MR perfusion imaging is becoming an increasingly common means of evaluating a variety of cerebral pathologies, including tumors and ischemia. In particular, there has been great interest in the use of MR perfusion imaging for both assessing brain tumor grade and for monitoring for tumor recurrence in previously treated patients. Of the various techniques devised for evaluating cerebral perfusion imaging, the dynamic susceptibility contrast method has been employed most widely among clinical MR imaging practitioners. However, when implementing DSC MR perfusion imaging in a contemporary radiology practice, a neuroradiologist is confronted with a large number of decisions. These include choices surrounding appropriate patient selection, scan-acquisition parameters, data-postprocessing methods, image interpretation, and reporting. Throughout the imaging literature, there is conflicting advice on these issues. In an effort to provide guidance to neuroradiologists struggling to implement DSC perfusion imaging in their MR imaging practice, the Clinical Practice Committee of the American Society of Functional Neuroradiology has provided the following recommendations. This guidance is based on review of the literature coupled with the practice experience of the authors. While the ASFNR acknowledges that alternate means of carrying out DSC perfusion imaging may yield clinically acceptable results, the following recommendations should provide a framework for achieving routine success in this complicated-but-rewarding aspect of neuroradiology MR imaging practice.

http://www.ajnr.org/content/ajnr/36/6/E41.full.pdf

ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain.

The most frequent adult-type primary CNS tumours are diffuse gliomas, but a large variety of rarer CNS tumour types exists. The classification of these tumours is increasingly based on molecular diagnostics, which is reflected in the extensive molecular foundation of the recent WHO 2021 classification of CNS tumours. Resection as extensive as is safely possible is the cornerstone of treatment in most gliomas, and is now also recommended early in the treatment of patients with radiological evidence of histologically low-grade tumours. For the adult-type diffuse glioma, standard of care is a combination of radiotherapy and chemotherapy. Although treatment with curative intent is not available, combined modality treatment has resulted in long-term survival (>10-20 years) for some patients with isocitrate dehydrogenase (IDH) mutant tumours. Other rarer tumours require tailored approaches, best delivered in specialised centres. Targeted treatments based on molecular alterations still only play a minor role in the treatment landscape of adult-type diffuse glioma, and today are mainly limited to patients with tumours with BRAFV600E (ie, Val600Glu) mutations. Immunotherapy for CNS tumours is still in its infancy, and so far, trials with checkpoint inhibitors and vaccination studies have not shown improvement in patient outcomes in glioblastoma. Current research is focused on improving our understanding of the immunosuppressive tumour environment, the molecular heterogeneity of tumours, and the role of tumour microtube network connections between cells in the tumour microenvironment. These factors all appear to play a role in treatment resistance, and indicate that novel approaches are needed to further improve outcomes of patients with CNS tumours. 

Primary brain tumours in adults

Primary Central Nervous System Lymphoma (PCNSL) is a rare neoplasm that can involve brain, eye, leptomeninges, and rarely spinal cord. PCNSL lesions most typically enhance homogeneously on T1-weighted magnetic resonance imaging (MRI) and appear T2-hypointense, but high variability in MRI features is commonly encountered. Neurological symptoms and MRI findings may mimic high grade gliomas (HGGs), tumefactive demyelinating lesions (TDLs), or infectious and granulomatous diseases. Advanced MRI techniques (MR diffusion, spectroscopy, and perfusion) and metabolic imaging, such as Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) or amino acid PET (usually employing methionine), may be useful in distinguishing these different entities and monitoring the disease course. Moreover, emerging data suggest a role for cerebrospinal fluid (CSF) markers in predicting prognosis and response to treatments. In this review, we will address the challenges in PCNSL diagnosis, assessment of response to treatments, and evaluation of potential neurotoxicity related to chemotherapy and radiotherapy.

