts were extracted based on the neonatal probabilistic WM pathway. The elastic net logistic regression model was used to identify altered WM tracts in the preterm brain. We evaluated the associations between the altered WM microstructure at near-term age and motor, cognitive, social-emotional, and receptive and expressive language developments at 18–22 months of age, as measured using the Bayley Scales of Infant Development, Third Edition.

Results

We found that the elastic net logistic regression model could classify preterm and full-term neonates with an accuracy of 87.9% (corrected p < 0.008) using the DTI metrics in the pathway of interest with a 10% threshold level. The fractional anisotropy (FA) values of the body and splenium of the corpus callosum, middle cerebellar peduncle, left and right uncinate fasciculi, and right portion of the pathway between the premotor and primary motor cortices (premotor-PMC), as well as the mean axial diffusivity (AD) values of the left cingulum, were identified as contributive features for classification. Increased adjusted AD values in the left cingulum pathway were significantly correlated with language scores after false discovery rate (FDR) correction (r = 0.217, p = 0.043). The expressive language and social-emotional composite scores showed a significant positive correlation with the AD values in the left cingulum pathway (r = 0.226 [p = 0.036] and r = 0.31 [p = 0.003], respectively) after FDR correction.

Conclusion

Our approach suggests that the cingulum pathways of very preterm infants differ from those of full-term infants and significantly contribute to the prediction of the subsequent development of the language and social-emotional domains. This finding could improve our understanding of how specific neural substrates influence neurodevelopment at later ages, and individual risk prediction, thus helping to inform early intervention strategies that address developmental delay.

Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study

Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (n = 183) was divided into individuals with preclinical AD (n = 78) and controls (n = 105) using amyloid-positron emission tomography, and this cohort was used as the training dataset (80%) and validation dataset (the remaining 20%); cohort 2 (n = 51) was selected retrospectively and divided into "converters" and "nonconverters" according to individuals' future cognitive status, and this cohort was used as a separate test dataset; cohort three included 37 converters (13 from the Alzheimer's Disease Neuroimaging Initiative) and was used as another test set for independent longitudinal research. We extracted radiomics features from multiparametric MRI scans from each participant, using t-tests, autocorrelation tests, and three independent selection algorithms. We then established two classification models (support vector machine [SVM] and random forest [RF]) to verify the efficiency of the retained features. Five-fold cross-validation and 100 repetitions were carried out for the above process. Furthermore, the acquired stable high-frequency features were tested in cohort three by paired two-sample t-tests and survival analyses to identify whether their levels changed with cognitive decline and impact conversion time. The SVM and RF models both showed excellent classification efficiency, with an average accuracy of 89.7-95.9% and 87.1-90.8% in the validation set and 81.9-89.1% and 83.2-83.7% in the test set, respectively. Three stable high-frequency features were identified, all based on the structural MRI modality: the large zone high-gray-level emphasis feature of the right posterior cingulate gyrus, the variance feature of the left superior parietal gyrus, and the coarseness feature of the left posterior cingulate gyrus; their levels were correlated with amyloid-β deposition and predicted future cognitive decline (areas under the curve 0.649-0.761). In addition, levels of the variance feature at baseline decreased with cognitive decline and could affect the conversion time (p < 0.05). In conclusion, this exploratory study shows that the radiomics features of multiparametric MRI scans could represent potential biomarkers of preclinical AD.

Diffusion tensor magnetic resonance imaging of white matter integrity in patients with HIV-associated neurocognitive disorders

Background

This study investigated the efficacy and neurotoxicity of highly active antiretroviral therapy (HAART) by evaluating white matter (WM) injury using diffusion tensor magnetic resonance imaging (DTI) in patients with human immunodeficiency virus (HIV)-associated neurocognitive disorders (HAND).

Methods

Forty-six patients with HAND underwent DTI before and every six months during HAART treatment. DTI data, including fractional anisotropy (FA) and mean diffusivity (MD) values of structural WM before and after HAART, were compared. The relationship between DTI values and plasma viral loads was tested. MD was more sensitive than FA for evaluating WM injury in HAND-positive patients.

Results

Following 12 months of HAART, increased MD values (compared to 6 months of HAART) were observed in the right temporal lobe, right parietal lobe, right occipital lobe, right anterior limb of the internal capsule, right lenticular nucleus, the right cerebral peduncle, left caudate nucleus, left dorsal thalamus, and left posterior limb of the internal capsule. MD values in the left genu of the internal capsule (r=0.350, P=0.017) and left corona radiata (r=0.338, P=0.021) were positively correlated with plasma viral loads.

