A Probabilistic Atlas of Diffuse WHO Grade II Glioma Locations in the Brain

Impaired creative cognition after surgery for an IDH-mutated glioma: A proof-of-concept study

Assessment of high cognitive functions, such as creativity, is often overlooked in medical practice. However, it is crucial to understand the impact of brain tumors, specifically low-grade gliomas, on creative cognition, as these tumors predominantly affect brain regions associated with cognitive creativity. In this study, we investigated creative cognition using the Alternative Uses Task (AUT) and the Combination of Associates Task (CAT) in a cohort of 29 patients who underwent brain surgery for a low-grade glioma, along with 27 control participants. While the group of patients did not exhibit deficits in clinical neuropsychological assessments, our results revealed significant impairment in generating original and creative ideas compared to the control group. Furthermore, when analyzing the specific brain regions affected by the tumors, patients with lesions overlapping the left rostro-lateral prefrontal cortex, a critical region for creativity, displayed more pronounced impairments in the CAT compared to patients with lesions outside this region. These findings provide proof of concept that patients can experience impaired creative cognition following surgery for low-grade glioma, highlighting the importance of assessing higher-order cognitive functions, including creativity, in neurosurgical patients. Moreover, beyond its clinical relevance, our study contributes to advancing our understanding of the neuroscience of creativity.

Tumor location and neurocognitive function-Unravelling the association and identifying relevant anatomical substrates in intra-axial brain tumors

Background

Neurocognitive function is a key outcome indicator of therapy in brain tumors. Understanding the underlying anatomical substrates involved in domain function and the pathophysiological basis of dysfunction can help ameliorate the effects of therapy and tailor directed rehabilitative strategies.

Methods

Hundred adult diffuse gliomas were co-registered onto a common demographic-specific brain template to create tumor localization maps. Voxel-based lesion symptom (VLSM) technique was used to assign an association between individual voxels and neuropsychological dysfunction in various domains (attention and executive function (A & EF), language, memory, visuospatial/constructive abilities, and visuomotor speed). The probability maps thus generated were further co-registered to cortical and subcortical atlases. A permutation-based statistical testing method was used to evaluate the statistically and clinically significant anatomical parcels associated with domain dysfunction and to create heat maps.

Results

Neurocognition was affected in a high proportion of subjects (93%), with A & EF and memory being the most affected domains. Left-sided networks were implicated in patients with A & EF, memory, and language deficits with the perisylvian white matter tracts being the most common across domains. Visuospatial dysfunction was associated with lesions involving the right perisylvian cortical regions, whereas deficits in visuomotor speed were associated with lesions involving primary visual and motor output pathways.

Conclusions

Significant baseline neurocognitive deficits are prevalent in gliomas. These are multidomain and the perisylvian network especially on the left side seems to be very important, being implicated in dysfunction of many domains.

Preoperative plasticity in the functional naming network of patients with left insular gliomas

Plasticity could take place as a compensatory process following brain glioma growth. Only a few studies specifically explored plasticity in patients affected by a glioma invading the left insula; even more, plasticity of the insular cortex in task-based functional language network is almost unexplored. In the current study, we explored potential plasticity in a consecutive series of 22 patients affected by a glioma centered to the left insula, by comparing their preoperative object-naming functional network with that of a group of healthy controls. After having controlled for demographic variables, fMRI results showed that patients vs. controls activated a cluster in the right, contralesional pars triangularis including the Broca's area. On the other hand, controls did not significantly activate any brain region more than patients. At behavioral level, patients retained a generally preserved naming performance as well as a proficient language processing profile. These findings suggest that involvement of language-specific areas in the healthy hemisphere could help compensate for the left, affected insula, thus allowing preservation of the naming functions. Results are commented in relation to lesion site, naming performance, and potential relevance for neurosurgery.

Spatial mapping of tumor heterogeneity in whole-body PET-CT: a feasibility study

BACKGROUND

Tumor heterogeneity is recognized as a predictor of treatment response and patient outcome. Quantification of tumor heterogeneity across all scales may therefore provide critical insight that ultimately improves cancer management.

METHODS

An image registration-based framework for the study of tumor heterogeneity in whole-body images was evaluated on a dataset of 490 FDG-PET-CT images of lung cancer, lymphoma, and melanoma patients. Voxel-, lesion- and subject-level features were extracted from the subjects' segmented lesion masks and mapped to female and male template spaces for voxel-wise analysis. Resulting lesion feature maps of the three subsets of cancer patients were studied visually and quantitatively. Lesion volumes and lesion distances in subject spaces were compared with resulting properties in template space. The strength of the association between subject and template space for these properties was evaluated with Pearson's correlation coefficient.

RESULTS

Spatial heterogeneity in terms of lesion frequency distribution in the body, metabolic activity, and lesion volume was seen between the three subsets of cancer patients. Lesion feature maps showed anatomical locations with low versus high mean feature value among lesions sampled in space and also highlighted sites with high variation between lesions in each cancer subset. Spatial properties of the lesion masks in subject space correlated strongly with the same properties measured in template space (lesion volume, R = 0.986, p < 0.001; total metabolic volume, R = 0.988, p < 0.001; maximum within-patient lesion distance, R = 0.997, p < 0.001). Lesion volume and total metabolic volume increased on average from subject to template space (lesion volume, 3.1 ± 52 ml; total metabolic volume, 53.9 ± 229 ml). Pair-wise lesion distance decreased on average by 0.1 ± 1.6 cm and maximum within-patient lesion distance increased on average by 0.5 ± 2.1 cm from subject to template space.

CONCLUSIONS

Spatial tumor heterogeneity between subsets of interest in cancer cohorts can successfully be explored in whole-body PET-CT images within the proposed framework. Whole-body studies are, however, especially prone to suffer from regional variation in lesion frequency, and thus statistical power, due to the non-uniform distribution of lesions across a large field of view.

Graph fusion prediction of autism based on attentional mechanisms

Autism spectrum disorder (ASD) is a pervasive developmental disorder, and the earlier detection and timely intervention for treatment positively affect the prognosis of patients. Deep learning algorithms based on graph structure have achieved good results in autism prediction in recent years. However, there are problems with standardized operations in extracting features and combining neighborhood node features with the structure of the graph dependent, which limits the generalization ability of the trained model to other graph structures. In this paper, we propose a graph fusion autism prediction model based on attentional mechanisms(AGF) to address the above problems. The AGF model represents the overall population (patients or healthy controls) as a sparse graph, where nodes are subjects, and non-imaging features are integrated as edge weights. Different weights can be defined for different nodes in the neighborhood through the attention mechanism without relying on prior knowledge of the graph structure. The model is also able to dynamically fuse multiple sparse graphs obtained from different non-imaging features by way of training weight assignment. Its performance is also compared with several other models (e.g., S-AGF, GCN, etc.), and the results show that it has superior prediction accuracy compared to the baseline model. The results show that this improvement of graph fusion works better on the ABIDE databases, and the classification accuracy can reach 73.9%. The datasets and source code are freely available at https://github.com/chengyu-github1012/Graph-Fusion.git. Strengths and limitations of this study: graph fusion; disease prediction; noise.

