ABSTRACT
Wallerian degeneration (WD) is defined as progressive anterograde disintegration of axons and accompanying demyelination after an injury to the proximal axon or cell body. Since the 1980s ...and 1990s, conventional magnetic resonance imaging (MRI) sequences have been shown to be sensitive to changes of WD in the subacute to chronic phases. More recently, advanced MRI techniques, such as diffusion‐weighted imaging (DWI) and diffusion tensor imaging (DTI), have demonstrated some of earliest changes attributed to acute WD, typically on the order of days. In addition, there is increasing evidence on the value of advanced MRI techniques in providing important prognostic information related to WD. This article reviews the utility of conventional and advanced MRI techniques for assessing WD, by focusing not only on the corticospinal tract but also other neural tracts less commonly thought of, including corticopontocerebellar tract, dentate‐rubro‐olivary pathway, posterior column of the spinal cord, corpus callosum, limbic circuit, and optic pathway. The basic anatomy of these neural pathways will be discussed, followed by a comprehensive review of existing literature supported by instructive clinical examples. The goal of this review is for readers to become more familiar with both conventional and advanced MRI findings of WD involving important neural pathways, as well as to illustrate increasing utility of advanced MRI techniques in providing important prognostic information for various pathologies.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
12.
Neuroimaging in Central Nervous System Lymphoma Nabavizadeh, Seyed Ali; Vossough, Arastoo; Hajmomenian, Mehrdad ...
Hematology/oncology clinics of North America,
08/2016, Volume:
30, Issue:
4
Journal Article
Peer reviewed
Primary central nervous system lymphoma (PCNSL) is a rare aggressive high-grade type of extranodal lymphoma. PCNSL can have a variable imaging appearance and can mimic other brain disorders such as ...encephalitis, demyelination, and stroke. In addition to PCNSL, the CNS can be secondarily involved by systemic lymphoma. Computed tomography and conventional MRI are the initial imaging modalities to evaluate these lesions. Recently, however, advanced MRI techniques are more often used in an effort to narrow the differential diagnosis and potentially inform diagnostic and therapeutic decisions.
Accurate differentiation of pseudoprogression (PsP) from tumor progression (TP) in glioblastomas (GBMs) is essential for appropriate clinical management and prognostication of these patients. In the ...present study, we sought to validate the findings of our previously developed multiparametric MRI model in a new cohort of GBM patients treated with standard therapy in identifying PsP cases.
Fifty-six GBM patients demonstrating enhancing lesions within 6 months after completion of concurrent chemo-radiotherapy (CCRT) underwent anatomical imaging, diffusion and perfusion MRI on a 3 T magnet. Subsequently, patients were classified as TP + mixed tumor (n = 37) and PsP (n = 19). When tumor specimens were available from repeat surgery, histopathologic findings were used to identify TP + mixed tumor (> 25% malignant features; n = 34) or PsP (< 25% malignant features; n = 16). In case of non-availability of tumor specimens, ≥ 2 consecutive conventional MRIs using mRANO criteria were used to determine TP + mixed tumor (n = 3) or PsP (n = 3). The multiparametric MRI-based prediction model consisted of predictive probabilities (PP) of tumor progression computed from diffusion and perfusion MRI derived parameters from contrast enhancing regions. In the next step, PP values were used to characterize each lesion as PsP or TP+ mixed tumor. The lesions were considered as PsP if the PP value was < 50% and TP+ mixed tumor if the PP value was ≥ 50%. Pearson test was used to determine the concordance correlation coefficient between PP values and histopathology/mRANO criteria. The area under ROC curve (AUC) was used as a quantitative measure for assessing the discriminatory accuracy of the prediction model in identifying PsP and TP+ mixed tumor.
Multiparametric MRI model correctly predicted PsP in 95% (18/19) and TP+ mixed tumor in 57% of cases (21/37) with an overall concordance rate of 70% (39/56) with final diagnosis as determined by histopathology/mRANO criteria. There was a significant concordant correlation coefficient between PP values and histopathology/mRANO criteria (r = 0.56; p < 0.001). The ROC analyses revealed an accuracy of 75.7% in distinguishing PsP from TP+ mixed tumor. Leave-one-out cross-validation test revealed that 73.2% of cases were correctly classified as PsP and TP + mixed tumor.
Our multiparametric MRI based prediction model may be helpful in identifying PsP in GBM patients.