Conclusions

DTI may be useful for assessing the efficacy and neurotoxicity of HAART in HAND-positive patients. Starting HAART may halt WM injury; however, prolonged HAART could worsen WM injury, highlighting the importance of optimal HAART duration.

Radiomics Analysis of Postoperative Epilepsy Seizures in Low-Grade Gliomas Using Preoperative MR Images

Purpose: The present study aimed to evaluate the performance of radiomics features in the preoperative prediction of epileptic seizure following surgery in patients with LGG. Methods: This retrospective study collected 130 patients with LGG. Radiomics features were extracted from the T2-weighted MR images obtained before surgery. Multivariable Cox-regression with two nested leave-one-out cross validation (LOOCV) loops was applied to predict the prognosis, and elastic net was used in each LOOCV loop to select the predictive features. Logistic models were then built with the selected features to predict epileptic seizures at two time points. Student's t-tests were then used to compare the logistic model predicted probabilities of developing epilepsy in the epilepsy and non-epilepsy groups. The t-test was used to identify features that differentiated patients with early-onset epilepsy from their late-onset counterparts. Results: Seventeen features were selected with the two nested LOOCV loops. The index of concordance (C-index) of the Cox model was 0.683, and the logistic model predicted probabilities of seizure were significantly different between the epilepsy and non-epilepsy groups at each time point. Moreover, one feature was found to be significantly different between the patients with early- or late-onset epilepsy. Conclusion: A total of 17 radiomics features were correlated with postoperative epileptic seizures in patients with LGG and one feature was a significant predictor of the time of epilepsy onset.

Computerized Tomography Radiomics Features Analysis for Evaluation of Perihematomal Edema in Basal Ganglia Hemorrhage

To evaluate the edema area around basal ganglia hemorrhage by the application of computerized tomography (CT)-based radiomics as a prognostic factor and improve the diagnosis efficacy, a total of 120 patients with basal ganglia hemorrhage were analyzed retrospectively. The texture analysis software Mazda 3.3 was used to preprocess the CT images and manually sketch the region of interest to extract the texture features. The extracted texture features were selected by Fisher coefficient, POE+ACC and mutual information. The texture discriminant analysis uses the B11 module in the Mazda 3.3 software. The data were randomly divided into a training dataset (67%) and test dataset (33%). To further study the texture features, the training dataset can be divided into groups according to the median of GCS score, NIHSS score, and maximum diameter of hematoma. Random forest model, support vector machine model, and neural network model were built. AUC of the receiver operating characteristics curve was used to assess the performance of models with test dataset. Among all texture post-processing methods, the lowest error rate was 2.22% for the POE+ACC/nonlinear discriminant. For the maximum diameter of hematoma, GCS score, and NIHSS score group, the lowest error rate were 26.66%, 23.33%, and 30.00%, respectively. The values of AUCs were 0.87, 0.81, and 0.76, for random forest model, support vector machine model, and neural network model in the test dataset, respectively. Radiomic method with proper model may have a potential role in predicting the edema area around basal ganglia hemorrhage. It can be used as a secondary group in the diagnosis of edema area around basal ganglia hemorrhage.

Quantitative Analysis of Multiphase Contrast-Enhanced CT Images: A Pilot Study of Preoperative Prediction of Fat-Poor Angiomyolipoma and Renal Cell Carcinoma

OBJECTIVE. The objective of our study was to preoperatively predict fat-poor angiomyolipoma (fp-AML) and renal cell carcinoma (RCC) by conducting quantitative analysis of contrast-enhanced CT images. MATERIALS AND METHODS. One hundred fifteen patients with a pathologic diagnosis of fp-AML or RCC from a single institution were randomly allocated into a train set (tumor size: mean ± SD, 4.50 ± 2.62 cm) and test set (tumor size: 4.32 ± 2.73 cm) after data augmentation. High-dimensional histogram-based features, texture-based features, and Laws features were first extracted from CT images and were then combined as different combinations sets to construct a logistic prediction model based on the least absolute shrinkage and selection operator procedure for the prediction of fp-AML and RCC. Prediction performances were assessed by classification accuracy, area under the ROC curve (AUC), positive predictive value, negative predictive value, true-positive rate, and false-positive rate (FPR). In addition, we also investigated the effects of different gray-scales of quantitative features on prediction performances. RESULTS. The following combination sets of features achieved satisfying performances in the test set: histogram-based features (mean AUC = 0.8492, mean classification accuracy = 91.01%); histogram-based features and texture-based features (mean AUC = 0.9244, mean classification accuracy = 91.81%); histogram-based features and Laws features (mean AUC = 0.8546, mean classification accuracy = 88.76%); and histogram-based features, texture-based features, and Laws features (mean AUC = 0.8925, mean classification accuracy = 90.36%). The different quantitative gray-scales did not have an obvious effect on prediction performances. CONCLUSION. The integration of histogram-based features with texture-based features and Laws features provided a potential biomarker for the preoperative diagnosis of fp-AML and RCC. The accurate diagnosis of benign or malignant renal masses would help to make the clinical decision for radical surgery or close follow-up.