IDH1/2 wildtype gliomas grade 2 and 3 with molecular glioblastoma-like profile have a distinct course of epilepsy compared to IDH1/2 wildtype glioblastomas

BACKGROUND

IDH1/2 wildtype (IDHwt) glioma WHO grade 2 and 3 patients with pTERT mutation and/or EGFR amplification and/or + 7/-10 chromosome gain/loss have a similar overall survival time as IDHwt glioblastoma patients, and are both considered glioblastoma IDHwt according to the WHO 2021 classification. However, differences in seizure onset have been observed. This study aimed to compare the course of epilepsy in the 2 glioblastoma subtypes.

METHODS

We analyzed epilepsy data of an existing cohort including IDHwt histologically lower-grade glioma WHO grade 2 and 3 with molecular glioblastoma-like profile (IDHwt hLGG) and IDHwt glioblastoma patients. Primary outcome was the incidence proportion of epilepsy during the disease course. Secondary outcomes included, among others, onset of epilepsy, number of seizure days, and antiepileptic drug (AED) polytherapy.

RESULTS

Out of 254 patients, 78% (50/64) IDHwt hLGG and 68% (129/190) IDHwt glioblastoma patients developed epilepsy during the disease (P = .121). Epilepsy onset before histopathological diagnosis occurred more frequently in IDHwt hLGG compared to IDHwt glioblastoma patients (90% vs 60%, P < .001), with a significantly longer median time to diagnosis (3.5 vs 1.3 months, P < .001). Median total seizure days was also longer for IDHwt hLGG patients (7.0 vs 3.0, P = .005), and they received more often AED polytherapy (32% vs 17%, P = .028).

CONCLUSIONS

Although the incidence proportion of epilepsy during the entire disease course is similar, IDHwt hLGG patients show a significantly higher incidence of epilepsy before diagnosis and a significantly longer median time between first seizure and diagnosis compared to IDHwt glioblastoma patients, indicating a distinct clinical course.

Presurgical Executive Functioning in Low-Grade Glioma Patients Cannot Be Topographically Mapped

Executive dysfunctions have a high prevalence in low-grade glioma patients and may be the result of structural disconnections of particular subcortical tracts and/or networks. However, little research has focused on preoperative low-grade glioma patients. The frontotemporoparietal network has been closely linked to executive functions and is substantiated by the superior longitudinal fasciculus. The aim of this study was to investigate their role in executive functions in low-grade glioma patients. Patients from two neurological centers were included with IDH-mutated low-grade gliomas. The sets of preoperative predictors were (i) distance between the tumor and superior longitudinal fasciculus, (ii) structural integrity of the superior longitudinal fasciculus, (iii) overlap between tumor and cortical networks, and (iv) white matter disconnection of the same networks. Linear regression and random forest analyses were performed. The group of 156 patients demonstrated significantly lower performance than normative samples and had a higher prevalence of executive impairments. However, both regression and random forest analyses did not demonstrate significant results, meaning that neither structural, cortical network overlap, nor network disconnection predictors explained executive performance. Overall, our null results indicate that there is no straightforward topographical explanation of executive performance in low-grade glioma patients. We extensively discuss possible explanations, including plasticity-induced network-level equipotentiality. Finally, we stress the need for the development of novel methods to unveil the complex and interacting mechanisms that cause executive deficits in low-grade glioma patients.

Network-level prediction of set-shifting deterioration after lower-grade glioma resection

OBJECTIVE

The aim of this study was to predict set-shifting deterioration after resection of low-grade glioma.

METHODS

The authors retrospectively analyzed a bicentric series of 102 patients who underwent surgery for low-grade glioma. The difference between the completion times of the Trail Making Test parts B and A (TMT B-A) was evaluated preoperatively and 3-4 months after surgery. High dimensionality of the information related to the surgical cavity topography was reduced to a small set of predictors in four different ways: 1) overlap between surgical cavity and each of the 122 cortical parcels composing Yeo's 17-network parcellation of the brain; 2) Tractotron: disconnection by the cavity of the major white matter bundles; 3) overlap between the surgical cavity and each of Yeo's networks; and 4) disconets: signature of structural disconnection by the cavity of each of Yeo's networks. A random forest algorithm was implemented to predict the postoperative change in the TMT B-A z-score.

RESULTS

The last two network-based approaches yielded significant accuracies in left-out subjects (area under the receiver operating characteristic curve [AUC] approximately equal to 0.8, p approximately equal to 0.001) and outperformed the two alternatives. In single tree hierarchical models, the degree of damage to Yeo corticocortical network 12 (CC 12) was a critical node: patients with damage to CC 12 higher than 7.5% (cortical overlap) or 7.2% (disconets) had much higher risk to deteriorate, establishing for the first time a causal link between damage to this network and impaired set-shifting.

CONCLUSIONS

The authors' results give strong support to the idea that network-level approaches are a powerful way to address the lesion-symptom mapping problem, enabling machine learning-powered individual outcome predictions.

Imaging fetal anatomy

Due to advancements in ultrasound techniques, the focus of antenatal ultrasound screening is moving towards the first trimester of pregnancy. The early first trimester however remains in part, a 'black box', due to the size of the developing embryo and the limitations of contemporary scanning techniques. Therefore there is a need for images of early anatomical developmental to improve our understanding of this area. By using new imaging techniques, we can not only obtain better images to further our knowledge of early embryonic development, but clear images of embryonic and fetal development can also be used in training for e.g. sonographers and fetal surgeons, or to educate parents expecting a child with a fetal anomaly. The aim of this review is to provide an overview of the past, present and future techniques used to capture images of the developing human embryo and fetus and provide the reader newest insights in upcoming and promising imaging techniques. The reader is taken from the earliest drawings of da Vinci, along the advancements in the fields of in utero ultrasound and MR imaging techniques towards high-resolution ex utero imaging using Micro-CT and ultra-high field MRI. Finally, a future perspective is given about the use of artificial intelligence in ultrasound and new potential imaging techniques such as synchrotron radiation-based CT to increase our knowledge regarding human development.

