Diffusion Tensor Imaging Reveals Microstructural Heterogeneity of Normal-Appearing White Matter and Related Cognitive Dysfunction in Glioma Patients

Perioperative Neurocognitive Function in Glioma Surgery

PURPOSE OF REVIEW

This review provides a concise overview of the recent literature regarding preoperative and postoperative neurocognitive functioning (NCF) in patients with glioma. Brief discussion also covers contemporary intraoperative brain mapping work, with a focus on potential influence of mapping upon NCF outcomes following awake surgery.

RECENT FINDINGS

Most patients with glioma exhibit preoperative NCF impairment, with severity varying by germ line and tumoral genetics, tumor grade, and lesion location, among other characteristics. Literature regarding postoperative NCF changes is mixed, though numerous studies indicate a majority of patients exhibit immediate and short-term worsening. This is often followed by recovery over several months; however, a substantial portion of patients harbor persisting declines. Decline appears related to surgically-induced structural and functional brain alterations, both local and distal to the tumor and resection cavity. Importantly, NCF decline may be mitigated to some extent by intraoperative brain mapping, including mapping of both language-mediated and nonverbal functions. Research regarding perioperative NCF in patients with glioma has flourished over recent years. While this has increased our understanding of contributors to NCF and risk of decline associated with surgical intervention, more work is needed to better preserve NCF throughout the disease course.

Multiple sclerosis clinical forms classification with graph convolutional networks based on brain morphological connectivity

Multiple Sclerosis (MS) is an autoimmune disease that combines chronic inflammatory and neurodegenerative processes underlying different clinical forms of evolution, such as relapsing-remitting, secondary progressive, or primary progressive MS. This identification is usually performed by clinical evaluation at the diagnosis or during the course of the disease for the secondary progressive phase. In parallel, magnetic resonance imaging (MRI) analysis is a mandatory diagnostic complement. Identifying the clinical form from MR images is therefore a helpful and challenging task. Here, we propose a new approach for the automatic classification of MS forms based on conventional MRI (i.e., T1-weighted images) that are commonly used in clinical context. For this purpose, we investigated the morphological connectome features using graph based convolutional neural network. Our results obtained from the longitudinal study of 91 MS patients highlight the performance (F1-score) of this approach that is better than state-of-the-art as 3D convolutional neural networks. These results open the way for clinical applications such as disability correlation only using T1-weighted images.

Diffusion MRI anomaly detection in glioma patients

Diffusion-MRI (dMRI) measures molecular diffusion, which allows to characterize microstructural properties of the human brain. Gliomas strongly alter these microstructural properties. Delineation of brain tumors currently mainly relies on conventional MRI-techniques, which are, however, known to underestimate tumor volumes in diffusely infiltrating glioma. We hypothesized that dMRI is well suited for tumor delineation, and developed two different deep-learning approaches. The first diffusion-anomaly detection architecture is a denoising autoencoder, the second consists of a reconstruction and a discrimination network. Each model was exclusively trained on non-annotated dMRI of healthy subjects, and then applied on glioma patients' data. To validate these models, a state-of-the-art supervised tumor segmentation network was modified to generate groundtruth tumor volumes based on structural MRI. Compared to groundtruth segmentations, a dice score of 0.67 ± 0.2 was obtained. Further inspecting mismatches between diffusion-anomalous regions and groundtruth segmentations revealed, that these colocalized with lesions delineated only later on in structural MRI follow-up data, which were not visible at the initial time of recording. Anomaly-detection methods are suitable for tumor delineation in dMRI acquisitions, and may further enhance brain-imaging analysis by detection of occult tumor infiltration in glioma patients, which could improve prognostication of disease evolution and tumor treatment strategies.

An MRI Study Combining Virtual Brain Grafting and Surface-Based Morphometry Analysis to Investigate Contralateral Alterations in Cortical Morphology in Patients With Diffuse Low-Grade Glioma

BACKGROUND

The human brain has ability to reorganize itself in response to glioma. However, the mechanism of cortical reorganization remains unclear.

PURPOSE

To investigate alterations in cortical thickness and local gyration index (LGI) in patients with unilateral frontal lobe diffuse low-grade glioma (DLGG).

STUDY TYPE

Retrospective.

SUBJECTS

Ninety-nine patients with histopathologically proven DLGG invading the left frontal lobe (LF; N = 56) or the right frontal lobe (RF; N = 43), and healthy controls (HC; N = 53).

FIELD STRENGTH/SEQUENCE

3.0 T, 3D T1-weighted images and gadolinium enhanced T1-weighted images using magnetization-prepared rapid gradient echo sequence, T2-weighted images, and fluid-attenuated inversion recovery using turbo spin echo sequence.

ASSESSMENT

In patients with DLGG, virtual brain grafting combined with Freesurfer was utilized to enable automated cortical thickness and LGI calculation. In HC, standard FreeSurfer pipeline was applied to calculate these measures. Radiomic features were extracted from glioma using Pyradiomic software.

STATISTICAL TESTS

General linear model and Pearson's correlation analysis. A P value <0.05 was considered statistically significant.

RESULTS

For LF patients, there was significantly increased cortical thickness in the rostral middle frontal gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual and medial orbitofrontal (MOF) gyrus in contralateral hemisphere. For RF patients, there was significantly increased cortical thickness in the middle temporal, lateral occipital extending to isthmus cingulate gyrus, significantly reduced cortical thickness in the precentral gyrus and hypogyrification in the lingual gyrus in the contralateral hemisphere. A negative association between four textural features of DLGG and LGI in the right MOF gyrus of LF group was found (r = -0.609, -0.442, -0.545, and -0.417, respectively).