Automated detection of hippocampal sclerosis using clinically empirical and radiomics features

OBJECTIVE

Temporal lobe epilepsy is a common form of epilepsy that might be amenable to surgery. However, magnetic resonance imaging (MRI)-negative hippocampal sclerosis (HS) can hamper early diagnosis and surgical intervention for patients in clinical practice, resulting in disease progression. Our aim was to automatically detect and evaluate the structural alterations of HS.

METHODS

Eighty patients with pharmacoresistant epilepsy and histologically proven HS and 80 healthy controls were included in the study. Two automated classifiers relying on clinically empirical and radiomics features were developed to detect HS. Cross-validation was implemented on all participants, and specificity was assessed in the 80 controls. The performance, robustness, and clinical utility of the model were also evaluated. Structural analysis was performed to investigate the morphological abnormalities of HS.

RESULTS

The computational model based on clinical empirical features showed excellent performance, with an area under the curve (AUC) of 0.981 in the primary cohort and 0.993 in the validation cohort. One of the features, gray-white matter boundary blurring in the temporal pole, exhibited the highest weight in model performance. Another model based on radiomics features also showed satisfactory performance, with AUC of 0.997 in the primary cohort and 0.978 in the validation cohort. In particular, the model improved the detection rate of MRI-negative HS to 96.0%. The novel feature of cortical folding complexity of the temporal pole not only played a crucial role in the classifier but also had significant correlation with disease duration.

SIGNIFICANCE

Machine learning with quantitative clinical and radiomics features is shown to improve HS detection. HS-related structural alterations were similar in the MRI-positive and MRI-negative HS patient groups, indicating that misdiagnosis originates mainly from empirical interpretation. The cortical folding complexity of the temporal pole is a potentially valuable feature for exploring the nature of HS.

Radiomics: a Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer

In order to predict the 3-year recurrence of advanced ovarian cancer before surgery, we retrospective collected 94 patients to analyze by using a novel radiomics method. A total of 575 3D imaging features used for radiomics analysis were extracted, and 7 features were selected from computed tomography (CT) images that were most strongly associated with 3-year clinical recurrence-free survival (CRFS) probability to build a radiomics signature. The area under the Receiver Operating Characteristic (ROC) curve (AUC) of 0.8567 (95% CI: 0.7251-0.9498) and 0.8533 (95% CI: 0.7231-0.9671) were obtained in the training cohort and validation cohort with the logistic regression classification model respectively. Experimental results show that CT-based radiomics features were closely associated with the recurrence of advanced ovarian cancer. It is possible to prejudge the recurrence of ovarian cancer before surgery.

A six-gene prognostic model predicts overall survival in bladder cancer patients

Background

The fatality and recurrence rates of bladder cancer (BC) have progressively increased. DNA methylation is an influential regulator associated with gene transcription in the pathogenesis of BC. We describe a comprehensive epigenetic study performed to analyse DNA methylation-driven genes in BC.

Methods

Data related to DNA methylation, the gene transcriptome and survival in BC were downloaded from The Cancer Genome Atlas (TCGA). MethylMix was used to detect BC-specific hyper-/hypo-methylated genes. Metascape was used to carry out gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related methylation-driven genes. Subsequently, we developed a six-gene risk evaluation model and a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the six genes.

Results

In total, 167 methylation-driven genes were identified. Based on the LASSO Cox regression, six genes, i.e., ARHGDIB, LINC00526, IDH2, ARL14, GSTM2, and LURAP1, were selected for the development of a risk evaluation model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 1.679e−05). The area under the curve (AUC) of this model was 0.698 at 3 years of OS. The verification performed in subgroups demonstrated the validity of the model. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.694. The methylation levels of IDH2 and ARL14 were appreciably related to the survival results. In addition, the methylation and gene expression-matched survival analysis revealed that ARHGDIB and ARL14 could be used as independent prognostic indicators. Among the six genes, 6 methylation sites in ARHGDIB, 3 in GSTM2, 1 in ARL14, 2 in LINC00526 and 2 in LURAP1 were meaningfully associated with BC prognosis. In addition, several abnormal methylated sites were identified as linked to gene expression.