The Distribution of Major Brain Metabolites in Normal Adults: Short Echo Time Whole-Brain MR Spectroscopic Imaging Findings

This prospective study aimed to evaluate the variation in magnetic resonance spectroscopic imaging (MRSI)-observed brain metabolite concentrations according to anatomical location, sex, and age, and the relationships among regional metabolite distributions, using short echo time (TE) whole-brain MRSI (WB-MRSI). Thirty-eight healthy participants underwent short TE WB-MRSI. The major metabolite ratios, i.e., N-acetyl aspartate (NAA)/creatine (Cr), choline (Cho)/Cr, glutamate + glutamine (Glx)/Cr, and myoinositol (mI)/Cr, were calculated voxel-by-voxel. Their variations according to anatomical regions, sex, and age, and their relationship to each other were evaluated by using repeated-measures analysis of variance, t-tests, and Pearson’s product-moment correlation analyses. All four metabolite ratios exhibited widespread regional variation across the cerebral hemispheres (corrected p < 0.05). Laterality between the two sides and sex-related variation were also shown (p < 0.05). In several regions, NAA/Cr and Glx/Cr decreased and mI/Cr increased with age (corrected p < 0.05). There was a moderate positive correlation between NAA/Cr and mI/Cr in the insular lobe and thalamus and between Glx/Cr and mI/Cr in the parietal lobe (r ≥ 0.348, corrected p ≤ 0.025). These observations demand age- and sex- specific regional reference values in interpreting these metabolites, and they may facilitate the understanding of glial-neuronal interactions in maintaining homeostasis.

White Matter Tracts and Diffuse Lower-Grade Gliomas: The Pivotal Role of Myelin Plasticity in the Tumor Pathogenesis, Infiltration Patterns, Functional Consequences and Therapeutic Management

For many decades, interactions between diffuse lower-grade glioma (LGG) and brain connectome were neglected. However, the neoplasm progression is intimately linked to its environment, especially the white matter (WM) tracts and their myelin status. First, while the etiopathogenesis of LGG is unclear, this tumor seems to appear during the adolescence, and it is mostly located within anterior and associative cerebral areas. Because these structures correspond to those which were myelinated later in the brain maturation process, WM myelination could play a role in the development of LGG. Second, WM fibers and the myelin characteristics also participate in LGG diffusion, since glioma cells migrate along the subcortical pathways, especially when exhibiting a demyelinated phenotype, which may result in a large invasion of the parenchyma. Third, such a migratory pattern can induce functional (neurological, cognitive and behavioral) disturbances, because myelinated WM tracts represent the main limitation of neuroplastic potential. These parameters are critical for tailoring an individualized therapeutic strategy, both (i) regarding the timing of active treatment(s) which must be proposed earlier, before a too wide glioma infiltration along the WM bundles, (ii) and regarding the anatomic extent of surgical resection and irradiation, which should take account of the subcortical connectivity. Therefore, the new science of connectomics must be integrated in LGG management, based upon an improved understanding of the interplay across glioma dissemination within WM and reactional neural networks reconfiguration, in order to optimize long-term oncological and functional outcomes. To this end, mechanisms of activity-dependent myelin plasticity should be better investigated.

The immunology of low-grade gliomas

Low-grade gliomas (LGGs), which harbor an isocitrate dehydrogenase (IDH) mutation, have a better prognosis than their high-grade counterparts; nonetheless, they remain incurable and impart significant negative impacts on patients' quality of life. Although immunotherapies represent a novel avenue of treatment for patients with LGGs, they have not yet been successful. Accurately selecting and evaluating immunotherapies requires a detailed understanding of LGG tumor immunology and the underlying tumor immune phenotype. A growing body of literature suggests that LGGs significantly differ in their immunology from high-grade gliomas, highlighting the importance of investigation into LGG immunology specifically. In this review, the authors aimed to discuss relevant research surrounding the LGG tumor immune microenvironment, including immune cell infiltration, tumor immunogenicity, checkpoint molecule expression, the impact of an IDH mutation, and implications for immunotherapies, while also briefly touching on current immunotherapy trials and future directions for LGG immunology research.

Pseudo-insular glioma syndrome: illustrative cases

BACKGROUND

Lower-grade insular gliomas often appear as expansile and infiltrative masses on magnetic resonance imaging (MRI). However, there are nonneoplastic lesions of the insula, such as demyelinating disease and vasculopathies, that can mimic insular gliomas.

OBSERVATIONS

The authors report two patients who presented with headaches and were found to have mass lesions concerning for lower-grade insular glioma based on MRI obtained at initial presentation. However, on the immediate preoperative MRI obtained a few weeks later, both patients had spontaneous and complete resolution of the insular lesions.

LESSONS

Tumor mimics should always be in the differential diagnosis of brain masses, including those involving the insula. The immediate preoperative MRI (within 24–48 hours of surgery) must be compared carefully with the initial presentation MRI to assess interval change that suggests tumor mimics to avoid unnecessary surgical intervention.

Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions

Background

Glioma is the most common intra-axial tumor, and its location relative to critical areas of the brain is important for treatment decision-making. Studies often report tumor location based on anatomical taxonomy alone since the estimation of eloquent regions requires considerable knowledge of functional neuroanatomy and is, to some degree, a subjective measure. An unbiased and reproducible method to determine tumor location and eloquence is desirable, both for clinical use and for research purposes.

Objective

To report on a voxel-based method for assessing anatomical distribution and proximity to eloquent regions in diffuse lower-grade gliomas (World Health Organization grades 2 and 3).

Methods

A multi-institutional population-based dataset of adult patients (≥18 years) histologically diagnosed with lower-grade glioma was analyzed. Tumor segmentations were registered to a standardized space where two anatomical atlases were used to perform a voxel-based comparison of the proximity of segmentations to brain regions of traditional clinical interest.

Results

Exploring the differences between patients with oligodendrogliomas, isocitrate dehydrogenase (IDH) mutated astrocytomas, and patients with IDH wild-type astrocytomas, we found that the latter were older, more often had lower Karnofsky performance status, and that these tumors were more often found in the proximity of eloquent regions. Eloquent regions are found slightly more frequently in the proximity of IDH-mutated astrocytomas compared to oligodendrogliomas. The regions included in our voxel-based definition of eloquence showed a high degree of association with performing biopsy compared to resection.

Conclusion

We present a simple, robust, unbiased, and clinically relevant method for assessing tumor location and eloquence in lower-grade gliomas.

Impaired Set-Shifting from Dorsal Stream Disconnection: Insights from a European Series of Right Parietal Lower-Grade Glioma Resection

Simple Summary

Awake surgery with cognitive monitoring has increasingly been implemented to preserve brain networks and functionality. More recently, not only surgery in the left but also in the right hemisphere, i.c., the parietal lobe, was associated with potential risk for deficits in cognitive functions, such as cognitive flexibility. We describe an explorative pilot study in an international consortium within clinical care as usual. Careful interpretation of our findings indicates that disconnection of the lateral part of the dorsal stream correlated with impaired set-shifting. More importantly, it illustrates the need for international collaboration on neuropsychological tests and methodologies to improve our understanding of white matter networks at risk during awake surgery.