DATA CONCLUSION

Cortical thickness compensation was shown in contralateral homotopic location and some distant contralateral regions. Additionally, there was decreased cortical thickness in the contralateral precentral gyrus and hypogyrification in contralateral lingual gyrus.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Cognitive functioning in a cohort of high-grade glioma patients

BACKGROUND

High grade gliomas are associated with cognitive problems. The aim of the study was to investigate cognitive functioning in a cohort of patients with high grade glioma, according to isocitrate dehydrogenase (IDH) and methyl guanine methyl transferase (MGMT) status and other clinical characteristics.

PATIENTS AND METHODS

The patients with the high-grade glioma treated in Slovenia in given period of time were included in study. Postoperatively they completed neuropsychological assessment consisting of Slovenian Verbal Learning Test, Slovenian Controlled Oral Word Association Test, Trail Making Test Part A and B and self-evaluation questionnaire. We analysed results (z-scores and dichotomized results) also according to IDH mutation and MGMT methylation. We examined differences between groups using T-test, Mann-Whitney U, χ² and Kendall's Tau tests.

RESULTS

Out of 275 patients in the cohort, we included 90. Forty-six percent of patients were unable to participate due to poor performance status and other conditions related to tumour. Patients with the IDH mutation were younger, with better performance status, larger proportions of grade III tumours and MGMT methylation. In this group cognitive functioning is significantly better in the domains of immediate recall, short delayed recall and delayed recall, and in the fields of executive functioning and recognition. There were no differences in cognitive functioning in regard to MGMT status. Grade III tumours were associated with more frequent MGMT methylation. Self-assessment proved week tool, associated only with immediate recall.

CONCLUSIONS

We found no differences in cognitive functioning according to MGMT status, but cognition was better when IDH mutation was present. In a cohort study of patients with high-grade glioma, almost half were unable to participate in a study, which points to an overrepresentation of patients with better cognitive functioning in the research.

Controllability and Robustness of Functional and Structural Connectomic Networks in Glioma Patients

Previous studies suggest that the topological properties of structural and functional neural networks in glioma patients are altered beyond the tumor location. These alterations are due to the dynamic interactions with large-scale neural circuits. Understanding and describing these interactions may be an important step towards deciphering glioma disease evolution. In this study, we analyze structural and functional brain networks in terms of determining the correlation between network robustness and topological features regarding the default-mode network (DMN), comparing prognostically differing patient groups to healthy controls. We determine the driver nodes of these networks, which are receptive to outside signals, and the critical nodes as the most important elements for controllability since their removal will dramatically affect network controllability. Our results suggest that network controllability and robustness of the DMN is decreased in glioma patients. We found losses of driver and critical nodes in patients, especially in the prognostically less favorable IDH wildtype (IDHwt) patients, which might reflect lesion-induced network disintegration. On the other hand, topological shifts of driver and critical nodes, and even increases in the number of critical nodes, were observed mainly in IDH mutated (IDHmut) patients, which might relate to varying degrees of network plasticity accompanying the chronic disease course in some of the patients, depending on tumor growth dynamics. We hereby implement a novel approach for further exploring disease evolution in brain cancer under the aspects of neural network controllability and robustness in glioma patients.

Associating IDH and TERT Mutations in Glioma with Diffusion Anisotropy in Normal-Appearing White Matter

BACKGROUND AND PURPOSE

IDH and TERT mutations might infiltratively manifest within normal-appearing white matter with specific phenotypes such as microstructural changes undetectable by standard MR imaging contrasts but potentially associable with DTI variables. The aim of this retrospective glioma study was to statistically investigate IDH and TERT associations and classifications with DTI reported microstructure in normal-appearing white matter.

MATERIALS AND METHODS

Retrospective data from patients imaged between March 2012 and February 2016 were analyzed by grouping them as IDH-TERT subgroups and by IDH and TERT mutation status. DTI variables in the IDH-TERT subgroups were first identified by the Kruskal-Wallis test, followed by Dunn-Šidák multiple comparisons with Bonferroni correction. IDH and TERT mutations were compared with the Mann-Whitney U test. Classification by thresholding was tested using receiver operating characteristic analysis.

RESULTS

Of 170 patients, 70 patients (mean age, 43.73 [SD, 15.32] years; 40 men) were included. Whole-brain normal-appearing white matter fractional anisotropy (FA) and relative anisotropy (RA) (P = .002) were significantly higher and the contralateral-ipsilateral hemispheric differences, ΔFA and ΔRA, (P < .001) were significantly lower in IDHonly patients compared with TERTonly, with a higher whole-brain normal-appearing white matter FA and RA (P = .01) and ΔFA and ΔRA (P = .002) compared to double positive patients. Whole-brain normal-appearing white matter ADC (P = .02), RD (P = .001), λ2 (P = .001), and λ3 (P = .001) were higher in IDH wild-type. Whole-brain normal-appearing white matter λ1 (AD) (P = .003), FA (P < .001), and RA (P = .003) were higher, but Δλ1 (P = .002), ΔFA, and ΔRA (P < .001) were lower in IDH mutant versus IDH wild-type. ΔFA (P = .01) and ΔRA (P = .02) were significantly higher in TERT mutant versus TERT wild-type.

CONCLUSIONS

Axial and nonaxial diffusivities, anisotropy indices in the normal-appearing white matter and their interhemispheric differences demonstrated microstructural differences between IDH and TERT mutations, with the potential for classification methods.

Cognitive issues in patients with IDH mutant gliomas: from neuroscience to clinical neuropsychology

PURPOSE

The understanding of cognitive symptoms in patients with IDH-Mutant gliomas (IDH-Mut) is rapidly developing. In this article, we summarize the neuroscientific knowledge base regarding the influence of IDH-Mut tumors and their treatment on cognition and provide guidance regarding the management of these symptoms in patients.

