Characterization of abnormal diffusion properties of supratentorial brain tumors: a preliminary diffusion tensor imaging study

Yuan, Weihong; Holland, Scott K.; Jones, Blaise V.; Crone, Kerry R.; Mangano, Francesco T.

doi:10.3171/ped/2008/1/4/263

Cited by 32 publications

(24 citation statements)

References 19 publications

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“…FA ranges from 0 (isotropic with zero net direction) to 1 (maximal anisotropy that occurs along the primary eigenvector). Other indices available to characterize isotropic and anisotropic elements of tŝƐƐƵĞ ĂƌĞ͗ ĞŝŐĞŶ|ĂůƵĞƐ ŝŶĚŝĐĞƐ͕ ƐƵĐŚ ĂƐ ĂǆŝĂů ĚŝĨĨƵƐŝŽŶ ĐŽĞĨĨŝĐŝĞŶƚ ; ʄ ʜʜ ) and radial diffusion ĐŽĞĨĨŝĐŝĞŶƚ ;ʄ ŏ ) (124,125). In addition to the degree of anisotropy there are geometric "shape" indices that may be particularly relevant in tumor that may induce anisotropy by compression of otherwise isotropic spherical cells, such as linear (CL), planar (CP) , or spherical (CS) tensor shape (117,126,127).…”

Section: Theoretic Basis and Imaging Techniquesmentioning

confidence: 99%

Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema

et al. 2009

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Background: Conventional magnetic resonance (MR) imaging has a number of limitations in the diagnosis of the most common intracranial brain tumors, including tumor specification and the detection of tumoral infiltration in regions of peritumoral edema. Purpose: To prospectively assess if diffusion-weighted MR imaging (DWI) could be used to differentiate between different types of brain tumors and to distinguish between peritumoral infiltration in high-grade gliomas, lymphomas, and pure vasogenic edema in metastases and meningiomas. Material and Methods: MR imaging and DWI was performed on 93 patients with newly diagnosed brain tumors: 59 patients had histologically verified high-grade gliomas (37 glioblastomas multiforme, 22 anaplastic astrocytomas), 23 patients had metastatic brain tumors, five patients had primary cerebral lymphomas, and six patients had meningiomas. Apparent diffusion coefficient (ADC) values of tumor (enhancing regions or the solid portion of tumor) and peritumoral edema, and ADC ratios (ADC of tumor or peritumoral edema to ADC of contralateral white matter, ADC of tumor to ADC of peritumoral edema) were compared with the histologic diagnosis. ADC values and ratios of high-grade gliomas, primary cerebral lymphomas, metastases, and meningiomas were compared by using ANOVA and multiple comparisons. Optimal thresholds of ADC values and ADC ratios for distinguishing high-grade gliomas from metastases were determined by receiver operating characteristic (ROC) curve analysis. Results: Statistically significant differences were found for minimum and mean of ADC tumor and ADC tumor ratio values between metastases and high-grade gliomas when including only one factor at a time. Including a combination of in total four parameters (mean ADC tumor, and minimum, maximum and mean ADC tumor ratio) resulted in sensitivity, specificity, positive (PPV), and negative predictive values (NPV) of 72.9, 82.6, 91.5, and 54.3% respectively. In the ROC curve analysis, the area under the curve of the combined four parameters was the largest (0.84), indicating a good test. Conclusion: Our results suggest that ADC values and ADC ratios (minimum and mean of ADC tumor and ADC tumor ratio) may be helpful in the differentiation of metastases from high-grade gliomas. It cannot distinguish high-grade gliomas from lymphomas, and lymphomas from metastases. ADC values and ADC ratios in peritumoral edema cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema when measurements for high-grade gliomas, lymphomas, metastases, and meningiomas were compared.

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Section: Theoretic Basis and Imaging Techniquesmentioning

confidence: 99%

Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema

et al. 2009

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“…Early reports established a correlation between MD and brain tumour cellularity (12)(13)(14)(15)(16)(17)(18)(19), perhaps explaining the observation of significantly lower diffusivity within lymphomas than meningiomas, metastases or high-grade gliomas (18,(20)(21)(22)(23). FA has been reported to be significantly different between high-grade and low-grade gliomas (13,24), and tumour MD may aid in the classification of low-grade gliomas (25).…”

Section: Introductionmentioning

confidence: 99%

Diffusion tensor imaging discriminates between glioblastoma and cerebral metastases in vivo

et al. 2011

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In a prospective study, patients with a radiologically proven brain tumour underwent diffusion tensor imaging (DTI) prior to definitive diagnosis and treatment. Twenty-eight patients with a histologically proven glioblastoma or metastasis were included in the study. Following the definition of regions of interest, DTI metrics [mean diffusivity (MD) and fractional anisotropy (FA)] were calculated for the tumour volume and the surrounding region of peritumoral oedema. These metrics were then subjected to logistic regression to investigate their ability to discriminate between glioblastomas and cerebral metastases. A cross-validation was performed to investigate the ability of the model to predict tumour. The logistic regression analysis correctly distinguished glioblastoma in 15 of 16 cases (93.8%) and metastasis in 11 of 12 cases (91.7%). Cross-validation resulted in the model correctly predicting 14 of 16 (87.5%) glioblastomas and 10 of 12 (83.3%) metastases studied. MD was significantly higher (p = 0.02) and FA was significantly lower (p = 0.04) within the oedema surrounding metastases than within the oedema around glioblastomas. MD was significantly higher (p = 0.02) within the tumour volume of the glioblastomas. Our results demonstrate that, when DTI metrics from the tumour volume and surrounding peritumoral oedema are studied in combination, glioblastoma can be reliably discriminated from cerebral metastases.

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“…[14][15][16] Previous DTI studies of WM development have been limited by small numbers of pediatric patients, inclusion of data from multiple institutions, combination of adult and pediatric data, or inclusion of potentially biasing pathologies without further follow-up. 4,8,13,[17][18][19][20][21][22] The aim of our study was, thus, to report a representative dataset of normal age-related changes in FA and MD values detected in the developing brain, derived from 202 healthy participants ranging in age from birth to adolescence.…”

mentioning

confidence: 99%

DTI Values in Key White Matter Tracts from Infancy through Adolescence

Cancelliere

Mangano²,

Air³

et al. 2013

AJNR Am J Neuroradiol

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Characterization of abnormal diffusion properties of supratentorial brain tumors: a preliminary diffusion tensor imaging study

Cited by 32 publications

References 19 publications

Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema

Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema

Diffusion tensor imaging discriminates between glioblastoma and cerebral metastases in vivo

DTI Values in Key White Matter Tracts from Infancy through Adolescence

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