Conclusion

We discovered differential methylation in BC patients with better and worse survival and provided a risk evaluation model by merging six gene markers with clinical characteristics.

Radiomic analysis for pretreatment prediction of response to neoadjuvant chemotherapy in locally advanced cervical cancer: A multicentre study

BACKGROUND

We aimed to investigate whether pre-therapeutic radiomic features based on magnetic resonance imaging (MRI) can predict the clinical response to neoadjuvant chemotherapy (NACT) in patients with locally advanced cervical cancer (LACC).

METHODS

A total of 275 patients with LACC receiving NACT were enrolled in this study from eight hospitals, and allocated to training and testing sets (2:1 ratio). Three radiomic feature sets were extracted from the intratumoural region of T1-weighted images, intratumoural region of T2-weighted images, and peritumoural region of T2-weighted images before NACT for each patient. With a feature selection strategy, three single sequence radiomic models were constructed, and three additional combined models were constructed by combining the features of different regions or sequences. The performance of all models was assessed using receiver operating characteristic curve.

FINDINGS

The combined model of the intratumoural zone of T1-weighted images, intratumoural zone of T2-weighted images,and peritumoural zone of T2-weighted images achieved an AUC of 0.998 in training set and 0.999 in testing set, which was significantly better (p < .05) than the other radiomic models. Moreover, no significant variation in performance was found if different training sets were used.

INTERPRETATION

This study demonstrated that MRI-based radiomic features hold potential in the pretreatment prediction of response to NACT in LACC, which could be used to identify rightful patients for receiving NACT avoiding unnecessary treatment.

Radiomic analysis of multiparametric magnetic resonance imaging for differentiating skull base chordoma and chondrosarcoma

PURPOSE

Patients with skull base chordoma and chondrosarcoma have different prognoses and are not readily differentiated preoperatively on imaging. Multiparametric magnetic resonance imaging (MRI) is a routine diagnostic tool that can noninvasively characterize the salient characteristics of tumors. In the present study, we developed and validated a preoperative multiparametric MRI-based radiomic signature for differentiating these tumors.

METHOD

This retrospective study enrolled 210 patients and consecutively divided them into the primary and validation cohorts. A total of 1941 radiomic features were acquired from preoperative T1-weighted imaging, T2-weighted imaging and contrast-enhanced T1-weighted imaging for each patient. The most discriminative features were selected by minimum-redundancy maximum-relevancy and recursive feature elimination algorithms in the primary cohort. The multiparametric and single-sequence MRI signatures were constructed with the selected features using a support vector machine model in the primary cohort. The ability of the novel radiomic signatures to differentiate chordoma from chondrosarcoma were assessed using receiver operating characteristic curve analysis in the validation cohort.

RESULTS

The multiparametric radiomic signature, which consisted of 11 selected features, reached an area under the receiver operating characteristic curve of 0.9745 and 0.8720 in the primary and validation cohorts, respectively. Moreover, compared with each single-sequence MRI signature, the multiparametric radiomic signature exhibited better classification performance with significant improvement (p <  0.05, Delong's test) in the primary cohorts.

CONCLUSION

By combining features from three MRI sequences, the multiparametric radiomics signature can accurately and robustly differentiate skull base chordoma from chondrosarcoma.

A Computed Tomography-Based Radiomic Prognostic Marker of Advanced High-Grade Serous Ovarian Cancer Recurrence: A Multicenter Study