METHODS

We performed a review of peer reviewed publications relevant to IDH-Mut glioma and cognitive outcomes and provide an overview of the literature as well as a case example to clarify management strategies.

RESULTS

At the time of presentation, patients with IDH-Mut gliomas have a favorable cognitive profile as compared with those with IDH-wild type (WT) tumors. The relatively low cognitive burden may reflect the slower growth rate of IDH-Mut tumors, which is less disruptive to both local and widespread neural networks. Human connectomic research using a variety of modalities has demonstrated relatively preserved network efficiency in patients with IDH-Mut gliomas as compared with IDH-WT tumors. Risk of cognitive decline from surgery can potentially be mitigated by careful integration of intra-operative mapping. Longer term cognitive risks of tumor treatment, including chemotherapy and radiation, are best managed by instituting neuropsychological assessment as part of the long-term care of patients with IDH-Mutant glioma. A specific timeline for such integrative care is provided.

CONCLUSIONS

Given the relative recency of the IDH-mutation based classification of gliomas, as well as the long time course of this disease, a thoughtful and comprehensive strategy to studying patient outcomes and devising methods of cognitive risk reduction is required.

Meningioma microstructure assessed by diffusion MRI: An investigation of the source of mean diffusivity and fractional anisotropy by quantitative histology

BACKGROUND

Mean diffusivity (MD) and fractional anisotropy (FA) from diffusion MRI (dMRI) have been associated with cell density and tissue anisotropy across tumors, but it is unknown whether these associations persist at the microscopic level.

PURPOSE

To quantify the degree to which cell density and anisotropy, as determined from histology, account for the intra-tumor variability of MD and FA in meningioma tumors. Furthermore, to clarify whether other histological features account for additional intra-tumor variability of dMRI parameters.

MATERIALS AND METHODS

We performed ex-vivo dMRI at 200 μm isotropic resolution and histological imaging of 16 excised meningioma tumor samples. Diffusion tensor imaging (DTI) was used to map MD and FA, as well as the in-plane FA (FA_IP). Histology images were analyzed in terms of cell nuclei density (CD) and structure anisotropy (SA; obtained from structure tensor analysis) and were used separately in a regression analysis to predict MD and FA_IP, respectively. A convolutional neural network (CNN) was also trained to predict the dMRI parameters from histology patches. The association between MRI and histology was analyzed in terms of out-of-sample (R²_OS) on the intra-tumor level and within-sample R² across tumors. Regions where the dMRI parameters were poorly predicted from histology were analyzed to identify features apart from CD and SA that could influence MD and FA_IP, respectively.

RESULTS

Cell density assessed by histology poorly explained intra-tumor variability of MD at the mesoscopic level (200 μm), as median R²_OS = 0.04 (interquartile range 0.01-0.26). Structure anisotropy explained more of the variation in FA_IP (median R²_OS = 0.31, 0.20-0.42). Samples with low R²_OS for FA_IP exhibited low variations throughout the samples and thus low explainable variability, however, this was not the case for MD. Across tumors, CD and SA were clearly associated with MD (R² = 0.60) and FA_IP (R² = 0.81), respectively. In 37% of the samples (6 out of 16), cell density did not explain intra-tumor variability of MD when compared to the degree explained by the CNN. Tumor vascularization, psammoma bodies, microcysts, and tissue cohesivity were associated with bias in MD prediction based solely on CD. Our results support that FA_IP is high in the presence of elongated and aligned cell structures, but low otherwise.

CONCLUSION

Cell density and structure anisotropy account for variability in MD and FA_IP across tumors but cell density does not explain MD variations within the tumor, which means that low or high values of MD locally may not always reflect high or low tumor cell density. Features beyond cell density need to be considered when interpreting MD.

Deep Learning Classifies Low- and High-Grade Glioma Patients with High Accuracy, Sensitivity, and Specificity Based on Their Brain White Matter Networks Derived from Diffusion Tensor Imaging

Classifying low-grade glioma (LGG) patients from high-grade glioma (HGG) is one of the most challenging tasks in planning treatment strategies for brain tumor patients. Previous studies derived several handcrafted features based on the tumor's texture and volume from magnetic resonance images (MRI) to classify LGG and HGG patients. The accuracy of classification was moderate. We aimed to classify LGG from HGG with high accuracy using the brain white matter (WM) network connectivity matrix constructed using diffusion tensor tractography. We obtained diffusion tensor images (DTI) of 44 LGG and 48 HGG patients using routine clinical imaging. Fiber tractography and brain parcellation were performed for each patient to obtain the fractional anisotropy, axial diffusivity, radial diffusivity, and mean diffusivity weighted connectivity matrices. We used a deep convolutional neural network (DNN) for classification and the gradient class activation map (GRAD-CAM) technique to identify the neural connectivity features focused on by the DNN. DNN could classify both LGG and HGG with 98% accuracy. The sensitivity and specificity values were above 0.98. GRAD-CAM analysis revealed a distinct WM network pattern between LGG and HGG patients in the frontal, temporal, and parietal lobes. Our results demonstrate that glioma affects the WM network in LGG and HGG patients differently.

White matter alterations associated with lifetime and current depression in adolescents: Evidence for cingulum disruptions

INTRODUCTION

Compared to research on adults with depression, relatively little work has examined white matter microstructure differences in depression arising earlier in life. Here we tested hypotheses about disruptions to white matter structure in adolescents with current and past depression, with an a priori focus on the cingulum bundles, uncinate fasciculi, corpus collosum, and superior longitudinal fasciculus.

METHODS

One hundred thirty-one children from the Preschool Depression Study were assessed using a Human Connectome Project style diffusion imaging sequence which was processed with HCP pipelines and TRACULA to generate estimates of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD).

