T2–FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project

Patel, Sohil H.; Poisson, Laila; Brat, Daniel J.; Zhou, Yueren; Cooper, Lee; Snuderl, Matija; Thomas, Cheddhi; Franceschi, Ana M.; Griffith, Brent; Flanders, Adam E.; Golfinos, John G.; Andrew, S.; Jain, Rajan

doi:10.1158/1078-0432.ccr-17-0560

Cited by 295 publications

(278 citation statements)

References 46 publications

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“…14 made an important contribution by introducing the ‘T2-FLAIR mismatch’ sign as a highly specific morphological feature of the IDH-mutant, 1p/19q non-codeleted molecular subtype of astrocytomas; Park et al . 15 have used the Visually AcceSAble Rembrandt Images (VASARI) library in lower grade gliomas and shown that features like larger proportion of enhancing tissue, multifocal/multicentric distribution, and poorly marginated non-enhancing tumour tissue were independent predictors of an IDH1 wild type tumour.…”

Section: Introductionmentioning

confidence: 99%

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

Bisdas

Shen

Thust

et al. 2018

Sci Rep

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We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status.

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Section: Introductionmentioning

confidence: 99%

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

Bisdas

Shen

Thust

et al. 2018

Sci Rep

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“…9 While T2FM demonstrates high positive predictive value when employed judiciously, it is absent in 27% to 88% of IDH mutant astrocytomas, depending on criteria used, and the reason for this remains unknown. [6][7][8]10 Diffusion Imaging Diffusion-weighted imaging (DWI) uses multiple diffusion gradients to measure the magnitude of the random movements of water molecules, defined as the apparent diffusion coefficient (ADC). Visual inspection of DWI/ADC in gliomas is generally not informative, but by defining a region of interest (ROI), it is possible to generate a histogram from the sum of voxels.…”

Section: Diagnostic Clues In Standard Mrimentioning

confidence: 99%

“…Several false‐positive cases of T2FM have been reported in pediatric‐type gliomas, perhaps limiting the utility of the marker in this population . While T2FM demonstrates high positive predictive value when employed judiciously, it is absent in 27% to 88% of IDH mutant astrocytomas, depending on criteria used, and the reason for this remains unknown …”

Section: Introductionmentioning

confidence: 99%

Clinical Imaging for Diagnostic Challenges in the Management of Gliomas: A Review

Bonm

Ritterbusch

Throckmorton

et al. 2020

Journal of Neuroimaging

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Neuroimaging plays a critical role in the management of patients with gliomas. While conventional magnetic resonance imaging (MRI) remains the standard imaging modality, it is frequently insufficient to inform clinical decision‐making. There is a need for noninvasive strategies for reliably distinguishing low‐grade from high‐grade gliomas, identifying important molecular features of glioma, choosing an appropriate target for biopsy, delineating target area for surgery or radiosurgery, and distinguishing tumor progression (TP) from pseudoprogression (PsP). One recent advance is the identification of the T2/fluid‐attenuated inversion recovery mismatch sign on standard MRI to identify isocitrate dehydrogenase mutant astrocytomas. However, to meet other challenges, neuro‐oncologists are increasingly turning to advanced imaging modalities. Diffusion‐weighted imaging modalities including diffusion tensor imaging and diffusion kurtosis imaging can be helpful in delineating tumor margins and better visualization of tissue architecture. Perfusion imaging including dynamic contrast‐enhanced MRI using gadolinium or ferumoxytol contrast agents can be helpful for grading as well as distinguishing TP from PsP. Positron emission tomography is useful for measuring tumor metabolism, which correlates with grade and can distinguish TP/PsP in the right setting. Magnetic resonance spectroscopy can identify tissue by its chemical composition, can distinguish TP/PsP, and can identify molecular features like 2‐hydroxyglutarate. Finally, amide proton transfer imaging measures intracellular protein content, which can be used to identify tumor grade/progression and distinguish TP/PsP.

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“…The current glioma histopathological criteria by World Health Organization (WHO) also adopts the aberration as a molecular diagnosis biomarker . In glioma patients, copy number codeletion at chromosomal region 1p/19q is characteristic of oligodendrogliomas and closely associated with improved survival . The copy number loss of chromosome 10 in different grades glioma is also reported and correlated with important oncogenes such as PTEN .…”

Section: Introductionmentioning

confidence: 99%

“…6 In glioma patients, copy number codeletion at chromosomal region 1p/19q is characteristic of oligodendrogliomas and closely associated with improved survival. 7,8 The copy number loss of chromosome 10 in different grades glioma is also reported and correlated with important oncogenes such as PTEN. [9][10][11] Unfortunately, the prognosis variations among individual patients still significantly interferes with the accuracy of glioma diagnosis.…”

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confidence: 97%

GATAD1 gene amplification promotes glioma malignancy by directly regulating CCND1 transcription

Zhang

Gao

2019

Cancer Medicine

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Background The GATAD1 gene overexpression induced by GATAD1 amplification upregulation is detected in different human tumors. To date, the relationship between GATAD1 amplification and glioma oncogenesis and malignancy is still unknown. Methods GATAD1 gene amplification and expression were analyzed in 187 gliomas using qPCR and immunostaining. The relation of GATAD1 to patients’ prognoses was assessed via the Kaplan–Meier method. The MTT and orthotopic tumor transplantation assays were used to identify the function of GATAD1 in glioma proliferation. cDNA microarray, ChIP qPCR, EMSA and 3C were used to screen the downstream mechanism of GATAD1 regulating glioma proliferation. Results Our results indicated that GATAD1 gene amplification and GATAD1 gene expression are novel independent diagnosis biomarkers to indicate poor outcome of glioma patients. GATAD1 knockdown can remarkably suppress GBM cell proliferation both in vitro and in vivo. GATAD1 could promote CCND1 gene transcription by inducing long range chromatin architectural interaction on the CCND1 promoter. Then GATAD1 sequentially accelerates GBM cell cycle transition and proliferation via regulating CCND1. Conclusions We identify GATAD1 as a novel potential diagnosis biomarker and promising prognosis predictor in glioma patients. Functionally, we confirm GATAD1 as an epigenetic chromatin topological regulator that promotes glioma proliferation by targeting CCND1.

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T2–FLAIR Mismatch, an Imaging Biomarker for IDH and 1p/19q Status in Lower-grade Gliomas: A TCGA/TCIA Project

Cited by 295 publications

References 46 publications

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

Texture analysis- and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction: a preliminary study

Clinical Imaging for Diagnostic Challenges in the Management of Gliomas: A Review

GATAD1 gene amplification promotes glioma malignancy by directly regulating CCND1 transcription

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