Abstract

Awake surgery with cognitive monitoring has increasingly been implemented to preserve brain networks and functionality. More recently, not only surgery in the left but also in the right hemisphere, i.c., the parietal lobe, was associated with potential risk for deficits in cognitive functions, such as cognitive flexibility. In this explorative pilot study, we compare cognitive performance more than three months after surgery with baseline measurements and explore the association between cognitive decline and subcortical tracts that may have been severed during surgery in the right hemisphere. Twenty-two patients who underwent surgery for a right parietal low-grade glioma were assessed pre- and postoperatively using the Trail Making Test and the Stroop task to administer set-shifting abilities and inhibition. Volume measurements and lesion–symptom mapping analyses were performed on postoperative MRI scans. Careful interpretation of the results shows a change in TMT performance and not on the Stroop Task when the lateral part of the arcuate fasciculus is damaged, indicating that disconnection of the lateral part of the dorsal stream might be correlated specifically with impaired set-shifting and not with inhibition. More importantly, this study underlines the need for international concertation to allow larger studies to increase power and perform more detailed analyses.

The link between gliomas infiltration and white matter architecture investigated with electron microscopy and diffusion tensor imaging

Diffuse low-grade gliomas (DLGG) display different preferential locations in eloquent and secondary associative brain areas. The reason for this tendency is still unknown. We hypothesized that the intrinsic architecture and water diffusion properties of the white matter bundles in these regions may facilitate gliomas infiltration. Magnetic resonance imaging of sixty-seven diffuse low-grade gliomas patients were normalized to/and segmented in MNI space to create three probabilistic infiltration weighted gradient maps according to the molecular status of each tumor group (IDH mutated, IDH wild-type and IDH mutated/1p19q co-deleted). Diffusion tensor imaging (DTI)- based parameters were derived for five major white matter bundles, displaying regional differences in the grade of infiltration, averaged over 20 healthy individuals acquired from the Human connectome project (HCP) database. Transmission electron microscopy (TEM) was used to analyze fiber density, fiber diameter and g-ratio in 100 human white matter regions, sampled from cadaver specimens, reflecting areas with different gliomas infiltration in each white matter bundle. Histological results and DTI-based parameters were compared in anatomical regions of high- and low grade of infiltration (HIF and LIF) respectively. We detected differences in the white matter infiltration of five major white matter bundles in three groups. Astrocytomas IDHm infiltrated left fronto-temporal subcortical areas. Astrocytomas IDHwt were detected in the posterior-temporal and temporo-parietal regions bilaterally. Oligodendrogliomas IDHm/1p19q infiltrated anterior subcortical regions of the frontal lobes bilaterally. Regional differences within the same white matter bundles were detected by both TEM- and DTI analysis linked to different topographical variables. Our multimodal analysis showed that HIF regions, common to all the groups, displayed a smaller fiber diameter, lower FA and higher RD compared with LIF regions. Our results suggest that the both morphological features and diffusion parameters of the white matter may be different in regions linked to the preferential location of DLGG.

Preferential tumor localization in relation to 18F-FDOPA uptake for lower-grade gliomas

PURPOSE

Although tumor localization and 3,4-dihydroxy-6-¹⁸F-fluoro-L-phenylalanine (FDOPA) uptake may have an association, preferential tumor localization in relation to FDOPA uptake is yet to be investigated in lower-grade gliomas (LGGs). This study aimed to identify differences in the frequency of tumor localization between FDOPA hypometabolic and hypermetabolic LGGs using a probabilistic radiographic atlas.

METHODS

Fifty-one patients with newly diagnosed LGG (WHO grade II, 29; III, 22; isocitrate dehydrogenase wild-type, 21; mutant 1p19q non-codeleted,16; mutant codeleted, 14) who underwent FDOPA positron emission tomography (PET) were retrospectively selected. Semiautomated tumor segmentation on FLAIR was performed. Patients with LGGs were separated into two groups (FDOPA hypometabolic and hypermetabolic LGGs) according to the normalized maximum standardized uptake value of FDOPA PET (a threshold of the uptake in the striatum) within the segmented regions. Spatial normalization procedures to build a 3D MRI-based atlas using each segmented region were validated by an analysis of differential involvement statistical mapping.

RESULTS

Superimposition of regions of interest showed a high number of hypometabolic LGGs localized in the frontal lobe, while a high number of hypermetabolic LGGs was localized in the insula, putamen, and temporal lobe. The statistical mapping revealed that hypometabolic LGGs occurred more frequently in the superior frontal gyrus (close to the supplementary motor area), while hypermetabolic LGGs occurred more frequently in the insula.

CONCLUSION

Radiographic atlases revealed preferential frontal lobe localization for FDOPA hypometabolic LGGs, which may be associated with relatively early detection.

Contralesional homotopic functional plasticity in patients with temporal glioma

OBJECTIVE

This study aimed to explore the contralesional homotopic functional plasticity in the brain of patients with unilateral temporal glioma.

METHODS

Demographic, neurocognitive, and resting-state functional MRI data were collected from 17 patients with temporal glioma (10 in the right lobe and 7 in the left lobe), along with 14 age- and sex-matched healthy controls. The amplitude of low-frequency fluctuation (ALFF) of the contralesional homotopic region and 2 control regions was examined. The region-of-interest-based analysis was used to determine the altered functional connectivity (FC) of the contralesional homotopic region, showing significantly different intrinsic regional brain activity between patients and controls. Partial correlation analysis was conducted to determine the association between the altered neural activity and behavioral characteristics.

RESULTS

Compared with controls, patients with right temporal glioma exhibited significantly increased ALFF in the contralesional homotopic hippocampus and parahippocampal region. In addition, the intrinsic regional activity in these regions was negatively correlated with the visuospatial score (r = -0.718, p = 0.045). Whole-brain FC analysis revealed significantly increased FC between the left hippocampus and parahippocampal regions and the left inferior temporal gyrus, and decreased FC between the left hippocampus and parahippocampal regions and the left inferior frontal gyrus. No significant changes were found in the 2 control regions.

CONCLUSIONS

Contralesional homotopic regions are instrumental in the process of neural plasticity and functional compensation observed in patients with unilateral temporal glioma. The observed findings might be used to help preoperative evaluation or rehabilitation of postsurgical patients.