Objectives: We used radiomic analysis to establish a radiomic signature based on preoperative contrast enhanced computed tomography (CT) and explore its effectiveness as a novel recurrence risk prognostic marker for advanced high-grade serous ovarian cancer (HGSOC). Methods: This study had a retrospective multicenter (two hospitals in China) design and a radiomic analysis was performed using contrast enhanced CT in advanced HGSOC (FIGO stage III or IV) patients. We used a minimum 18-month follow-up period for all patients (median 38.8 months, range 18.8-81.8 months). All patients were divided into three cohorts according to the timing of their surgery and hospital stay: training cohort (TC) and internal validation cohort (IVC) were from one hospital, and independent external validation cohort (IEVC) was from another hospital. A total of 620 3-D radiomic features were extracted and a Lasso-Cox regression was used for feature dimension reduction and determination of radiomic signature. Finally, we combined the radiomic signature with seven common clinical variables to develop a novel nomogram using a multivariable Cox proportional hazards model. Results: A final 142 advanced HGSOC patients were enrolled. Patients were successfully divided into two groups with statistically significant differences based on radiomic signature, consisting of four radiomic features (log-rank test P = 0.001, <0.001, <0.001 for TC, IVC, and IEVC, respectively). The discrimination accuracies of radiomic signature for predicting recurrence risk within 18 months were 82.4% (95% CI, 77.8-87.0%), 77.3% (95% CI, 74.4-80.2%), and 79.7% (95% CI, 73.8-85.6%) for TC, IVC, and IEVC, respectively. Further, the discrimination accuracies of radiomic signature for predicting recurrence risk within 3 years were 83.4% (95% CI, 77.3-89.6%), 82.0% (95% CI, 78.9-85.1%), and 70.0% (95% CI, 63.6-76.4%) for TC, IVC, and IEVC, respectively. Finally, the accuracy of radiomic nomogram for predicting 18-month and 3-year recurrence risks were 84.1% (95% CI, 80.5-87.7%) and 88.9% (95% CI, 85.8-92.5%), respectively. Conclusions: Radiomic signature and radiomic nomogram may be low-cost, non-invasive means for successfully predicting risk for postoperative advanced HGSOC recurrence before or during the perioperative period. Radiomic signature is a potential prognostic marker that may allow for individualized evaluation of patients with advanced HGSOC.

The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

Radiomics Analysis of DTI Data to Assess Vision Outcome After Intravenous Methylprednisolone Therapy in Neuromyelitis Optic Neuritis

BACKGROUND

Neuromyelitis optica-optic neuritis (NMO-ON) patients are routinely treated with intravenous methylprednisolone (IVMP). For the patients nonresponsive to IVMP, more effective but aggressive therapy of plasma exchange (PE) should be employed instead of IVMP in the first line.

PURPOSE

To assess the visual outcomes of NMO-ON patients after IVMP by radiomics analysis of whole brain diffusion tensor imaging (DTI) data.

STUDY TYPE

Retrospective.

POPULATION

In all, 57 NMO-ON patients receiving IVMP therapy for 3 days.

FIELD STRENGTH/SEQUENCE

3.0T; DTI images acquired by a single-shot echo planar image sequence; T₁ images acquired by 3D fast spoiled gradient echo (3D-FSPGR) MRI.

ASSESSMENT

In all, 200 DTI measures were extracted from the DTI data and employed as features to construct a radiomics assessment model for visual outcomes of NMO-ON patients after IVMP. The assessment performance was evaluated by area under the receiver operating characteristic curve (AUC), classification accuracy (ACC), sensitivity, specificity, and positive and negative predicted values (PPV and NPV). The selected DTI measures would reveal the white matter impairments related to visual recovery of NMO-ON patients.

STATISTICAL TESTS

The relationship between the selected DTI measures and the clinical visual characteristics were investigated by Pearson correlation, Spearman's rank correlation, and one-way analysis of variance analysis.

RESULTS

The radiomics model obtained an ACC of 73.68% (P = 0.002), AUC of 0.7931, sensitivity of 0.6207, specificity of 0.8571, PPV of 0.8182, and NPV of 0.6857 in assessing visual outcomes of the NMO-ON patients after IVMP treatment. The selected DTI measures revealed white matter impairments related to the visual outcomes in the white matter tracts of vision-relevant regions, motor-related regions, and corpus callosum. The white matter impairments were found significantly correlated with the disease duration and the length of lesions in the optic nerve.

DATA CONCLUSION

Radiomics analysis of DTI data has great potential in assessing visual outcomes of NMO-ON patients after IVMP therapy.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 4 J. Magn. Reson. Imaging 2019;49:1365-1373.

A Novel MRI-Based Radiomics Model for Predicting Recurrence in Chordoma

Chordoma is a rare primary malignant tumor. For evaluating the related factors of postoperative recurrence probability of chordoma before surgery, we retrospective collected 80 patients to analyze by using a novel radiomics method. A total of 620 3D imaging features used for radiomics analysis were extracted, and 5 features were selected from T2-weighted (T2-w) magnetic resonance imaging (MRI) that were most strongly associated with 4-year recurrence probability to build a radiomics signature. Verification by logistic regression classification model, the area under the receiver operating characteristic curve and accuracy was 0.8600 (95% CI: 0.7226-0.9824) and 85.00% in the training cohort, respectively, while in the validation cohort was 0.8568 (95% CI: 0.7327-0.9758) and 85.00%. Experimental results show that T2-w MRI-based radiomics signature is closely associated with the recurrence of chordoma. It is possible to prejudge the recurrence of chordoma before surgery.