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Glioblastoma is a highly heterogeneous disease, with variations observed at both phenotypical and molecular levels. Personalized therapies would be facilitated by non-invasive in vivo approaches for ...characterizing this heterogeneity. In this study, we developed unsupervised joint machine learning between radiomic and genomic data, thereby identifying distinct glioblastoma subtypes. A retrospective cohort of 571 IDH-wildtype glioblastoma patients were included in the study, and pre-operative multi-parametric MRI scans and targeted next-generation sequencing (NGS) data were collected. L21-norm minimization was used to select a subset of 12 radiomic features from the MRI scans, and 13 key driver genes from the five main signal pathways most affected in glioblastoma were selected from the genomic data. Subtypes were identified using a joint learning approach called Anchor-based Partial Multi-modal Clustering on both radiomic and genomic modalities. Kaplan-Meier analysis identified three distinct glioblastoma subtypes: high-risk, medium-risk, and low-risk, based on overall survival outcome (p < 0.05, log-rank test; Hazard Ratio = 1.64, 95% CI 1.17-2.31, Cox proportional hazard model on high-risk and low-risk subtypes). The three subtypes displayed different phenotypical and molecular characteristics in terms of imaging histogram, co-occurrence of genes, and correlation between the two modalities. Our findings demonstrate the synergistic value of integrated radiomic signatures and molecular characteristics for glioblastoma subtyping. Joint learning on both modalities can aid in better understanding the molecular basis of phenotypical signatures of glioblastoma, and provide insights into the biological underpinnings of tumor formation and progression.
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This study aimed to investigate the potential of quantitative radiomic data extracted from conventional MR images in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type glioblastomas ...(GBMs). A cohort of 57 treatment-naïve patients with IDH-mutant grade 4 astrocytomas (
= 23) and IDH-wild-type GBMs (
= 34) underwent anatomical imaging on a 3T MR system with standard parameters. Post-contrast T1-weighted and T2-FLAIR images were co-registered. A semi-automatic segmentation approach was used to generate regions of interest (ROIs) from different tissue components of neoplasms. A total of 1050 radiomic features were extracted from each image. The data were split randomly into training and testing sets. A deep learning-based data augmentation method (CTGAN) was implemented to synthesize 200 datasets from the training sets. A total of 18 classifiers were used to distinguish two genotypes of grade 4 astrocytomas. From generated data using 80% training set, the best discriminatory power was obtained from core tumor regions overlaid on post-contrast T1 using the K-best feature selection algorithm and a Gaussian naïve Bayes classifier (AUC = 0.93, accuracy = 0.92, sensitivity = 1, specificity = 0.86, PR_AUC = 0.92). Similarly, high diagnostic performances were obtained from original and generated data using 50% and 30% training sets. Our findings suggest that conventional MR imaging-based radiomic features combined with machine/deep learning methods may be valuable in discriminating IDH-mutant grade 4 astrocytomas from IDH-wild-type GBMs.
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Parkinson’s disease (PD) is a progressive neurological disorder and appears to have gender-specific symptoms. Studies have observed a higher frequency for development of PD in male than in female. In ...the current study, we evaluated the gender-based changes in cortical thickness and structural connectivity in PD patients. With informed consent, 64 PD (43 males and 21 females) patients, and 46 (12 males and 34 females) age-matched controls underwent clinical assessment including Mini-Mental State Examination (MMSE) and magnetic resonance imaging on a 1.5 Tesla clinical MR scanner. Whole brain high-resolution T1-weighted images were acquired from all subjects and used to measure cortical thickness and structural network connectivity. No significant difference in MMSE score was observed between male and female both in control and PD subjects. Male PD patients showed significantly reduced cortical thickness in multiple brain regions including frontal, parietal, temporal, and occipital lobes as compared with those in female PD patients. The graph theory-based network analysis depicted lower connection strengths, lower clustering coefficients, and altered network hubs in PD male than in PD female. Male-specific cortical thickness changes and altered connectivity in PD patients may derive from behavioral, physiological, environmental, and genetical differences between male and female, and may have significant implications in diagnosing and treating PD among genders.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Purpose: The isocitrate dehydrogenase (IDH) mutation has become one of the most important prognostic biomarkers in glioma management, indicating better treatment response and prognosis. IDH mutations ...confer neomorphic activity leading to the conversion of alpha-ketoglutarate (α-KG) to 2-hydroxyglutarate (2HG). The purpose of this study was to investigate the clinical potential of proton MR spectroscopy (1H-MRS) in identifying IDH-mutant gliomas by detecting characteristic resonances of 2HG and its complex interplay with other clinically relevant metabolites. Materials and Methods: Thirty-two patients with suspected infiltrative glioma underwent a single-voxel (SVS, n = 17) and/or single-slice-multivoxel (1H-MRSI, n = 15) proton MR spectroscopy (1H-MRS) sequence with an optimized echo-time (97 ms) on 3T-MRI. Spectroscopy data were analyzed using the linear combination (LC) model. Cramér–Rao lower bound (CRLB) values of <40% were considered acceptable for detecting 2HG and <20% for other metabolites. Immunohistochemical analyses for determining IDH mutational status were subsequently performed from resected tumor specimens and findings were compared with the results from spectral data. Mann–Whitney and chi-squared tests were performed to ascertain differences in metabolite levels