Prognostic Value of Dynamic Susceptibility Contrast-Enhanced and Diffusion-Weighted MR Imaging in Patients with Glioblastomas

Çoban, Gökçen; Mohan, Suyash; Kural, Feride; Wang, S.; O’Rourke, Donald M.; Poptani, Harish

doi:10.3174/ajnr.a4284

Cited by 28 publications

(25 citation statements)

References 43 publications

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“…This is further supported by the observation that hypo-perfusion (as seen in pattern 3), previously reported to be a marker for better clinical out-come in glioma [15], correlates with high-grade meningiomas with poor prognosis. Angiogenesis in both meningioma and glioma is known to share a common signaling pathway mediated by vascular endothelial growth factor (VEGF) [16,26,27].…”

Section: Discussionsupporting

confidence: 73%

“…Perfusion, diffusion and MR spec-troscopy have all been investigated as potential methods to add value to standard imaging in the assessment of meningiomas [12]. Most perfu-sion studies of central nervous system tumors have focused on the use of quantitative parameters, such as rCBV and CBF, as markers of histo-pathological grade, therapy response and clinical outcome [13–15]. In the present study, a qualitative examination of CBF maps was conducted in a patient cohort with meningioma, and its potential for predicting meningioma tumor grade was analyzed.…”

Section: Discussionmentioning

confidence: 99%

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Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas

Qiao

Kim

Wang

et al. 2017

European Journal of Radiology

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Purpose: Differentiating WHO grade I–III of meningioma by non-invasive imaging is challenging. This study investigated the potential of MR arterial spin labeling (ASL) to establish tumor grade in meningioma patients. Material and methods: Pseudo-continuous ASL with 3D background suppressed gradient and spin echo (GRASE) was acquired on 54 patients with newly diagnosed or recurrent intracranial meningioma. Perfusion patterns characterized in CBF color maps were independently evaluated by three neuroradiologists blinded to patient history, and correlated with tumor grade from histo-pathological review. Results: Three perfusion patterns could be discerned by visual evaluation of CBF maps. Pattern 1 consisted of homogeneous hyper-perfusion of the entire tumor; pattern 2 demonstrated heterogeneous hyper-perfusion; pattern 3 showed no substantial hyper-perfusion. Evaluation of the perfusion patterns was highly concordant among the three readers (Kendall W = 0.9458, P < 0.0001). Pattern 1 was associated with WHO Grade I meningioma of (P < 0.0001). Patterns 2 and 3 were predictive of WHO Grade II and III meningioma (P < 0.0001), with an odds ratio (OR, versus pattern 1) of 49.6 (P < 0.01) in a univariate analysis, and an OR of 186.4 (P < 0.01) in a multivariate analysis. Conclusion: Qualitative evaluation of ASL CBF maps can help differentiate benign (WHO Grade I) from higher grade (WHO Grade II and III) intracranial meningiomas, potentially impacting therapeutic strategy.

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Section: Discussionsupporting

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas

Qiao

Kim

Wang

et al. 2017

European Journal of Radiology

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“…Several studies have suggested a threshold for rCBV that could help to establish the histopathological grade of adult gliomas and predict outcome, 6,12 but to date, no studies have been published on ependymomas. In the present study, the low number of grade-II patients compared with grade-III patients prevented us from investigating the difference between these grades, therefore we ran separate analyses of the differences between CEL and NEL.…”

Section: Discussionmentioning

confidence: 99%

Prognostic and predictive values of diffusion and perfusion MRI in paediatric intracranial ependymomas in a large national study

Tensaouti

Ducassou

Chaltiel

et al. 2016

BJR

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Objective: To assess the relative cerebral blood volume (rCBV) and apparent diffusion coefficient (ADC) derived, respectively, from perfusion and diffusion pre-operative MRI of intracranial ependymomas and their predictive and prognostic values. Methods: Pre-operative MRI and clinical data for intracranial ependymomas diagnosed between January 2000 and December 2013 were retrospectively retrieved from a web-based national database. MRI data included diffusion (62 patients) and perfusion (20 patients) MRI. Patient age, histopathological diagnosis, tumour location, ADC, relative ADC (rADC) and rCBV were considered as potential factors in a survival analysis. Survival rates were estimated using the Kaplan-Meier method. Univariate analyses were performed using the log-rank test to compare groups. We also performed a multivariate analysis, applying the Cox proportional hazards model.Results: ADC and rADC values within hypointense regions differed significantly between grades II and III (p 5 0.01). The 75th percentile of ADC within hypointense regions and the 25th percentile of rCBV within nonenhancing lesions were prognostic of disease-free survival (p 5 0.004, p 5 0.05). A significant correlation was found between the 75th percentile of rCBV and the 25th percentile of rADC (p 5 0.01) in enhancing regions of grade-III tumours. Conclusion: Pre-operative rADC and rCBV could be used as prognostic factors for clinical outcome and to predict histological grade in paediatric ependymomas. Advances in knowledge: Prognostic value of diffusion and perfusion MRI in paediatric ependymoma was found and may play a role in the prognostic classification of patients in order to design more tailored treatment strategies.

