Differentiation between high-grade gliomas and metastatic brain tumors using Diffusion Tensor Imaging metrics

El-Serougy, Lamiaa; Razek, Ahmed Abdel Khalek Abdel; Mousa, Amani Ezzat; Eldawoody, Hany; El-Morsy, Ahmad

doi:10.1016/j.ejrnm.2015.08.005

Cited by 17 publications

(19 citation statements)

References 31 publications

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“…Despite this pathological difference, there have been mixed results using DTI metrics in the peritumoral region to differentiate between these two distinct tumor types (19). Although the majority of previous studies demonstrated no difference in the peritumoral FA across gliomas and metastases (6, 10, 11, 14, 20–23), few depicted higher FA in gliomas (11, 19, 24), while others found metastases to have higher FA (17, 25). Most could not find a significant difference in MD or ADC between gliomas and metastatic lesions within the peritumoral region (4, 11, 14, 15, 17, 19, 21, 24, 25).…”

Section: Introductionmentioning

confidence: 90%

“…Although the majority of previous studies demonstrated no difference in the peritumoral FA across gliomas and metastases (6, 10, 11, 14, 20–23), few depicted higher FA in gliomas (11, 19, 24), while others found metastases to have higher FA (17, 25). Most could not find a significant difference in MD or ADC between gliomas and metastatic lesions within the peritumoral region (4, 11, 14, 15, 17, 19, 21, 24, 25). While others found that high-grade gliomas had significantly lower peritumoral MD values in comparison to the metastases (3, 10, 11, 16, 22), one study revealed higher MD in high-grade gliomas compared to metastatic tumors (26).…”

Section: Introductionmentioning

confidence: 90%

“…Some studies used subjective placement of ROIs surrounding the tumor to examine FA and/or MD (3, 10, 12, 15, 16, 21, 23, 25–28). Several studies have employed experts to manually draw a perimeter around the tumor (4–7, 11, 14, 19, 22, 24, 29).…”

Section: Introductionmentioning

confidence: 99%

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High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases

et al. 2017

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Differentiating high-grade gliomas and intracranial metastases through non-invasive imaging has been challenging. Here, we retrospectively compared both intratumoral and peritumoral fractional anisotropy (FA), mean diffusivity (MD), and fluid-attenuated inversion recovery (FLAIR) measurements between high-grade gliomas and metastases. Two methods were utilized to select peritumoral region of interest (ROI). The first method utilized the manual placement of four ROIs adjacent to the lesion. The second method utilized a semiautomated and proprietary MATLAB script to generate an ROI encompassing the entire tumor. The average peritumoral FA, MD, and FLAIR values were determined within the ROIs for both methods. Forty patients with high-grade gliomas and 44 with metastases were enrolled in this study. Thirty-five patients with high-grade glioma and 30 patients with metastases had FLAIR images. There was no significant difference in age, gender, or race between the two patient groups. The high-grade gliomas had a significantly higher tumor-to-brain area ratio compared to the metastases. There were no differences in average intratumoral FA, MD, and FLAIR values between the two groups. Both the manual sample method and the semiautomated peritumoral ring method resulted in significantly higher peritumoral FA and significantly lower peritumoral MD in high-grade gliomas compared to metastases (p < 0.05). No significant difference was found in FLAIR values between the two groups peritumorally. Receiver operating curve analysis revealed FA to be a more sensitive and specific metric to differentiate high-grade gliomas and metastases than MD. The differences in the peritumoral FA and MD values between high-grade gliomas and metastases seemed due to the infiltration of glioma to the surrounding brain parenchyma.

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confidence: 90%

Section: Introductionmentioning

confidence: 90%

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High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases

et al. 2017

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“…Notable differences have been demonstrated between high‐grade brain tumors and brain metastases based on advanced MRI methods including diffusing tensor imaging (DTI), MR spectroscopy (MRS), and perfusion imaging . A few studies used automatic quantitative analysis methods and machine‐learning techniques to differentiate between high‐grade brain tumors and brain metastases, using texture analysis.…”

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confidence: 99%

“…11,12 A precise diagnosis requires a good correlation of clinical, radiological, and histopathological data, yet many errors in the interpretation of neurosurgical specimens occur due to misleading clinical and radiological findings. 13 Notable differences have been demonstrated between high-grade brain tumors and brain metastases based on advanced MRI methods including diffusing tensor imaging (DTI), 14,15 MR spectroscopy (MRS), 16,17 and perfusion imaging. 18,19 A few studies used automatic quantitative analysis methods and machine-learning techniques to differentiate between high-grade brain tumors and brain metastases, using texture analysis.…”

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Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis

