Background
Molecular markers of WHO grade II/III glioma are known to have important prognostic and predictive implications and may be associated with unique imaging phenotypes. The purpose of this study is to determine whether three clinically relevant molecular markers identified in gliomas—IDH, 1p/19q, and MGMT status—show distinct quantitative MRI characteristics on FLAIR imaging.
BACKGROUND AND PURPOSE: Treatment with bevacizumab is standard of care for recurrent high-grade gliomas; however, monitoring response to treatment following bevacizumab remains a challenge. The purpose of this study was to determine whether quantifying the sharpness of the fluid-attenuated inversion recovery hyperintense border using a measure derived from texture analysis—edge contrast—improves the evaluation of response to bevacizumab in patients with high-grade gliomas. MATERIALS AND METHODS:...
#1Rachel L. Delfanti(UCSD: University of California, San Diego)H-Index: 5
#2David Piccioni(UCSD: University of California, San Diego)H-Index: 23
Last. Nikdokht Farid(UCSD: University of California, San Diego)H-Index: 17
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The 2016 World Health Organization Classification of Tumors of the Central Nervous System incorporates the use of molecular information into the classification of brain tumors, including grade II and III gliomas, providing new prognostic information that cannot be delineated based on histopathology alone. We hypothesized that these genomic subgroups may also have distinct imaging features. A retrospective single institution study was performed on 40 patients with pathologically proven infiltrati...
Imaging phenotypes can serve as noninvasive surrogates for tumor genotypes and can provide important information on diagnosis, prognosis, and, eventually, personalized treatment.
#1Naeim Bahrami(UCSD: University of California, San Diego)H-Index: 12
#2Tyler M. Seibert(UCSD: University of California, San Diego)H-Index: 18
Last. Carrie R. McDonald(UCSD: University of California, San Diego)H-Index: 38
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Abstract Radiation therapy (RT) is a critical treatment modality for patients with brain tumors, although it can cause adverse effects. Recent data suggest that brain RT is associated with dose-dependent cortical atrophy, which could disrupt neocortical networks. This study examines whether brain RT affects structural network properties in brain tumor patients. We applied graph theory to MRI-derived cortical thickness estimates of 54 brain tumor patients before and after RT. Cortical surfaces we...
In the past few years, paradigms for the treatment of low-grade glioma have shifted, owing to new diagnostic criteria and new clinical trial evidence. Here, Jan Buckner and colleagues discuss how molecular markers are challenging previous assumptions about low-grade glioma, and examine how new data will affect diagnostic testing, surgery, chemotherapy and radiation therapy for individuals with these tumours.
Last. Hugues Duffau(French Institute of Health and Medical Research)H-Index: 102
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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 ...
OBJECTIVEMutations in the isocitrate dehydrogenase (IDH) genes are of proven diagnostic and prognostic significance for cerebral gliomas. The objective of this study was to evaluate the clinical feasibility of using a recently described method for determining IDH mutation status by using magnetic resonance spectroscopy (MRS) to detect the presence of 2-hydroxyglutarate (2HG), the metabolic product of the mutant IDH enzyme.METHODSBy extending imaging time by 6 minutes, the authors were able to in...
#1Amol Narang(JHUSOM: Johns Hopkins University School of Medicine)H-Index: 18
#2Kaisorn L. Chaichana(JHUSOM: Johns Hopkins University School of Medicine)H-Index: 52
Last. Lawrence Kleinberg(JHUSOM: Johns Hopkins University School of Medicine)H-Index: 14
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Background In patients with progressive low-grade glioma (LGG), the presence of new magnetic resonance imaging (MRI) enhancement is commonly used as an indicator of malignant degeneration, but its accuracy in this setting is uncertain. Objective We characterize the ability of new MRI enhancement to serve as a surrogate for histologic grade in patients with progressive LGG, and to explore the prognostic value of new MRI enhancement, pathologic grade, and extent of resection. Methods Patients at o...
#2Arie Perry(UCSF: University of California, San Francisco)H-Index: 105
Last. David W. Ellison(St. Jude Children's Research Hospital)H-Index: 93
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The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and oth...
MRI grading of grade II and III gliomas may have an important impact on treatment decisions. Occasionally, both conventional MRI (cMRI) and histology fail to clearly establish the tumour grade. Three cMRI features (no necrosis; no relevant oedema; absent or faint contrast enhancement) previously validated in 196 patients with supratentorial gliomas directed our selection of 68 suspected low-grade gliomas (LGG) that were also investigated by advanced MRI (aMRI), including perfusion weighted imagi...
BackgroundGlioblastoma multiforme (GBM) is the commonest malignant primary brain tumor and still has one of the worst prognoses among cancers in general. There is a need for non-invasive methods to...
Last. Jie Lu(Capital Medical University)H-Index: 17
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The purpose of this study was to investigate the predictive value of standard MR imaging features and apparent diffusion coefficient (ADC) values as specific markers for isocitrate dehydrogenase (IDH)-mutated lower grade gliomas (LGGs). Standard MR imaging features including location, T2-FLAIR mismatch sign, signal heterogeneity, margin, growth pattern, degree of enhancement and peritumoral edema, and ADC parameters of 74 LGGs were retrospectively assessed. The values of these imaging features i...
Last. Leonard Wee(Maastricht University Medical Centre)H-Index: 15
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Abstract Introduction Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM. Materials and methods Clinical data and p...
Last. Vadim Spektor(CUMC: Columbia University Medical Center)
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The natural history and treatment landscape of primary brain tumours are complicated by the varied tumour behaviour of primary or secondary gliomas (high-grade transformation of low-grade lesions), as well as the dilemmas with identification of radiation necrosis, tumour progression, and pseudoprogression on MRI. Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy. This is achieve...
This study aims to build a radiological model based on standard MR sequences for detecting methylguanine methyltransferase (MGMT) methylation in gliomas using texture analysis. A retrospective cross-sectional study was undertaken in a cohort of 53 glioma patients who underwent standard preoperative magnetic resonance (MR) imaging. Conventional visual radiographic features and clinical factors were compared between MGMT promoter methylated and unmethylated groups. Texture analysis extracted the t...
Last. Osamu Abe(UTokyo: University of Tokyo)H-Index: 67
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Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA...
Last. Pascale Varlet(French Institute of Health and Medical Research)H-Index: 56
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BACKGROUND Assess the contribution of preoperative MRI data in improving grading of adult astrocytomas reclassified according to the WHO 2016 and cIMPACT-NOW update 3. METHODS Retrospective unicentric cohort study of 679 adult patients treated for newly diagnosed diffuse astrocytic and oligodendroglial tumours (January 2006 to December 2016). We first systematically compared radiological (contrast enhancement present (CE+) versus absent (CE-)) and histopathological findings (microvascular prolif...
#1Sahil S. Nalawade(UTSW: University of Texas Southwestern Medical Center)H-Index: 4
#2Fang F. Yu(UTSW: University of Texas Southwestern Medical Center)H-Index: 2
Last. Joseph A. Maldjian(UTSW: University of Texas Southwestern Medical Center)H-Index: 62
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Deep learning has shown promise for predicting glioma molecular profiles using MR images. Before clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. We sought to evaluate the effects of motion artifact on glioma marker classifier performance and develop a deep learning motion correction network to restore classification accuracies. T2w images and molecular information were retrieved from the TCIA and TCGA databases. Three-fold cross-validation...
Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dynamics throughout the course of treatment. This is particularly difficult in gliomas, where heterogeneity has been well established at a molecular level as well as visually in conventional imaging. Thus, acquiring clinically useful features remains dif...