between IDH-mutant and IDH-wild-type gliomas. Receiver operating characteristic (ROC) curve analyses were also performed. Results: Data from eight cases were excluded due to poor spectral quality or non-tumor-related etiology, and final data analyses were performed from 24 cases. Of these cases, 9/12 (75%) were correctly identified as IDH-mutant or IDH-wildtype gliomas through SVS and 10/12 (83%) through 1H-MRSI with an overall concordance rate of 79% (19/24). The sensitivity, specificity, positive predictive value, and negative predictive value were 80%, 77%, 86%, and 70%, respectively. The metabolite 2HG was found to be significant in predicting IDH-mutant gliomas through the chi-squared test (p < 0.01). The IDH-mutant gliomas also had a significantly higher NAA/Cr ratio (1.20 ± 0.09 vs. 0.75 ± 0.12 p = 0.016) and lower Glx/Cr ratio (0.86 ± 0.078 vs. 1.88 ± 0.66; p = 0.029) than those with IDH wild-type gliomas. The areas under the ROC curves for NAA/Cr and Glx/Cr were 0.808 and 0.786, respectively. Conclusions: Noninvasive optimized 1H-MRS may be useful in predicting IDH mutational status and 2HG may serve as a valuable diagnostic and prognostic biomarker in patients with gliomas
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Proton magnetic resonance spectroscopy (1H-MRS) allows non-invasive assessment of the metabolic landscape of biological tissue. Despite demonstrating promising findings in clinical practice, ...single-voxel or single-slice two-dimensional 1H-MRS methods present a few challenges mainly related to limited spatial coverage and low spatial and spectral resolutions. In the recent past, the advent of more sophisticated metabolic imaging and spectroscopic sequences, such as three-dimensional echoplanar spectroscopic imaging (3D-EPSI), two-dimensional correlation spectroscopy (2D-COSY), and chemical exchange saturation technique (CEST) has revolutionized the field of metabolomics. For the metabolic characterization of diffused neurodegenerative diseases, whole brain coverage is essential for a comprehensive overview of the topography and understanding of the underlying pathophysiological processes. The 3D-EPSI sequence allows the acquisition of whole brain (volumetric) metabolite maps with high spatial resolution.1 These metabolite maps can be co-registered to anatomical images for facilitating the mapping of metabolite alterations from different brain regions in a single session, thus providing the true spatial extent of a global disease. The potential of 3D‐EPSI in characterizing several neurological and neurodegenerative disorders has been reported. On conventional one-dimensional 1H-MRS, spectral peaks due to methyl, methylene, and methine protons from N-acetyl aspartate, glutamate, glutamine, gamma-aminobutyric acid, and taurine extensively overlap in the spectral region of 2-4 ppm, often confounding the reliable detection and quantification of these metabolites. In contrast, 2D-COSY offers unambiguous identification of potentially overlapping resonances by dispersing the multiplet structure of scalar (J)-coupled spin systems into a second spectral dimension,2 especially at higher field strength3,4 and by exploiting the unlikely possibility that two metabolites would share identical chemical shifts in two-dimensions. Due to technical limitations and long acquisition time, 2D-COSY sequence has not been widely used to study neurodegenerative diseases. However, future modifications would benefit from implementing faster acquisition schemes and improved spectral fitting methods for data analysis. We believe that these new approaches could make the clinical applications of the 2D-COSY sequence faster, easier, and more versatile. CEST is a relatively novel metabolic imaging modality that allows the detection of specific exogenous and endogenous metabolites/molecules present at millimolar concentrations. Exchangeable solute protons present in chemical functional groups such as amide (-CONH), amine (-NH2) or hydroxyl (-OH) resonate at a frequency different from bulk water protons. These labile protons are selectively saturated using radiofrequency irradiation, which is subsequently transferred to the bulk water pool, leading to a decrease in the water signal intensity proportional to the concentration of solute molecules, number of labile protons and proton exchange rate.5 CEST offers more than two orders of magnitude higher sensitivity compared to 1H-MRS in detecting metabolites such as glutamate, creatine, myoinositol and mobile peptides.5 While amide proton transfer (APT) imaging has been investigated in various neurological disorders, other CEST imaging techniques such as glutamate-CEST, creatine-CEST have been performed only in pre-clinical or pilot clinical studies related to neurodegenerative diseases. We believe that these newer developments in metabolic imaging techniques will have a significant impact in reshaping our understanding of biochemical profiles of various neurodegenerative diseases. However, standardization and harmonization of acquisition parameters are required for fast-tracking the implementation of these metabolic techniques into routine clinical workflow.
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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.
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.
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.
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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Cervical lymphadenopathy is a common indication for imaging evaluation of the neck. Besides metastatic squamous cell carcinoma of the head and neck, cervical lymphadenopathy can be due to many ...causes, with simple reactive lymphadenopathy on one end of the spectrum and malignant lymphadenopathy due to distant a distant infraclavicular primary, on the other end. A systematic approach to the cause of cervical lymphadenopathy which includes pattern of lymph node enlargement, lymph node characteristics, systemic symptoms and extranodal abnormalities can be very useful in arriving at the correct diagnosis. In this review, various patterns of cervical lymphadenopathy due to non-squamous cell causes are discussed.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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