Artzi

Bressler

Bashat

2019

Magnetic Resonance Imaging

134

107

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Background Differentiation between glioblastoma and brain metastasis is highly important due to differing medical treatment strategies. While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between glioblastoma and solitary brain metastasis may be challenging due to their similar appearance on MRI. Purpose To differentiate between glioblastoma and brain metastasis subtypes using radiomics analysis based on conventional post‐contrast T1‐weighted (T1W) MRI. Study Type Retrospective. Subjects Data were acquired from 439 patients: 212 patients with glioblastoma and 227 patients with brain metastasis (breast, lung, and others). Field Strength/Sequence Post‐contrast 3D T1W gradient echo images, acquired with 1.5 and 3.0 T MR systems. Assessment Analysis included image preprocessing, segmentation of tumor area, and features extraction including: patients' clinical information, tumor location, first‐ and second‐order statistical, morphological, wavelet features, and bag‐of‐features. Following dimension reduction, classification was performed using various machine‐learning algorithms including support‐vector machine (SVM), k‐nearest neighbor, decision trees, and ensemble classifiers. Statistical Tests For classification, the data were divided into training (80%) and testing datasets (20%). Following optimization of the classifiers, mean sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were calculated. Results For the testing dataset, the best results for differentiation of glioblastoma from brain metastasis were obtained using the SVM classifier with mean accuracy = 0.85, sensitivity = 0.86, specificity = 0.85, and AUC = 0.96. The best classification results between glioblastoma and brain metastasis subtypes were obtained using SVM classifier with mean accuracy = 0.85, 0.89, 0.75, 0.90; sensitivity = 1.00, 0.60, 0.57, 0.11; specificity = 0.76, 0.92, 0.87, 0.99; and AUC = 0.98, 0.81, 0.83, 0.57 for the glioblastoma, breast, lung, and other brain metastases, respectively. Data Conclusion Differentiation between glioblastoma and brain metastasis showed a high success rate based on postcontrast T1W MRI. Classification between glioblastoma and brain metastasis subtypes may require additional MR sequences with other tissue contrasts. Level of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:519–528.

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Differentiation Between High‐Grade Glioma and Brain Metastasis Using Cerebral Perfusion‐Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta‐Analysis of Perfusion‐weighted MRI Techniques

Mohammadi,

Ghaderi,

Jouzdani

et al. 2024

Magnetic Resonance Imaging

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BackgroundDistinguishing high‐grade gliomas (HGGs) from brain metastases (BMs) using perfusion‐weighted imaging (PWI) remains challenging. PWI offers quantitative measurements of cerebral blood flow (CBF) and cerebral blood volume (CBV), but optimal PWI parameters for differentiation are unclear.PurposeTo compare CBF and CBV derived from PWIs in HGGs and BMs, and to identify the most effective PWI parameters and techniques for differentiation.Study TypeSystematic review and meta‐analysis.PopulationTwenty‐four studies compared CBF and CBV between HGGs (n = 704) and BMs (n = 488).Field Strength/SequenceArterial spin labeling (ASL), dynamic susceptibility contrast (DSC), dynamic contrast‐enhanced (DCE), and dynamic susceptibility contrast‐enhanced (DSCE) sequences at 1.5 T and 3.0 T.AssessmentFollowing the PRISMA guidelines, four major databases were searched from 2000 to 2024 for studies evaluating CBF or CBV using PWI in HGGs and BMs.Statistical TestsStandardized mean difference (SMD) with 95% CIs was used. Risk of bias (ROB) and publication bias were assessed, and I2 statistic was used to assess statistical heterogeneity. A P‐value<0.05 was considered significant.ResultsHGGs showed a significant modest increase in CBF (SMD = 0.37, 95% CI: 0.05–0.69) and CBV (SMD = 0.26, 95% CI: 0.01–0.51) compared with BMs. Subgroup analysis based on region, sequence, ROB, and field strength for CBF (HGGs: 375 and BMs: 222) and CBV (HGGs: 493 and BMs: 378) values were conducted. ASL showed a considerable moderate increase (50% overlapping CI) in CBF for HGGs compared with BMs. However, no significant difference was found between ASL and DSC (P = 0.08).Data ConclusionASL‐derived CBF may be more useful than DSC‐derived CBF in differentiating HGGs from BMs. This suggests that ASL may be used as an alternative to DSC when contrast medium is contraindicated or when intravenous injection is not feasible.Level of Evidence1.Technical EfficacyStage 2.

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Differentiation between high-grade gliomas and metastatic brain tumors using Diffusion Tensor Imaging metrics

Cited by 17 publications

References 31 publications

High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases

High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases

Differentiation between glioblastoma, brain metastasis and subtypes using radiomics analysis

Differentiation Between High‐Grade Glioma and Brain Metastasis Using Cerebral Perfusion‐Related Parameters (Cerebral Blood Volume and Cerebral Blood Flow): A Systematic Review and Meta‐Analysis of Perfusion‐weighted MRI Techniques

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