RESULTS

We found that reduced FA, reduced AD, and increased RD in the dorsal cingulum bundle were associated with a lifetime diagnosis of major depression and greater cumulative and current depression severity. Reduced FA, reduced AD, and increased RD in the ventral cingulum were associated with greater cumulative depression severity.

CONCLUSION

These findings support the emergence of white matter differences detected in adolescence associated with earlier life and concurrent depression. They also highlight the importance of connections of the cingulate to other brain regions in association with depression, potentially relevant to understanding emotion dysregulation and functional connectivity differences in depression.

Self-Referential Processing and Resting-State Functional MRI Connectivity of Cortical Midline Structures in Glioma Patients

Metacognition has only scarcely been investigated in brain tumor patients. It is unclear if and how the tumor-lesioned brain might be able to maintain an adequate sense-of-self. As cortical midline structures (CMS) are regarded as essential for self-referential mental activity, we investigated resting-state fMRI connectivity (FC) of CMS to the default-mode network (DMN) and to the whole brain, comparing glioma patients and matched controls. Subjects furthermore performed a trait judgement (TJ), a trait recall task (TR), and neuropsychological testing. In the TJ, adjectives had to be ascribed as self- or non-self-describing, assessing the self-serving effect (SSE), a normally observed bias for positive traits. In the TR, the mnemic neglect effect (MNE), a memory advantage for positive traits, was tested. The groups were compared and partial correlations between FC and test metrics were analyzed. Although patients were significantly impaired in terms of verbal memory, groups did not differ in the SSE or the MNE results, showing preserved metacognitive abilities in patients. FC of CMS to the DMN was maintained, but was significantly decreased to whole brain in the patients. FC of the dorsomedial prefrontal cortex (DMPFC) to whole brain was correlated with the MNE in patients. Preserving the DMPFC in therapeutic interventions might be relevant for maintaining self-related verbal information processing in the memory domain in glioma patients.

Research on the Mechanism of Cognitive Decline in Patients With Acoustic Neuroma

Little is known about neuropsychological research on patients with acoustic neuroma (AN), especially cognitive neuropsychology. We aim to compare the cognitive function of patients with AN and healthy controls (HCs) and explore possible underlying mechanisms. Various neuropsychological assessments were performed on all participants. Tract-based spatial statistics (TBSS) was used to compare DTI metrics such as fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD), and mean diffusivity (MD). Correlation analysis was analyzed between DTI metrics and cognitive scales. Compared with the HC group, the AN group performed worse in the neuropsychological evaluations, and TBSS analysis showed widespread alteration of the FA, AD, RD, and MD, which correlated with the cognitive function. These white matter tracts include minor forceps, major forceps, anterior thalamic radiation, superior longitudinal fasciculus, corticospinal tract, and right inferior fronto-occipital fasciculus. Meanwhile, we found for the first time that cognitive decline was related to the decrease of FA in minor forceps, which can be used as a neurobiological marker of cognitive impairment in patients with AN. The occurrence of cognition impairment is common in patients with AN. Including neuropsychological evaluation in the routine clinical assessment and appropriate treatment may strengthen clinical management and improve the quality of life of patients.

DTI Abnormalities Related to Glioblastoma: A Prospective Comparative Study with Metastasis and Healthy Subjects

(1) Background: Glioblastoma multiforme (GBM) shows complex mechanisms of spreading of the tumor cells, up to remote areas, and little is still known of these mechanisms, thus we focused on MRI abnormalities observable in the tumor and the brain adjacent to the lesion, up to the contralateral hemisphere, with a special interest on tensor diffusion imaging informing on white matter architecture; (2) Material and Methods: volumes, macroscopic volume (MV), brain-adjacent-tumor (BAT) volume and abnormal color-coded DTI volume (aCCV), and region-of-interest samples (probe volumes, ipsi, and contra lateral to the lesion), with their MRI characteristics, apparent diffusion coefficient (ADC), fractional anisotropy (FA) values, and number of fibers (DTI fiber tracking) were analyzed in patients suffering GBM (n = 15) and metastasis (n = 9), and healthy subjects (n = 15), using ad hoc statistical methods (type I error = 5%) (3) Results: GBM volumes were larger than metastasis volumes, aCCV being larger in GBM and BAT ADC was higher in metastasis, ADC decreased centripetally in metastasis, FA increased centripetally either in GBM or metastasis, MV and BAT FA values were higher in GBM, ipsi FA values of GBM ROIs were higher than those of metastasis, and the GBM ipsi number of fibers was higher than the GBM contra number of fibers; (4) Conclusions: The MV, BAT and especially the aCCV, as well as their related water diffusion characteristics, could be useful biomarkers in oncology and functional oncology.

Glioma Grading Prediction Using Multiparametric Magnetic Resonance Imaging-based Radiomics Combined with Proton Magnetic Resonance Spectroscopy and Diffusion Tensor Imaging

PURPOSE

To evaluate the efficacy of three-dimensional (3D) segmentation-based radiomics analysis of multiparametric MRI combined with proton magnetic resonance spectroscopy (¹ H-MRS) and diffusion tensor imaging (DTI) in glioma grading.

METHOD

A total of 100 patients with histologically confirmed gliomas (grade II-IV) were examined using conventional MRI, ¹ H-MRS, and DTI. Tumor segmentations of T1-weighted imaging (T1WI), contrast-enhanced T1WI (T1WI+C), T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) mapping, and fractional anisotropy (FA) mapping were performed. In total, 396 radiomics features were extracted and reduced using basic tests and least absolute shrinkage and selection operator (LASSO) regression. The selected features of each sequence were combined, and logistic regression with ten-fold cross-validation was applied to develop the grading model. Sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC) were compared. The model developed from the training set was applied to the test set to measure accuracy. One optimal grading quantitative parameter was selected for each ¹ H-MRS and DTI analysis. A radiomics nomogram model including radiomics signature, quantitative parameters, and clinical features was developed.