Contralesional functional network reorganization of the insular cortex in diffuse low-grade glioma patients

Diffuse low-grade gliomas (DLGGs) growing on the insular lobe induce contralesional hemispheric insular lobe compensation of damaged functioning by increasing cortical volumes. However, it remains unclear how functional networks are altered in patients with insular lobe DLGGs during functional compensation. Thirty-five patients with insular DLGGs were classified into the left (insL, n = 16) and right groups (insR, n = 19), and 33 healthy subjects were included in the control group. Resting state functional magnetic resonance imaging was used to generate functional connectivity (FC), and network topological properties were evaluated using graph theoretical analysis based on FC matrices. Network-based statistics were applied to compare differences in the FC matrices. A false discovery rate was applied to correct the topological properties. There was no difference in the FC of edges between the control and insL groups; however, the nodal shortest path length of the right insular lobe was significantly increased in the insL group compared to the control group. Additionally, FC was increased in the functional edges originating from the left insular lobe in the insR group compared to the control group. Moreover, there were no differences in topological properties between the insR and control groups. The contralesional insular lobe is crucial for network alterations. The detailed patterns of network alterations were different depending on the affected hemisphere. The observed network alterations might be associated with functional network reorganization and functional compensation.

Fully Automatic Calibration of Tumor-Growth Models Using a Single mpMRI Scan

Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the original, localized site(s) of tumor initiation. Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

Identifying resting-state effective connectivity abnormalities in drug-naïve major depressive disorder diagnosis via graph convolutional networks

Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting-state functional magnetic resonance imaging (rs-fMRI) and estimate functional connectivity for brain-disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph-based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole-brain data-driven manner from rs-fMRI. To distinguish drug-naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD.

Differences in the preferential location and invasiveness of diffuse low-grade gliomas and their impact on outcome

Background

Low‐grade gliomas (LGGs) are primary diffuse slow‐growing brain tumors derived from glial cells. The management of these tumors is dependent on their location, which often harbors eloquent areas. We retrospectively recorded the location of diffuse gliomas to identify whether specific differences exist between the histological types.

Methods

We analyzed 102 patients with previous histological diagnosis of WHO‐II astrocytomas (62) and WHO‐II oligodendrogliomas (40) according to WHO‐2016 classification. MRI sequences (T2‐FLAIR) were used for tumor volume segmentation and to create a frequency map of their locations within the Montreal Neurological Institute (MNI) space. The Brain‐Grid (BG) system (standardized radiological tool of intersected lines according to anatomical landmarks) was created and merged with a tractography atlas for infiltration analysis.

Results

Astrocytomas frequently infiltrated association and projection white matter pathways within fronto‐temporo‐insular regions on the left side. Oligodendrogliomas infiltrated larger white matter networks (association‐commissural‐projection) of the frontal lobe bilaterally. A critical number of infiltrated BG voxels (7 for astrocytomas, 10 for oligodendrogliomas) significantly predicted shorter overall survival (OS) in both groups. Bilateral tumor extension in astrocytomas and preoperative tumor volume in oligodendrogliomas were independent prognostic factors for shorter OS.

Conclusions

Astrocytomas and oligodendrogliomas differ in preferential location, and this has an impact on the type and the extent of white matter involvement. The number of BG voxels infiltrated reflected different tumor invasiveness and its impact on OS in both groups. All this new information may be valuable in neurosurgical oncology to classify and plan treatment for patients with diffuse gliomas.

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Image registration and segmentation are the two most studied problems in medical image analysis. Deep learning algorithms have recently gained a lot of attention due to their success and state-of-the-art results in variety of problems and communities. In this paper, we propose a novel, efficient, and multi-task algorithm that addresses the problems of image registration and brain tumor segmentation jointly. Our method exploits the dependencies between these tasks through a natural coupling of their interdependencies during inference. In particular, the similarity constraints are relaxed within the tumor regions using an efficient and relatively simple formulation. We evaluated the performance of our formulation both quantitatively and qualitatively for registration and segmentation problems on two publicly available datasets (BraTS 2018 and OASIS 3), reporting competitive results with other recent state-of-the-art methods. Moreover, our proposed framework reports significant amelioration (p < 0.005) for the registration performance inside the tumor locations, providing a generic method that does not need any predefined conditions (e.g., absence of abnormalities) about the volumes to be registered. Our implementation is publicly available online at https://github.com/TheoEst/joint_registration_tumor_segmentation.

Is the anatomical distribution of low-grade gliomas linked to regions of gliogenesis?

INTRODUCTION

According to the stem cell theory, two neurogenic niches in the adult human brain may harbor cells that initiate the formation of gliomas: The larger subventricular zone (SVZ) and the subgranular zone (SGZ) in the hippocampus. We wanted to explore whether defining molecular markers in low-grade gliomas (LGG; WHO grade II) are related to distance to the neurogenic niches.

METHODS

Patients treated at two Norwegian university hospitals with population-based referral were included. Eligible patients had histopathological verified supratentorial low-grade glioma. IDH mutational status and 1p19q co-deletion status was retrospectively assessed. 159 patients were included, and semi-automatic tumor segmentation was done from pre-treatment T2-weighted (T2W) or Fluid-Attenuated Inversion Recovery (FLAIR) images. 3D maps showing the anatomical distribution of the tumors were then created for each of the three molecular subtypes (IDH mutated/1p19q co-deleted, IDH mutated and IDH wild-type). Both distance from tumor center and tumor border to the neurogenic niches were recorded.

RESULTS

In this population-based cohort of previously untreated low-grade gliomas, we found that low-grade gliomas are more often found closer to the SVZ than the SGZ, but IDH wild-type tumors are more often found near SGZ.

CONCLUSION

Our study suggests that the stem cell origin of IDH wild-type and IDH mutated low-grade gliomas may be different.

MRI Atlas of IDH Wild-Type Supratentorial Glioblastoma: Probabilistic Maps of Phenotype, Management, and Outcomes