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“…Several prognostic factors influence the survival of patients with glioblastoma, including clinical characteristics (patient age, performance status), molecular characteristics (most notably, 06-methylguanine DNA methyltransferase promotor methylation, isocitrate dehydrogenase mutation, and deletion of the 1p and 19q chromosome arms [3]). Moreover, characteristics of noninvasive magnetic resonance (MR) imaging (most importantly, the apparent diffusion coefficient [ADC] derived from diffusion-weighted MR imaging and the relative cerebral blood volume [CBV] derived from perfusion-weighted MR imaging) also have been shown to be reliable prognostic biomarkers for stratification of patients with glioblastoma (4,5). More recently, the field of radiomics has been introduced to extend the noninvasive study of oncologic tissue beyond established MR imaging metrics, and a large number of quantitative descriptors that reflect textural variations in image intensity, among other features, have been derived from imaging data (6)(7)(8)(9)(10)(11).…”

Section: Author Contributionsmentioning

confidence: 99%

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

Kickingereder¹,

Burth²,

et al. 2016

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Purpose To evaluate whether radiomic feature-based magnetic resonance (MR) imaging signatures allow prediction of survival and stratification of patients with newly diagnosed glioblastoma with improved accuracy compared with that of established clinical and radiologic risk models. Materials and Methods Retrospective evaluation of data was approved by the local ethics committee and informed consent was waived. A total of 119 patients (allocated in a 2:1 ratio to a discovery [n = 79] or validation [n = 40] set) with newly diagnosed glioblastoma were subjected to radiomic feature extraction (12 190 features extracted, including first-order, volume, shape, and texture features) from the multiparametric (contrast material-enhanced T1-weighted and fluid-attenuated inversion-recovery imaging sequences) and multiregional (contrast-enhanced and unenhanced) tumor volumes. Radiomic features of patients in the discovery set were subjected to a supervised principal component (SPC) analysis to predict progression-free survival (PFS) and overall survival (OS) and were validated in the validation set. The performance of a Cox proportional hazards model with the SPC analysis predictor was assessed with C index and integrated Brier scores (IBS, lower scores indicating higher accuracy) and compared with Cox models based on clinical (age and Karnofsky performance score) and radiologic (Gaussian normalized relative cerebral blood volume and apparent diffusion coefficient) parameters. Results SPC analysis allowed stratification based on 11 features of patients in the discovery set into a low- or high-risk group for PFS (hazard ratio [HR], 2.43; P = .002) and OS (HR, 4.33; P < .001), and the results were validated successfully in the validation set for PFS (HR, 2.28; P = .032) and OS (HR, 3.45; P = .004). The performance of the SPC analysis (OS: IBS, 0.149; C index, 0.654; PFS: IBS, 0.138; C index, 0.611) was higher compared with that of the radiologic (OS: IBS, 0.175; C index, 0.603; PFS: IBS, 0.149; C index, 0.554) and clinical risk models (OS: IBS, 0.161, C index, 0.640; PFS: IBS, 0.139; C index, 0.599). The performance of the SPC analysis model was further improved when combined with clinical data (OS: IBS, 0.142; C index, 0.696; PFS: IBS, 0.132; C index, 0.637). Conclusion An 11-feature radiomic signature that allows prediction of survival and stratification of patients with newly diagnosed glioblastoma was identified, and improved performance compared with that of established clinical and radiologic risk models was demonstrated. (©) RSNA, 2016 Online supplemental material is available for this article.

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Prognostic Value of Dynamic Susceptibility Contrast-Enhanced and Diffusion-Weighted MR Imaging in Patients with Glioblastomas

Cited by 28 publications

References 43 publications

Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas

Application of arterial spin labeling perfusion MRI to differentiate benign from malignant intracranial meningiomas

Prognostic and predictive values of diffusion and perfusion MRI in paediatric intracranial ependymomas in a large national study

Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models

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