RESULTS

T1WI+C exhibited the highest grading efficacy among single sequences (AUC, 0.92; sensitivity, 0.89; specificity, 0.85), but the efficacy of the combined model was higher (AUC, 0.97; sensitivity, 0.94; specificity, 0.91). The AUCs of all models exhibited high accuracy, and no significant differences were observed in AUCs between the training and test sets. The visualized nomogram was developed based on the combined radiomics signature and choline (Cho)/N-acetyl aspartate (NAA) from ¹ H-MRS.

CONCLUSION

Multiparametric MRI can be used to predict the pathological grading of HGG and LGG by combining radiomics features with quantitative parameters. The visualized nomogram may provide an intuitive assessment tool in clinical practice.

CLINICAL TRIAL REGISTRATION

This trial was not registered, as it was a retrospective study and was approved by the local institutional review board. This article is protected by copyright. All rights reserved.

Glioma invasion along white matter tracts: A dilemma for neurosurgeons

Invasive growth along white matter (WM) tracts is one of the most prominent clinicopathological features of glioma and is also an important reason for surgical treatment failure in glioma patients. A full understanding of relevant clinical features and mechanisms is of great significance for finding new therapeutic targets and developing new treatment regimens and strategies. Herein, we review the imaging and histological characteristics of glioma patients with WM tracts invasion and summarize the possible molecular mechanism. On this basis, we further discuss the correlation between glioma molecular typing, radiotherapy and tumor treating fields (TTFields) and the invasion of glioma along WM tracts.

T2 FLAIR Hyperintensity Volume Is Associated With Cognitive Function and Quality of Life in Clinically Stable Patients With Lower Grade Gliomas

Survival outcomes for patients with lower grade gliomas (LrGG) continue to improve. However, damage caused both by tumor growth and by the consequences of treatment often leads to significantly impaired cognitive function and quality of life (QoL). While neuropsychological testing is not routine, serial clinical MRIs are standard of care for patients with LrGG. Thus, having a greater understanding of MRI indicators of cognitive and QoL impairment risk could be beneficial to patients and clinicians. In this work we sought to test the hypothesis that in clinically stable LrGG patients, T2 FLAIR hyperintensity volumes at the time of cognitive assessment are associated with impairments of cognitive function and QoL and could be used to help identify patients for cognitive and QoL assessments and interventions. We performed anatomical MR imaging, cognitive testing and QoL assessments cross-sectionally in 30 clinically stable grade 2 and 3 glioma patients with subjective cognitive concerns who were 6 or more months post-treatment. Larger post-surgical T2 FLAIR volume at testing was significantly associated with lower cognitive performance, while pre-surgical tumor volume was not. Older patients had lower cognitive performance than younger patients, even after accounting for normal age-related declines in performance. Patients with Astrocytoma, IDH mutant LrGGs were more likely to show lower cognitive performance than patients with Oligodendroglioma, IDH mutant 1p19q co-deleted LrGGs. Previous treatment with combined radiation and chemotherapy was associated with poorer self-reported QoL, including self-reported cognitive function. This study demonstrates the importance of appreciating that LrGG patients may experience impairments in cognitive function and QoL over their disease course, including during periods of otherwise sustained clinical stability. Imaging factors can be helpful in identifying vulnerable patients who would benefit from cognitive assessment and rehabilitation.

Neurocognition and Health-Related Quality of Life Among Patients with Brain Tumors

Patients with brain tumors experience great symptom burden across various domains of functioning, with associated decreases in health-related quality of life and general well-being. Impaired neurocognitive functioning is among the primary concerns of these patients. Unfortunately, most patients will experience such impairment at some point in the disease. However, impaired neurocognitive functioning, symptom burden, and well-being vary according numerous patient-, tumor-, and treatment-related factors. Recent work has furthered our understanding of these contributors to patient functioning and health-related quality of life and also points to various potential targets for prevention and intervention strategies, though more efficacious treatments remain needed.

Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study

Background

Meningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis.

Methods

Five hundred and five patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1,015 radiomics features respectively obtained from nine different ROIs (brain-tumor interface (BTI)2-5mm; whole tumor; the amalgamation of the two regions) on contrast-enhanced T1-weighted imaging using least-absolute shrinkage and selection operator and random forest. Principal component analysis with varimax rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power.

Results

Model obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomics features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomics features (BTI4mm) enhanced model discrimination of invasive meningiomas.

Conclusions

The combined model (radiomics classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models and its clinical application may positively contribute to the management of meningioma patients.

Dissociation of structural and functional connectomic coherence in glioma patients

With diffuse infiltrative glioma being increasingly recognized as a systemic brain disorder, the macroscopically apparent tumor lesion is suggested to impact on cerebral functional and structural integrity beyond the apparent lesion site. We investigated resting-state functional connectivity (FC) and diffusion-MRI-based structural connectivity (SC) (comprising edge-weight (EW) and fractional anisotropy (FA)) in isodehydrogenase mutated (IDHmut) and wildtype (IDHwt) patients and healthy controls. SC and FC were determined for whole-brain and the Default-Mode Network (DMN), mean intra- and interhemispheric SC and FC were compared across groups, and partial correlations were analyzed intra- and intermodally. With interhemispheric EW being reduced in both patient groups, IDHwt patients showed FA decreases in the ipsi- and contralesional hemisphere, whereas IDHmut patients revealed FA increases in the contralesional hemisphere. Healthy controls showed strong intramodal connectivity, each within the structural and functional connectome. Patients however showed a loss in structural and reductions in functional connectomic coherence, which appeared to be more pronounced in IDHwt glioma patients. Findings suggest a relative dissociation of structural and functional connectomic coherence in glioma patients at the time of diagnosis, with more structural connectomic aberrations being encountered in IDHwt glioma patients. Connectomic profiling may aid in phenotyping and monitoring prognostically differing tumor types.