Background Tumor location is a main prognostic parameter in patients with glioblastoma. Probabilistic MRI-based brain atlases specifying the probability of tumor location associated with important demographic, clinical, histomolecular, and management data are lacking for isocitrate dehydrogenase (IDH) wild-type glioblastomas. Purpose To correlate glioblastoma location with clinical phenotype, surgical management, and outcomes by using a probabilistic analysis in a three-dimensional (3D) MRI-based atlas. Materials and Methods This retrospective study included all adults surgically treated for newly diagnosed IDH wild-type supratentorial glioblastoma in a tertiary adult surgical neuro-oncology center (2006-2016). Semiautomated tumor segmentation and spatial normalization procedures to build a 3D MRI-based atlas were validated. The authors performed probabilistic analyses by using voxel-based lesion symptom mapping technology. The Liebermeister test was used for binary data, and the generalized linear model was used for continuous data. Results A total of 392 patients (mean age, 61 years ± 13; 233 men) were evaluated. The authors identified the preferential location of glioblastomas according to subventricular zone, age, sex, clinical presentation, revised Radiation Therapy Oncology Group-Recursive Partitioning Analysis class, Karnofsky performance status, O⁶-methylguanine DNA methyltransferase promoter methylation status, surgical management, and survival. The superficial location distant from the eloquent area was more likely associated with a preserved functional status at diagnosis (348 of 392 patients [89%], P < .05), a large surgical resection (173 of 392 patients [44%], P < .05), and prolonged overall survival (163 of 334 patients [49%], P < .05). In contrast, deep location and location within eloquent brain areas were more likely associated with an impaired functional status at diagnosis (44 of 392 patients [11%], P < .05), a neurologic deficit (282 of 392 patients [72%], P < .05), treatment with biopsy only (183 of 392 patients [47%], P < .05), and shortened overall survival (171 of 334 patients [51%], P < .05). Conclusion The authors identified the preferential location of isocitrate dehydrogenase wild-type glioblastomas according to parameters of interest and provided an image-based integration of multimodal information impacting survival results. This suggests the role of glioblastoma location as a surrogate and multimodal parameter integrating several known prognostic factors. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Huang in this issue.

Correspondence between cerebral glucose metabolism and BOLD reveals relative power and cost in human brain

The correspondence between cerebral glucose metabolism (indexing energy utilization) and synchronous fluctuations in blood oxygenation (indexing neuronal activity) is relevant for neuronal specialization and is affected by brain disorders. Here, we define novel measures of relative power (rPWR, extent of concurrent energy utilization and activity) and relative cost (rCST, extent that energy utilization exceeds activity), derived from FDG-PET and fMRI. We show that resting-state networks have distinct energetic signatures and that brain could be classified into major bilateral segments based on rPWR and rCST. While medial-visual and default-mode networks have the highest rPWR, frontoparietal networks have the highest rCST. rPWR and rCST estimates are generalizable to other indexes of energy supply and neuronal activity, and are sensitive to neurocognitive effects of acute and chronic alcohol exposure. rPWR and rCST are informative metrics for characterizing brain pathology and alternative energy use, and may provide new multimodal biomarkers of neuropsychiatric disorders.

The brain primarily uses glucose to generate energy, but the relationship of neuronal activity to glucose utilization is not necessarily a simple linear one. Here, the authors introduce relative power (rPWR) and relative cost (rCST) as new metrics to quantify how brain activity relates to glucose consumption.

A novel radiological classification system for cerebral gliomas: The Brain-Grid

Purpose

Standard radiological/topographical classifications of gliomas often do not reflect the real extension of the tumor within the lobar-cortical anatomy. Furthermore, these systems do not provide information on the relationship between tumor growth and the subcortical white matter architecture. We propose the use of an anatomically standardized grid system (the Brain-Grid) to merge serial morphological magnetic resonance imaging (MRI) scans with a representative tractographic atlas. Two illustrative cases are presented to show the potential advantages of this classification system.

Methods

MRI scans of 39 patients (WHO grade II and III gliomas) were analyzed with a standardized grid created by intersecting longitudinal lines on the axial, sagittal, and coronal planes. The anatomical landmarks were chosen from an average brain, spatially normalized to the Montreal Neurological Institute (MNI) space and the Talairach space. Major white matter pathways were reconstructed with a deterministic tracking algorithm on a reference atlas and analyzed using the Brain-Grid system.

Results

In all, 48 brain grid voxels (areas defined by 3 coordinates, axial (A), coronal (C), sagittal (S) and numbers from 1 to 4) were delineated in each MRI sequence and on the tractographic atlas. The number of grid voxels infiltrated was consistent, also in the MNI space. The sub-cortical insula/basal ganglia (A3-C2-S2) and the fronto-insular region (A3-C2-S1) were most frequently involved. The inferior fronto-occipital fasciculus, anterior thalamic radiation, uncinate fasciculus, and external capsule were the most frequently associated pathways in both hemispheres.

Conclusions

The Brain-Grid based classification system provides an accurate observational tool in all patients with suspected gliomas, based on the comparison of grid voxels on a morphological MRI and segmented white matter atlas. Important biological information on tumor kinetics including extension, speed, and preferential direction of progression can be observed and even predicted with this system. This novel classification can easily be applied to both prospective and retrospective cohorts of patients and increase our comprehension of glioma behavior.

The Impacts of Tumor and Tumor Associated Epilepsy on Subcortical Brain Structures and Long Distance Connectivity in Patients With Low Grade Glioma

Low grade gliomas in cerebral cortex often cause symptoms related to higher cerebral functions such as attention, memory and executive function before treatment is initiated. Interestingly, focal tumors residing in one cortical region can lead to a diverse range of symptoms, indicating that the impact of a tumor is extended to multiple brain regions. We hypothesize that the presence of focal glioma in the cerebral cortex leads to alterations of distant subcortical areas and essential white matter tracts. In this study, we analyzed diffusion tensor imaging scans in glioma patients to study the effect of glioma on subcortical gray matter nuclei and long-distance connectivity. We found that the caudate nucleus, putamen and thalamus were affected by cortical glioma, displaying both volumetric and diffusion alterations. The cerebellar cortex contralateral to the tumor side also showed significant volume decrease. Additionally, tractography of the cortico-striatal and cortico-thalamic projections shows similar diffusion alterations. Tumor associated epilepsy might be an important contributing factor to the found alterations. Our findings indeed confirm concurrent structural and connectivity abrasions of brain areas distant from brain tumor, and provide insights into the pathogenesis of diverse neurological symptoms in glioma patients.

Contralesional macrostructural plasticity of the insular cortex in patients with glioma: A VBM study

OBJECTIVE

To assess the homotopic structural plasticity in case of unilateral damage of the insula.

METHODS

To detect changes in gray matter volumes of the contralesional insula from structural MRIs, we used voxel-based morphometry (VBM) in a sample of 84 patients with a diffuse low-grade glioma invading the left insula (insL group; n = 47) or the right insula (insR group; n = 37).

RESULTS

The region of interest-based VBM analysis highlighted a large cluster of voxels with gray matter volume increase in the contralesional insula in both patient groups (k = 2,214 voxels for insL and k = 879 voxels for insR, p < 0.05, family-wise error corrected) compared with 24 age-matched healthy controls. Gray matter volume was increased for the entire insula (t ₆₉ = 3.63, p = 0.0016 for insL; t ₅₉ = 3.54, p = 0.0024 for insR, Bonferroni corrected), whereas no significant changes were found in 2 control regions for both patient groups. Furthermore, an increase of 24.6% and 31.6% in the gray matter volume was observed in the insula-related VBM cluster for insL and insR patients, respectively, compared with healthy controls (t ₆₉ = 7.39, p = 2.59 × 10^-10 and t ₅₉ = 7.51, p = 3.61 × 10^-10).