Axonal chronic injury in treatment-naïve HIV+ adults with asymptomatic neurocognitive impairment and its relationship with clinical variables and cognitive status

Background

HIV is a neurotropic virus, and it can bring about neurodegeneration and may even result in cognitive impairments. The precise mechanism of HIV-associated white matter (WM) injury is unknown. The effects of multiple clinical contributors on WM impairments and the relationship between the WM alterations and cognitive performance merit further investigation.

Methods

Diffusion tensor imaging (DTI) was performed in 20 antiretroviral-naïve HIV-positive asymptomatic neurocognitive impairment (ANI) adults and 20 healthy volunteers. Whole-brain analysis of DTI metrics between groups was conducted by employing tract-based spatial statistics (TBSS), including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). DTI parameters were correlated with clinical variables (age, CD4⁺ cell count, CD4⁺/CD8⁺ ratio, plasma viral load and duration of HIV infection) and multiple cognitive tests by using multilinear regression analyses.

Results

DTI quantified diffusion alterations in the corpus callosum and corona radiata (MD increased significantly, P < 0.05) and chronic axonal injury in the corpus callosum, corona radiata, internal capsule, external capsule, posterior thalamic radiation, sagittal stratum, and superior longitudinal fasciculus (AD increased significantly, P < 0.05). The impairments in the corona radiata had significant correlations with the current CD4⁺/CD8⁺ ratios. Increased MD or AD values in multiple white matter structures showed significant associations with many cognitive domain tests.

Conclusions

WM impairments are present in neurologically asymptomatic HIV+ adults, periventricular WM (corpus callosum and corona radiata) are preferential occult injuries, which is associated with axonal chronic damage rather than demyelination. Axonopathy may exist before myelin injury. DTI-TBSS is helpful to explore the WM microstructure abnormalities and provide a new perspective for the investigation of the pathomechanism of HIV-associated WM injury.

The Aging Brain With HIV Infection: Effects of Alcoholism or Hepatitis C Comorbidity

As successfully treated individuals with Human Immunodeficiency Virus (HIV)-infected age, cognitive and health challenges of normal aging ensue, burdened by HIV, treatment side effects, and high prevalence comorbidities, notably, Alcohol Use Disorders (AUD) and Hepatitis C virus (HCV) infection. In 2013, people over 55 years old accounted for 26% of the estimated number of people living with HIV (~1.2 million). The aging brain is increasingly vulnerable to endogenous and exogenous insult which, coupled with HIV infection and comorbid risk factors, can lead to additive or synergistic effects on cognitive and motor function. This paper reviews the literature on neuropsychological and in vivo Magnetic Resonance Imaging (MRI) evaluation of the aging HIV brain, while also considering the effects of comorbidity for AUD and HCV.

Imaging studies of the HIV-infected brain

Human immunodeficiency virus (HIV) enters the brain early after infecting humans and may remain in the central nervous system despite successful antiretroviral treatment. Many neuroimaging techniques were used to study HIV+ patients with or without opportunistic infections. These techniques assessed abnormalities in brain structures (using computed tomography, structural magnetic resonance imaging (MRI), diffusion MRI) and function (using functional MRI at rest or during a task, and perfusion MRI with or without a contrast agent). In addition, single-photon emission computed tomography with various tracers (e.g., thallium-201, Tc99-HMPAO) and positron emission tomography with various agents (e.g., [18F]-dexoyglucose, [11C]-PiB, and [11C]-TSPO tracers), were applied to study opportunistic infections or HIV-associated neurocognitive disorders. Neuroimaging provides diagnoses and biomarkers to quantitate the severity of brain injury or to monitor treatment effects, and may yield insights into the pathophysiology of HIV infection. As the majority of antiretroviral-stable HIV+ patients are living longer, age-related comorbid disorders (e.g., additional neuroinflammation, cerebrovascular disorders, or other dementias) will need to be considered. Other highly prevalent conditions, such as substance use disorders, psychiatric illnesses, and the long-term effects of combined antiretroviral therapy, all may lead to additional brain injury. Neuroimaging studies could provide knowledge regarding how these comorbid conditions impact the HIV-infected brain. Lastly, specific molecular imaging agents may be needed to assess the central nervous system viral reservoir.