Ensemble Learning for Multiple Sclerosis Disability Estimation Using Brain Structural Connectivity

BACKGROUND

Multiple Sclerosis (MS) is an autoimmune inflammatory disease of the central nervous system characterized by demyelination and neurodegeneration processes. It leads to different clinical courses and degrees of disability that need to be anticipated by the neurologist for personalized therapy. Recently, machine learning (ML) techniques have reached a high level of performance in brain disease diagnosis and/or prognosis, but the decision process of a trained ML system is typically non-transparent. Using brain structural connectivity data, a fully automatic ensemble learning model, augmented with an interpretable model, is proposed for the estimation of MS patients' disability, measured by the Expanded Disability Status Scale (EDSS).

METHOD

An ensemble of four boosting-based models (GBM, XGBoost, CatBoost, LightBoost) organized following a stacking generalization scheme, was developed using DTI-based structural connectivity data. In addition, an interpretable model based on conditional logistic regression was developed to explain the best performances in terms of white matter (WM) links for three classes of EDSS (Low, Medium, High).

RESULTS

The ensemble model reached excellent level of performance (RMSE of 0.92 ± 0.28) compared to single-based models and provided a better EDSS estimation using DTI-based structural connectivity data compared to conventional MRI measures associated with patient data (age, gender and disease duration). Used for interpretation of the estimation process, the counterfactual method showed the importance of certain brain networks, corresponding mainly to left hemisphere WM links, connecting the left superior temporal with the left posterior cingulate and the right precuneus gray matter regions, and the inter-hemispheric WM links constituting the corpus callosum. Also, a better accuracy estimation was found for the high disability class.

CONCLUSION

The combination of advanced ML models and sensitive techniques such as DTI-based structural connectivity demonstrated to be useful for the estimation of MS patients' disability and to point out the most important brain WM networks involved in disability.

Prediction of Lower Grade Insular Glioma Molecular Pathology Using Diffusion Tensor Imaging Metric-Based Histogram Parameters

Objectives

To explore whether a simplified lesion delineation method and a set of diffusion tensor imaging (DTI) metric-based histogram parameters (mean, 25th percentile, 75th percentile, skewness, and kurtosis) are efficient at predicting the molecular pathology status (MGMT methylation, IDH mutation, TERT promoter mutation, and 1p19q codeletion) of lower grade insular gliomas (grades II and III).

Methods

40 lower grade insular glioma patients in two medical centers underwent preoperative DTI scanning. For each patient, the entire abnormal area in their b-non (b0) image was defined as region of interest (ROI), and a set of histogram parameters were calculated for two DTI metrics, fractional anisotropy (FA) and mean diffusivity (MD). Then, we compared how these DTI metrics varied according to molecular pathology and glioma grade, with their predictive performance individually and jointly assessed using receiver operating characteristic curves. The reliability of the combined prediction was evaluated by the calibration curve and Hosmer and Lemeshow test.

Results

The mean, 25th percentile, and 75th percentile of FA were associated with glioma grade, while the mean, 25th percentile, 75th percentile, and skewness of both FA and MD predicted IDH mutation. The mean, 25th percentile, and 75th percentile of FA, and all MD histogram parameters significantly distinguished TERT promoter status. Similarly, all MD histogram parameters were associated with 1p19q status. However, none of the parameters analyzed for either metric successfully predicted MGMT methylation. The 25th percentile of FA yielded the highest prediction efficiency for glioma grade, IDH mutation, and TERT promoter mutation, while the 75th percentile of MD gave the best prediction of 1p19q codeletion. The combined prediction could enhance the discrimination of grading, IDH and TERT mutation, and also with a good fitness.

Conclusions

Overall, more invasive gliomas showed higher FA and lower MD values. The simplified ROI delineation method presented here based on the combination of appropriate histogram parameters yielded a more practical and efficient approach to predicting molecular pathology in lower grade insular gliomas. This approach could help clinicians to determine the extent of tumor resection required and reduce complications, enabling more precise treatment of insular gliomas in combination with radiotherapy and chemotherapy.

Primary application of mean apparent propagator-MRI diffusion model in the grading of diffuse glioma

PURPOSE

To evaluate the diagnostic -->performance of mean apparent propagator-magnetic resonance imaging (MAP-MRI) in distinguishing the grades of diffuse gliomas.

METHOD

Thirty-six patients with pathologically confirmed diffuse gliomas were enrolled in this study. MAP-MRI parameters were measured in the parenchymal area of the tumour: non-Gaussianity (NG), non-Gaussianity axial (NGAx), non-Gaussianity vertical (NGRad), Q-space inverse variance (QIV), return to the origin probability (RTOP), return to the axis probability (RTAP), return to the plane probability (RTPP), and mean square displacement (MSD). Differences in the parameters between any two grades were compared, the characteristics of the parameters for different diffuse glioma grades were analysed, and receiver operating characteristic (ROC) curves were plotted to analyse the diagnostic value of each parameter.

RESULTS

Compared with grade III gliomas, grade II gliomas had lower NG, NGAx and NGRad values. NG, NGAx and NGRad had great area under the ROC curve (AUC) values (0.823, 0.835, and 0.838, P < 0.05). Compared with those of grade IV glioma, the NG, NGAx, NGRad, RTAP and RTOP values for grade II glioma were lower, the QIV values were higher (all P < 0.05). NG, NGAx, NGRad, RTAP, RTOP and QIV had great area under the ROC curve (AUC) values (0.923, 0.929, 0.923,0.793,0.822, and 0.769, P < 0.05).