CONCLUSIONS

The reported results demonstrate that slow-growing but massive lesion infiltration of the insula induces marked increase of gray matter volume in the contralateral one. Our findings give support for a homotopic reorganization that might be a physiologic basis for the high level of functional compensation observed in patients with glioma.

Artificial intelligence in radiology

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Artificial intelligence in radiology

Artificial intelligence (AI) algorithms, particularly deep learning, have demonstrated remarkable progress in image-recognition tasks. Methods ranging from convolutional neural networks to variational autoencoders have found myriad applications in the medical image analysis field, propelling it forward at a rapid pace. Historically, in radiology practice, trained physicians visually assessed medical images for the detection, characterization and monitoring of diseases. AI methods excel at automatically recognizing complex patterns in imaging data and providing quantitative, rather than qualitative, assessments of radiographic characteristics. In this Opinion article, we establish a general understanding of AI methods, particularly those pertaining to image-based tasks. We explore how these methods could impact multiple facets of radiology, with a general focus on applications in oncology, and demonstrate ways in which these methods are advancing the field. Finally, we discuss the challenges facing clinical implementation and provide our perspective on how the domain could be advanced.

Hammock Middle Cerebral Artery and Delayed Infarction in Lenticulostriate Artery After Staged Resection of Giant Insular Glioma

BACKGROUND

Delayed infarction in the lenticulostriate artery (LSA) area after insular glioma resection is not common, and its pathophysiology remains unknown.

CASE DESCRIPTION

A 32-year-old right-handed man with a giant insular low-grade glioma with frontal and temporal extension underwent awake craniotomy with an intentional staged surgery strategy. Preoperative radiologic images demonstrated a diagonally elevated middle cerebral artery (MCA) by the temporal tumor and a significantly compressed striatum. With intraoperative subcortical direct electrical stimulation, the resection was finalized in the temporal part of the tumor due to the semantic paraphasia induced in the temporal stem, fatigue, and loss of concentration. The immediate postoperative clinical course was uneventful. However, on postoperative day 20, he suddenly experienced right hemiparesis. Repeated images revealed infarction in the LSA area. The previously compressed striatum was then relieved and relocated to its original position in just 20 days, and the M1 segment of the MCA was remarkably downward, in which the MCA resembled a hammock. Angiography confirmed the hammock-shaped MCA and significantly stretched LSA, suggesting the combination of freed striatum from the compression and loss of temporal structure by the tumor resection as the key mechanism of severe dislocation of the MCA and delayed ischemia.

CONCLUSIONS

In a staged resection of giant insular glioma, attention should be paid to a possible severe dislocation of the MCA in a delayed postoperative period, which may lead to LSA stretching and delayed infarction.

Improving surgical outcome for gliomas with intraoperative mapping

INTRODUCTION

Radical glioma resection improves overall survival, both in low-grade and high-grade glial tumors. However, preservation of the quality of life is also crucial. Areas covered: Due to the diffuse feature of gliomas, which invade the central nervous system, and due to considerable variations of brain organization among patients, an individual cerebral mapping is mandatory to solve the classical dilemma between the oncological and functional issues. Because functional neuroimaging is not reliable enough, intraoperative electrical stimulation, especially in awake patients benefiting from a real-time cognitive monitoring, is the best way to increase the extent of resection while sparing eloquent neural networks. Expert commentary: Here, we propose a paradigmatic shift from image-guided resection to functional mapping-guided resection, based on the study of the dynamic distribution of delocalized cortico-subcortical circuits at the individual level, i.e., the investigation of brain connectomics and neuroplastic potential. This surgical philosophy results in an improvement of both oncological outcomes and quality of life. This highlights the need to reinforce the link between glioma surgery and cognitive neurosciences.

Population Pharmacokinetics of Tracers: A New Tool for Medical Imaging?

Positron emission tomography-computed tomography is a medical imaging method measuring the activity of a radiotracer chosen to accumulate in cancer cells. A recent trend of medical imaging analysis is to account for the radiotracer's pharmacokinetic properties at a voxel (three-dimensional-pixel) level to separate the different tissues. These analyses are closely linked to population pharmacokinetic-pharmacodynamic modelling. Kineticists possess the cultural background to improve medical imaging analysis. This article stresses the common points with population pharmacokinetics and highlights the methodological locks that need to be lifted.

Features of the normal choriocapillaris with OCT-angiography: Density estimation and textural properties

The main objective of our work is to perform an in depth analysis of the structural features of normal choriocapillaris imaged with OCT Angiography. Specifically, we provide an optimal radius for a circular Region of Interest (ROI) to obtain a stable estimate of the subfoveal choriocapillaris density and characterize its textural properties using Markov Random Fields. On each binarized image of the choriocapillaris OCT Angiography we performed simulated measurements of the subfoveal choriocapillaris densities with circular Regions of Interest (ROIs) of different radii and with small random displacements from the center of the Foveal Avascular Zone (FAZ). We then calculated the variability of the density measure with different ROI radii. We then characterized the textural features of choriocapillaris binary images by estimating the parameters of an Ising model. For each image we calculated the Optimal Radius (OR) as the minimum ROI radius required to obtain a standard deviation in the simulation below 0.01. The density measured with the individual OR was 0.52 ± 0.07 (mean ± STD). Similar density values (0.51 ± 0.07) were obtained using a fixed ROI radius of 450 μm. The Ising model yielded two parameter estimates (β = 0.34 ± 0.03; γ = 0.003 ± 0.012; mean ± STD), characterizing pixel clustering and white pixel density respectively. Using the estimated parameters to synthetize new random textures via simulation we obtained a good reproduction of the original choriocapillaris structural features and density. In conclusion, we developed an extensive characterization of the normal subfoveal choriocapillaris that might be used for flow analysis and applied to the investigation pathological alterations.

Cortex Parcellation Associated Whole White Matter Parcellation in Individual Subjects

The investigation of specific white matter areas is a growing field in neurological research and is typically achieved through the use of atlases. However, the definition of anatomically based regions remains challenging for the white matter and thus hinders region-specific analysis in individual subjects. In this article, we focus on creating a whole white matter parcellation method for individual subjects where these areas can be associated to cortex regions. This is done by combining cortex parcellation and fiber tracking data. By tracking fibers out of each cortex region and labeling the fibers according to their origin, we populate a candidate image. We then derive the white matter parcellation by classifying each white matter voxel according to the distribution of labels in the corresponding voxel from the candidate image. The parcellation of the white matter with the presented method is highly reliable and is not as dependent on registration as with white matter atlases. This method allows for the parcellation of the whole white matter into individual cortex region associated areas and, therefore, associates white matter alterations to cortex regions. In addition, we compare the results from the presented method to existing atlases. The areas generated by the presented method are not as sharply defined as the areas in most existing atlases; however, they are computed directly in the DWI space of the subject and, therefore, do not suffer from distortion caused by registration. The presented approach might be a promising tool for clinical and basic research to investigate modalities or system specific micro structural alterations of white matter areas in a quantitative manner.