/pdf/primary-cns-lymphomas-challenges-in-diagnosis-and-monitoring-16qq7fjny5.pdf

Primary CNS Lymphomas: Challenges in Diagnosis and Monitoring

Background The aim of this study was to predict isocitrate dehydrogenase (IDH) genotypes of gliomas using an interpretable deep learning application for dynamic susceptibility contrast (DSC) perfusion MRI. Methods Four hundred sixty-three patients with gliomas who underwent preoperative MRI were enrolled in the study. All the patients had immunohistopathologic diagnoses of either IDH-wildtype or IDH-mutant gliomas. Tumor subregions were segmented using a convolutional neural network followed by manual correction. DSC perfusion MRI was performed to obtain T2* susceptibility signal intensity-time curves from each subregion of the tumors: enhancing tumor, non-enhancing tumor, peritumoral edema, and whole tumor. These, with arterial input functions, were fed into a neural network as multidimensional inputs. A convolutional long short-term memory model with an attention mechanism was developed to predict IDH genotypes. Receiver operating characteristics analysis was performed to evaluate the model. Results The IDH genotype predictions had an accuracy, sensitivity, and specificity of 92.8%, 92.6%, and 93.1%, respectively, in the validation set (area under the curve [AUC], 0.98; 95% confidence interval [CI], 0.969-0.991) and 91.7%, 92.1%, and 91.5%, respectively, in the test set (AUC, 0.95; 95% CI, 0.898-0.982). In temporal feature analysis, T2* susceptibility signal intensity-time curves obtained from DSC perfusion MRI with attention weights demonstrated high attention on the combination of the end of the pre-contrast baseline, up/downslopes of signal drops, and/or post-bolus plateaus for the curves used to predict IDH genotype. Conclusions We developed an explainable recurrent neural network model based on DSC perfusion MRI to predict IDH genotypes in gliomas.

Prediction of IDH genotype in gliomas with dynamic susceptibility contrast perfusion MR imaging using an explainable recurrent neural network.

In recent years Magnetic Resonance Elastography (MRE) emerged into a clinically applicable imaging technique. It has been shown that MRE is capable of measuring global changes of the viscoelastic properties of cerebral tissue. The purpose of our study was to evaluate a spatially resolved three-dimensional multi-frequent MRE (3DMMRE) for assessment of the viscoelastic properties of intracranial tumours. A total of 27 patients (63±13 years) were included. All examinations were performed on a 3.0 T scanner, using a modified phase-contrast echo planar imaging sequence. We used 7 vibration frequencies in the low acoustic range with a temporal resolution of 8 dynamics per wave cycle. Post-processing included multi-frequency dual elasto-visco (MDEV) inversion to generate high-resolution maps of the magnitude |G*| and the phase angle φ of the complex valued shear modulus. The tumour entities included in this study were: glioblastoma (n = 11), anaplastic astrocytoma (n = 3), meningioma (n = 7), cerebral metastasis (n = 5) and intracerebral abscess formation (n = 1). Primary brain tumours and cerebral metastases were not distinguishable in terms of |G*| and φ. Glioblastoma presented the largest range of |G*| values and a trend was delineable that glioblastoma were slightly softer than WHO grade III tumours. In terms of φ, meningiomas were clearly distinguishable from all other entities. In this pilot study, while analysing the viscoelastic constants of various intracranial tumour entities with an improved spatial resolution, it was possible to characterize intracranial tumours by their mechanical properties. We were able to clearly delineate meningiomas from intraaxial tumours, while for the latter group an overlap remains in viscoelastic terms.

High Resolution Imaging of Viscoelastic Properties of Intracranial Tumours by Multi-Frequency Magnetic Resonance Elastography.

Purpose
Primary central nervous system lymphoma (PCNSL) and high-grade glioma (HGG) may have similar enhancement patterns on magnetic resonance imaging (MRI), making the differential diagnosis difficult or even impractical. Relative cerebral blood volume (rCBV) and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced (DSC) perfusion MR imaging may help distinguish PCNSL from HGG. The purpose of this study was to evaluate the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of these two imaging parameters used alone or in combination for differentiating PCNSL from HGG.

Differentiation of Primary Central Nervous System Lymphomas from High-Grade Gliomas by rCBV and Percentage of Signal Intensity Recovery Derived from Dynamic Susceptibility-Weighted Contrast-Enhanced Perfusion MR Imaging

Intracranial solitary fibrous tumor/hemangiopericytoma (SFT/HPC) and meningioma are difficult to distinguish owing to their overlapping imaging manifestation on routine magnetic resonance imaging. The purpose of this study was to assess whether SFT/HPC can be differentiated from meningioma with diffusion-weighted imaging (DWI) and susceptibility-weighted imaging (SWI). We retrospectively reviewed DWI, SWI, conventreional MR, and CT imaging features of 16 patients with SFT/HPC and 96 patients with meningioma. The apparent diffusion coefficient (ADC) value, normalized ADC (nADC) value, and degree of intratumoral susceptibility signal intensity (ITSS) were compared between SFT/HPCs and meningiomas using two-sample t tests, and among SFT/HPCs, low-grade and high-grade meningioma were tested using one-way analysis of variance (ANOVA). Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to determine the differentiation capacity. The ADC value, nADC value, and the degree of ITSS in SFT/HPC were significantly higher than those in low-grade and high-grade meningiomas (all p 1.15 for nADC provided 75.00% sensitivity and 60.42% specificity for differentiating SFT/HPC from meningioma. Compared with nADC, the degree of ITSS had a moderate sensitivity (62.50%) and a higher specificity (85.42%) using the threshold value of > 1.00. Furthermore, combining DWI and SWI can achieve a relatively high differentiation capacity with a sensitivity of 81.25% and specificity of 78.12%. The nADC ratios and ITSS are useful for differentiating SFT/HPC from meningioma. Combining ITSS and nADC value appears to be a promising option for differential diagnosis.