CONCLUSIONS

Quantitative MAP-MRI parameters can distinguish grade II and III and grade II and IV gliomas before surgery but not grade III and IV gliomas. Thus, these parameters have clinical guiding value in the noninvasive preoperative evaluation of tumour pathological grading.

Perspectives on (A)symmetry of Arcuate Fasciculus. A Short Review About Anatomy, Tractography and TMS for Arcuate Fasciculus Reconstruction in Planning Surgery for Gliomas in Language Areas

Gliomas are brain tumors that are treated with surgical resection. Prognosis is influenced by the extent of resection and postoperative neurological status. As consequence, given the extreme interindividual and interhemispheric variability of subcortical white matter (WM) surgical planning requires to be patient's tailored. According to the “connectionist model,” there is a huge variability among both cortical areas and subcortical WM in all human beings, and it is known that brain is able to reorganize itself and to adapt to WM lesions. Brain magnetic resonance imaging diffusion tensor imaging (DTI) tractography allows visualization of WM bundles. Nowadays DTI tractography is widely available in the clinical setting for presurgical planning. Arcuate fasciculus (AF) is a long WM bundle that connects the Broca's and Wernicke's regions with a complex anatomical architecture and important role in language functions. Thus, its preservation is important for the postoperative outcome, and DTI tractography is usually performed for planning surgery within the language-dominant hemisphere. High variability among individuals and an asymmetrical pattern has been reported for this WM bundle. However, the functional relevance of AF in the contralateral non-dominant hemisphere in case of tumoral or surgical lesion of the language-dominant AF is unclear. This review focuses on AF anatomy with special attention to its asymmetry in both normal and pathological conditions and how it may be explored with preoperative tools for planning surgery on gliomas in language areas. Based on the findings available in literature, we finally speculate about the potential role of preoperative evaluation of the WM contralateral to the surgical site.

Functional connectivity of the default mode, dorsal attention and fronto-parietal executive control networks in glial tumor patients

PURPOSE

Resting state functional magnetic resonance imaging (rsfMRI) is an emerging tool to explore the functional connectivity of different brain regions. We aimed to assess the disruption of functional connectivity of the Default Mode Network (DMN), Dorsal Attention Network(DAN) and Fronto-Parietal Network (FPN) in patients with glial tumors.

METHODS

rsfMRI data acquired on 3T-MR of treatment-naive glioma patients prospectively recruited (2015-2019) and matched controls from the 1000 functional-connectomes-project were analyzed using the CONN functional toolbox. Seed-Based Connectivity Analysis (SBCA) and Independent Component Analysis (ICA, with 10 to 100 components) were performed to study reliably the three networks of interest.

RESULTS

35 patients with gliomas (17 WHO grade I-II, 18 grade III-IV) and 70 controls were included. Global increased DMN connectivity was consistently found with SBCA and ICA in patients compared to controls (Cluster1: Precuneus, height: p < 10^-6; Cluster2: subcallosum; height: p < 10^-5). However, an area of decreased connectivity was found in the posterior corpus callosum, particularly in high-grade gliomas (height: p < 10^-5). The DAN demonstrated small areas of increased connectivity in frontal and occipital regions (height: p < 10^-6). For the FPN, increased connectivity was noted in the precuneus, posterior cingulate gyrus, and frontal cortex. No difference in the connectivity of the networks of interest was demonstrated between low- and high-grade gliomas, as well as when stratified by their IDH1-R132H (isocitrate dehydrogenase) mutation status.

CONCLUSION

Altered functional connectivity is reliably found with SBCA and ICA in the DMN, DAN, and FPN in glioma patients, possibly explained by decreased connectivity between the cerebral hemispheres across the corpus callosum due to disruption of the connections.

Impaired neurocognitive function in glioma patients: from pathophysiology to novel intervention strategies

PURPOSE OF REVIEW

This review succinctly summarizes the recent literature regarding etiological contributors to impaired neurocognitive function (NCF) in adult patients with glioma. A brief overview of intervention and prevention strategies is also provided.

RECENT FINDINGS

A majority of patients with glioma exhibit NCF deficits, most frequently in memory and executive functioning. Impairments are often disabling and associated with reduced quality of life and survival. Cause is multifactorial and includes the tumour itself, treatments received and associated comorbidities. Although modern techniques such as brain mapping, dosing modifications and prophylactic medication aim to improve the NCF outcomes following neurosurgical resection and radiation therapy, a sizeable proportion of patients continue to evidence treatment-related NCF declines related to adverse effects to both local and distributed cerebral networks. Numerous patient and tumour characteristics, including genetic markers and sociodemographic factors, influence the pattern and severity of NCF impairment. Some rehabilitative and pharmacologic approaches show promise in mitigating NCF impairment in this population, though benefits are somewhat modest and larger scale intervention studies are needed.

SUMMARY

Research regarding NCF in patients with glioma has dramatically proliferated, providing insights into the mechanisms underlying impaired NCF and pointing to potential interventions, though further work is needed.

Isocitrate Dehydrogenase, Patient-Reported Outcomes, and Cognitive Functioning of Glioma Patients: a Systematic Review

PURPOSE OF REVIEW

Isocitrate dehydrogenase (IDH) mutation status has important prognostic implications in glioma patients, with IDH wild-type (IDH-WT) gliomas being associated with worse prognosis and shorter survival when compared with IDH mutant (IDH-mut) gliomas. Optimization of quality of life is a priority in the management of glioma patients. The goal of this systematic review was to identify studies that explored the association of IDH mutation status with patient-reported outcomes (PROs) and cognitive functioning of glioma patients.

