Metrics and Textural Features of MRI Diffusion to Improve Classification of Pediatric Posterior Fossa Tumors

Gutierrez, Daniel Rodriguez; Awwad, Amir; Meijer, Lisethe; Manita, Muftah; Jaspan, Tim; Dineen, Robert A.; Grundy, Richard G.; Auer, Dorothee P.

doi:10.3174/ajnr.a3784

Cited by 107 publications

(87 citation statements)

References 26 publications

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“…In the majority of recent studies employing quantitative image feature assessment for gliomas, only 1 category feature from 1 MRI sequence, such as texture, was applied [16,25], which provided limited information. In this study we investigated the efficiency of radiomic features from multiple MRI sequences for gliomas grading, yielding promising results.…”

Section: Discussionmentioning

confidence: 99%

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences

et al. 2017

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BackgroundGliomas are the most common primary brain neoplasms. Misdiagnosis occurs in glioma grading due to an overlap in conventional MRI manifestations. The aim of the present study was to evaluate the power of radiomic features based on multiple MRI sequences – T2-Weighted-Imaging-FLAIR (FLAIR), T1-Weighted-Imaging-Contrast-Enhanced (T1-CE), and Apparent Diffusion Coefficient (ADC) map – in glioma grading, and to improve the power of glioma grading by combining features.Material/MethodsSixty-six patients with histopathologically proven gliomas underwent T2-FLAIR and T1WI-CE sequence scanning with some patients (n=63) also undergoing DWI scanning. A total of 114 radiomic features were derived with radiomic methods by using in-house software. All radiomic features were compared between high-grade gliomas (HGGs) and low-grade gliomas (LGGs). Features with significant statistical differences were selected for receiver operating characteristic (ROC) curve analysis. The relationships between significantly different radiomic features and glial fibrillary acidic protein (GFAP) expression were evaluated.ResultsA total of 8 radiomic features from 3 MRI sequences displayed significant differences between LGGs and HGGs. FLAIR GLCM Cluster Shade, T1-CE GLCM Entropy, and ADC GLCM Homogeneity were the best features to use in differentiating LGGs and HGGs in each MRI sequence. The combined feature was best able to differentiate LGGs and HGGs, which improved the accuracy of glioma grading compared to the above features in each MRI sequence. A significant correlation was found between GFAP and T1-CE GLCM Entropy, as well as between GFAP and ADC GLCM Homogeneity.ConclusionsThe combined radiomic feature had the highest efficacy in distinguishing LGGs from HGGs.

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Section: Discussionmentioning

confidence: 99%

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences

et al. 2017

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“…13,14,15,16 Several of these studies used DWI to distinguish between different types of paediatric posterior fossa tumours, but there were many limitations [14][15][16][17][18] . To our knowledge, the present study is the first to have specifically sought to grade ependymomas in children.…”

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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“…Conventional T1‐ and T2‐weighted magnetic resonance imaging (MRI) is essential for diagnosis and evaluation of tumor location, extent, and involvement of eloquent brain regions, but does not reliably differentiate between the various tumor grades and types . Several studies have shown that the diffusion characteristics may be helpful in narrowing down the preoperative diagnosis . Previous studies used quantitative apparent diffusion coefficient (ADC) measurements to differentiate the various grades and histological subtypes of cerebellar tumors.…”

Section: Introductionmentioning

confidence: 99%

“…Previous studies used quantitative apparent diffusion coefficient (ADC) measurements to differentiate the various grades and histological subtypes of cerebellar tumors. ADC values were typically measured in the solid, contrast enhancing component of the tumor (SCT) . This approach, however, has several limitations.…”

Section: Introductionmentioning

confidence: 99%

Histogram Analysis of Diffusion Tensor Imaging Parameters in Pediatric Cerebellar Tumors

Wagner

Narayan

Bosemani

et al. 2015

Journal of Neuroimaging

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Metrics and Textural Features of MRI Diffusion to Improve Classification of Pediatric Posterior Fossa Tumors

Cited by 107 publications

References 26 publications

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences

Grading of Gliomas by Using Radiomic Features on Multiple Magnetic Resonance Imaging (MRI) Sequences

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

Histogram Analysis of Diffusion Tensor Imaging Parameters in Pediatric Cerebellar Tumors

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