Differentiating intracranial solitary fibrous tumor/hemangiopericytoma from meningioma using diffusion-weighted imaging and susceptibility-weighted imaging.

BACKGROUND AND PURPOSE: On DWI and DSC-PWI, hemangioblastomas and brain metastases may exhibit different signal intensities depending on their cellularity and angiogenesis. The purpose of this study was to evaluate whether a hemangioblastoma can be differentiated from a single brain metastasis with DWI and DSC-PWI. MATERIALS AND METHODS: We retrospectively reviewed DWI, DSC-PWI, and conventional MR imaging of 21 patients with hemangioblastomas and 30 patients with a single brain metastasis. Variables of minimum ADC and relative ADC were acquired by DWI and the parameter of relative maximum CBV, by DSC-PWI. Minimum ADC, relative ADC, and relative maximum CBV values were compared between hemangioblastomas and brain metastases by using the nonparametric Mann-Whitney test. The sensitivity, specificity, positive and negative predictive values, accuracy, and the area under the receiver operating characteristic curve were determined. RESULTS: Both the minimum ADC values and relative ADC ratios were significantly higher in hemangioblastomas compared with brain metastases (P CONCLUSIONS: DWI and DSC-PWI are helpful in the characterization and differentiation of hemangioblastomas from brain metastases. DWI appears to be the most efficient MR imaging technique for providing a distinct differentiation of the 2 tumor types.

http://www.ajnr.org/content/ajnr/37/10/1844.full.pdf

Differentiating Hemangioblastomas from Brain Metastases Using Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast-Enhanced Perfusion-Weighted MR Imaging.

Objectives The performance of multiparametric MRI-based radiomics models for predicting H3 K27M mutant status in diffuse midline glioma (DMG) has not been thoroughly evaluated. The optimal combination of multiparametric MRI and machine learning techniques remains undetermined. We compared the performance of various radiomics models across different MRI sequences and different machine learning techniques. Methods A total of 102 patients with pathologically confirmed DMG were retrospectively enrolled (27 with H3 K27M-mutant and 75 with H3 K27M wild-type). Radiomics features were extracted from eight sequences, and 18 feature sets were conducted by independent combination. There were three feature matrix normalization algorithms, two dimensionality-reduction methods, four feature selectors, and seven classifiers, consisting of 168 machine learning pipelines. Radiomics models were established across different feature sets and machine learning pipelines. The performance of models was evaluated using receiver operating characteristic curves with area under the curve (AUC) and compared with DeLong’s test. Results The multiparametric MRI-based radiomics models could accurately predict the H3 K27M mutant status in DMG (highest AUC: 0.807–0.969, for different sequences or sequence combinations). However, the results varied significantly between different machine learning techniques. When suitable machine learning techniques were used, the conventional MRI-based radiomics models shared similar performance to advanced MRI-based models (highest AUC: 0.875–0.915 vs. 0.807–0.926; DeLong’s test, p > 0.05). Most models had a better performance when generated with a combination of MRI sequences. The optimal model in the present study used a combination of all sequences (AUC = 0.969). Conclusions The multiparametric MRI-based radiomics models could be useful for predicting H3 K27M mutant status in DMG, but the performance varied across different sequences and machine learning techniques.

Multiparametric MRI-Based Radiomics Model for Predicting H3 K27M Mutant Status in Diffuse Midline Glioma: A Comparative Study Across Different Sequences and Machine Learning Techniques

Hemangioblastoma (HBL) within the central nervous system is a benign vascular neoplasm that usually occurs in the cerebellum. Supratentorial occurrence of HBL is an extremely rare event. Till date, approximately 129 cases of supratentorial HBL have been reported in the literature. Here, we present three new cases of supratentorial hemangioblatomas, one of which was found to have the lesions in a unique location of the choroidal fissure. The clinical, histopathological, and neuroradiological characteristics, as well as management of this rare disease are discussed with a review of the pertinent literature.

Supratentorial hemangioblastomas: three case reports and review of the literature.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Zhen Xing

Author Tools

Chat about Author