RECENT FINDINGS

Studies that evaluated the association of IDH mutation status with PROs and/or cognitive functioning of glioma patients were identified from the Pubmed/MEDLINE, Clarivate analytics, and Google Scholar databases. Eight studies (7 journal articles and 2 conference abstracts) with a total of 658 low-grade glioma and high-grade glioma patients investigated the association of cognitive functioning and/or QoL with IDH status. IDH-WT status was associated with greater cognitive impairment relative to IDH-Mut status in three studies, while one study did not find the association between IDH status and perioperative cognitive functioning. One study reported worse postoperative cognitive functioning patients with IDH-WT vs. IDH-mut gliomas. In one study, IDH-WT status was linked to greater impairment on physical and communication functioning after surgery. IDH-WT gliomas are associated with greater cognitive burden than IDH-Mut tumors. The association of IDH status with QoL remains less clear. Assessment of IDH status should be considered when evaluating QoL and cognitive complaints of glioma patients. Further studies linking glioma molecular phenotypes with PROs and cognitive functioning are encouraged.

Asymmetric tumor-related alterations of network-specific intrinsic functional connectivity in glioma patients

Resting-state functional MRI (rs-fMRI) allows mapping temporally coherent brain networks, and intra- and inter-network alterations have been described in different diseases. This prospective study investigated hemispheric resting-state functional connectivity (RSFC) differences in the default-mode network (DMN) and fronto-parietal network (FPN) between patients with left- and right-hemispheric gliomas (LH PAT, RH PAT), addressing asymmetry effects the tumor might have on network-specific intrinsic functional connectivity under consideration of the prognostically relevant isocitrate-dehydrogenase (IDH) mutation status. Twenty-seven patients (16 LH PAT, 12 IDH-wildtype) and 27 healthy controls underwent anatomical and rs-fMRI as well as neuropsychological assessment. Independent component analyses were performed to identify the DMN and FPN. Hemispheric DMN- and FPN-RSFC were computed, compared across groups, and correlated with cognitive performance. Patient groups did not differ in tumor volume, grade or location. RH PAT showed higher contra-tumoral DMN-RSFC than controls and LH PAT. With regard to the FPN, contra-tumoral RSFC was increased in both patient groups as compared to controls. Higher contra-tumoral RSFC was associated with worse cognitive performance in patients, which, however, seemed to apply mainly to IDH-wildtype patients. The benefit of RSFC alterations for cognitive performance varied depending on the affected hemisphere, cognitive demand, and seemed to be altered by IDH-mutation status. At the time of study initiation, a clinical trial registration was not mandatory at our faculty, but it can be applied for if requested.

Contrahemispheric Cortex Predicts Survival and Molecular Markers in Patients With Unilateral High-Grade Gliomas

Background: Malignant high-grade gliomas are characterized by infiltration and destruction of surrounding brain tissue. Alterations in the contrahemispheric brain structure and their roles that may offer prognostically valuable information have not been investigated in high-grade gliomas. Methods: In total, 153 patients with unilateral glioma (low-grade, n = 77; high-grade, n = 76) and 115 healthy controls (HCs) were recruited and scanned with 3-D T1 imaging. The gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) volume in the contrahemisphere were examined. Partial correlation, logistic regression, and multivariate Cox's regression analyses were performed. Results: The contrahemispheric GM volume (CHGMV) in the high-grade glioma patients was significantly decreased compared to that in the HCs/low-grade gliomas (one-way ANOVA, Bonferroni corrected, p < 0.05). The CHGMV is significantly correlated with the WHO grade (r = -0.22, p < 0.05) and contrast-enhanced volume (r = -0.33, p < 0.01). In the high-grade gliomas, the binary logistic regression revealed that the CHGMV can independently predict isocitrate dehydrogenase 1 (IDH1) and P53 mutations. The survival curves revealed that the patients with a low CHGMV had a shorter overall survival (OS) than the patients with a high CHGMV (p = 0.001). The multivariate Cox's regression analysis showed that a low CHGMV can independently predict unfavorable OS with a hazard ratio of 2.883 (p = 0.035). Conclusions: Volume of the contrahemispheric cortex can be potentially used in clinical practice as an imaging biomarker to predict survival and molecular markers in patients with unilateral high-grade gliomas.

Structural plasticity of the bilateral hippocampus in glioma patients

This study investigates the structural plasticity and neuronal reaction of the hippocampus in glioma patient pre-surgery. Ninety-nine glioma patients without bilateral hippocampus involvement (low-grade, n=52; high-grade, n=47) and 80 healthy controls with 3D T1 images and resting-fMRI were included. Hippocampal volume and dynamic amplitude of low-frequency fluctuation (dALFF) were analyzed among groups. Relationships between hippocampal volume and clinical characteristics were assessed. We observed remote hippocampal volume increases in low- and high-grade glioma and a greater response of the ipsilateral hippocampus than the contralesional hippocampus. The bilateral hippocampal dALFF was significantly increased in high-grade glioma. Tumor-associated epilepsy and the IDH-1 mutation did not affect hippocampal volume in glioma patients. No significant relationship between hippocampal volume and age was observed in high-grade glioma. The Kaplan-Meier curve and log-rank test revealed that large hippocampal volume was associated with shorter overall survival (OS) compared with small hippocampal volume (p=0.007). Multivariate Cox regression analysis revealed that large hippocampal volume was an independent predictor of unfavorable OS (HR=3.597, 95% CI: 1.160-11.153, p=0.027) in high-grade glioma. Our findings suggest that the hippocampus has a remarkable degree of plasticity in response to pathological stimulation of glioma and that the hippocampal reaction to glioma may be related to tumor malignancy.