IDH mutation and 1p19q codeletion distinguish two radiological patterns of diffuse low-grade gliomas

Diffuse low-grade gliomas (DLGG) prognosis is variable, depending on several factors, including the isocitrate dehydrogenase (IDH) mutation and the 1p19q codeletion. A few studies suggested associations between these parameters and tumor radiological characteristics including topography. Our aim was analyzing the correlations between the IDH and 1p19q statuses and the tumor intracerebral distribution (at the lobar and voxel levels), volume, and borders. We conducted a retrospective, monocentric study on a consecutive series of 198 DLGG patients. The IDH and 1p19q statuses were recorded. The pre-treatment magnetic resonance FLAIR imagings were reviewed for determination of lobar topography, tumor volume, and characterisation of tumor borders (sharp or indistinct). We conducted a voxel-based lesion-symptom mapping analysis to investigate the correlations between the IDH and 1p19q statuses and topography at the voxel level. The IDH mutation and 1p19q statuses were correlated with the tumor topography defined using lobar anatomy (p < 0.001 and p = 0.004, respectively). Frontal tumors were more frequently IDH-mutant (87.1 vs. 57.4%) and 1p19q codeleted (45.2 vs. 17.0%) than temporo-insular lesions. At the voxel level, these associations were not found. Tumors with sharp borders were more frequently IDH-mutant (p = 0.001) while tumors with indistinct borders were more frequently IDH wild-type and 1p19q non-codeleted (p < 0.001). Larger tumors at diagnosis (possibly linked to a slower growth rate) were more frequently IDH-mutant (p < 0.001). IDH wild-type, 1p19q non-codeleted temporo-insular tumors are distinct from IDH-mutant, 1p19q codeleted frontal tumors. Further studies are needed to determine whether the therapeutic strategy should be adapted to each pattern.

High ATP2A2 expression correlates with better prognosis of diffuse astrocytic tumor patients

Novel molecular markers are required for defining subsets of diffuse astrocytic tumor patients with differing prognoses. Here, we examined ATP2A2 expression in 109 human diffuse astrocytic tumor samples (39 grade II diffuse astrocytoma (DA), 19 grade III anaplastic astrocytoma (AA), 51 grade IV glioblastoma) and its correlation with patient clinicopathologic characteristics. ATP2A2 expression significantly correlated with tumor grade and survival (P<0.05). High ATP2A2 expression was detected in 35.3% (18/51) of glioblastoma patients, compared to 61.5% (24/39) in grade II, and 52.6% (10/19) in grade III astrocytoma patients (P=0.043). The median survival was 45±5.3 (95% CI, 34.7-55.3) months in patients with high ATP2A2 expression and 16±5.0 (95% CI, 6.3-25.7) months in patients with low ATP2A2 expression (P<0.0001). Additionally, high grade astrocytoma patients with high ATP2A2 expression showed longer survival (median, 31.0±4.9 months, 95% CI, 21.4-40.7) than those with low ATP2A2 expression (median: 13.0±1.6 months, 95% CI, 9.9-16.1; P=0.027). Furthermore, both ATP2A2 overexpression and IDH1 mutation were detected in secondary glioblastoma, AA developed from DA and oligodendrogiomas with IDH1 mutation. The MTT assays showed that lentiviral ATP2A2 overexpression significantly suppressed the clonogenic growth of glioblastoma U251MG cells (P<0.05). Xenografts stably overexpressing ATP2A2 were markedly smaller in size 4 weeks post inoculation (P<0.05). Our findings identified high ATP2A2 expression in a subset of astrocytoma patients that was associated with better prognosis and ATP2A2 suppressed astrocytoma growth.

The etiopathogenesis of diffuse low-grade gliomas

The origins of diffuse low-grade gliomas (DLGG) are unknown. Beyond some limited data on their temporal and cellular origins, the mechanisms and risk factors involved are poorly known. First, based on strong relationships between DLGG development and the eloquence of brain regions frequently invaded by these tumors, we propose a "functional theory" to explain the origin of DLGG. Second, the biological pathways involved in DLGG genesis may differ according to tumor location (anatomo-molecular correlations). The cellular and molecular mechanisms of such "molecular theory" will be reviewed. Third, the geographical distribution of diffuse WHO grade II-III gliomas within populations is heterogeneous, suggesting possible environmental risk factors. We will discuss this "environmental theory". Finally, we will summarize the current knowledge on genetic susceptibility in gliomas ("genetic predisposition theory"). These crucial issues illustrate the close relationships between the pathophysiology of gliomagenesis, the anatomo-functional organization of the brain, and personalized management of DLGG patients.

Population-based MRI atlases of spatial distribution are specific to patient and tumor characteristics in glioblastoma

BACKGROUND AND PURPOSE

In treating glioblastoma (GB), surgical and chemotherapeutic treatment guidelines are, for the most part, independent of tumor location. In this work, we compiled imaging data from a large cohort of GB patients to create statistical atlases illustrating the disease spatial frequency as a function of patient demographics as well as tumor characteristics.

MATERIALS AND METHODS

Two-hundred-six patients with pathology-proven glioblastoma were included. Of those, 65 had pathology-proven recurrence and 113 had molecular subtype and genetic information. We used validated software to segment the tumors in all patients and map them from patient space into a common template. We then created statistical maps that described the spatial location of tumors with respect to demographics and tumor characteristics. We applied a chi-square test to determine whether pattern differences were statistically significant.

RESULTS

The most frequent location for glioblastoma in our patient population is the right temporal lobe. There are statistically significant differences when comparing patterns using demographic data such as gender (p = 0.0006) and age (p = 0.006). Small and large tumors tend to occur in separate locations (p = 0.0007). The tumors tend to occur in different locations according to their molecular subtypes (p < 10(- 6)). The classical subtype tends to spare the frontal lobes, the neural subtype tend to involve the inferior right frontal lobe. Although the sample size is limited, there was a difference in location according to EGFR VIII genotype (p < 10(- 4)), with a right temporal dominance for EFGR VIII negative tumors, and frontal lobe dominance in EGFR VIII positive tumors.

CONCLUSIONS

Spatial location of GB is an important factor that correlates with demographic factors and tumor characteristics, which should therefore be considered when evaluating a patient with GB and might assist in personalized treatment.