Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

Cauter, Sofie Van; Keyzer, Frederik De; Sima, Diana M.; Sava, Anca; D’Arco, Felice; Veraart, Jelle; Peeters, Ronald; Leemans, Alexander; Gool, Stefaan Van; Wilms, Guido; Demaerel, Philippe; Huffel, Sabine Van; Sunaert, Stefan; Himmelreich, Uwe

doi:10.1093/neuonc/not304

Cited by 70 publications

(71 citation statements)

References 37 publications

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“…The present results are also comparable to a standard-dose prospective DSC perfusion study, where the authors reported a sensitivity of 78% and specificity of 91%. 29 In another DSC study, a sensitivity of 100% and specificity of 94.4% in differentiating low-and highgrade gliomas was reported. 34 In one of the patients in the present study, the tumour was reported to be low-grade based on rCBV values but turned out to be high grade at histopathology.…”

Section: Discussionmentioning

confidence: 95%

Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI

et al. 2015

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Section: Discussionmentioning

confidence: 95%

Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI

et al. 2015

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“…van Cauter et al . showed a high accuracy for the distinction of HGGs from the low-grade gliomas (LGGs) based on mean axial and radial kurtosis values 5,6 . Mean kurtosis has outperformed diagnostically the remainder of the diffusion metrics for grading (WHO grade 2–4) and predicting Ki-67 as a measure of cellularity 7 .…”

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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“…For comparison between DWI, DTI, and DKI, see Table 1. Several recent studies demonstrate that DKI in general has even higher accuracy as compared to DTI in various diseases including brain neoplasm [20,21], stroke [3,22,23], or neurodegenerative disorders [24][25][26][27][28]. This added diagnostic accuracy however has the price of longer acquisition times, which may among other problems increase head motion artifacts in patients.…”

Section: Diffusion Imagingmentioning

confidence: 99%

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

et al. 2015

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This article reviews the most relevant state-of-the-art magnetic resonance (MR) techniques, which are clinically available to investigate brain diseases. MR acquisition techniques addressed include notably diffusion imaging (diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI)) as well as perfusion imaging (dynamic susceptibility contrast (DSC), arterial spin labeling (ASL), and dynamic contrast enhanced (DCE)). The underlying models used to process these images are described, as well as the theoretic underpinnings of quantitative diffusion and perfusion MR imaging-based methods. The technical requirements and how they may help to understand, classify, or follow-up neurological pathologies are briefly summarized. Techniques, principles, advantages but also intrinsic limitations, typical artifacts, and alternative solutions developed to overcome them are discussed. In this article, we also review routinely available three-dimensional (3D) techniques in neuro MRI, including state-of-the-art and emerging angiography sequences, and briefly introduce more recently proposed 3D quantitative neuro-anatomy sequences, and new technology, such as multi-slice and multi-transmit imaging.

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Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas

Abstract: Combining information from DKI, DSC-MRI, and CSI increases diagnostic accuracy to differentiate low- from high-grade gliomas, possibly providing diagnosis for the individual patient.

Cited by 70 publications

References 37 publications

Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI

Prospective glioma grading using single-dose dynamic contrast-enhanced perfusion MRI

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

State-of-the-art MRI techniques in neuroradiology: principles, pitfalls, and clinical applications

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