Study on the variations of the apparent diffusion coefficient in areas of solid tumor in high grade gliomas

Muti, M.; Aprile, I.; Principi, Massimo; Italiani, M.; Guiducci, Antonio; Giulianelli, G.; Ottaviano, P.

doi:10.1016/s0730-725x(02)00594-5

Cited by 31 publications

(17 citation statements)

References 10 publications

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“…Our quantitative measurements of ADC values for normal brain and gliomas are within the wide range of those reported in previous studies (8–11, 13, 22, 23). Differences in the ADC values are caused to some extent by the different imaging techniques used.…”

Section: Discussionsupporting

confidence: 86%

Can the apparent diffusion coefficient be used as a noninvasive parameter to distinguish tumor tissue from peritumoral tissue in cerebral gliomas?

Pauleit

Langen

Floeth

et al. 2004

Magnetic Resonance Imaging

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Purpose:To determine whether the apparent diffusion coefficient (ADC) can be used to distinguish between tumor tissue and peritumoral brain tissue in cerebral gliomas. Materials and Methods:Twenty-two patients with 44 biopsies were enrolled in this study. ADC maps calculated from a diffusion-weighted (DW) multislice EPI sequence were coregistered with conventional MR images. Neuronavigated biopsies and intraoperative markers were used for correlation with the histologic specimens. ADC values and lesion-to-brain ratios of the different sequences were calculated and compared for tumor tissue and peritumoral brain tissue. A logistic regression analysis was performed to determine the diagnostic value of the ADC maps. Results:The ADC values and ratios demonstrated a large overlap between tumor tissue and peritumoral tissue. Group comparisons revealed a significantly (P ϭ 0.03) lower ADC ratio in tumor tissue (mean ϭ 1.28 Ϯ 0.39) compared to peritumoral tissue (mean ϭ 1.48 Ϯ 0.30), whereas the absolute ADC values did not differ significantly. In the logistic regression analysis, the lesion-to-brain ratio of the gadolinium (Gd)-enhanced T1-weighted sequence was the most valuable predictor of the presence of tumor tissue. The ADC value and ratio were not identified as significant predictors. Conclusion:The ADC is not helpful for distinguishing tumor tissue from peritumoral brain tissue in gliomas.

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Section: Discussionsupporting

confidence: 86%

Can the apparent diffusion coefficient be used as a noninvasive parameter to distinguish tumor tissue from peritumoral tissue in cerebral gliomas?

Pauleit

Langen

Floeth

et al. 2004

Magnetic Resonance Imaging

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“…Our results are consistent with Castillo (35), who found that the ADC values helped to distinguish high-grade glioma from normal tissue but could not be used to separate highgrade glioma from surrounding edema. Other studies (28,(34)(35)(36)(37)(38)(39)(40) have reported that high-grade tumors have significantly lower ADC values than low-grade and benign tumors and that peritumoral ADC is significantly higher in low-grade than in high-grade astrocytomas. In cases where the minimum ADC was considered, it was lower in high-grade gliomas than in the low-grade gliomas and appeared to be correlated well with tumor cellularity (26).…”

Section: Discussionmentioning

confidence: 98%

Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas

et al. 2006

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Purpose To evaluate perfusion, diffusion, and spectroscopy values in enhancing and non-enhancing lesions for patients with newly diagnosed gliomas of different grades. Materials and Methods Sixty-seven patients with newly diagnosed glioma were entered into the study 20 grade II, 26 grade III and 21 grade IV. MR data were acquired at 1.5T and included diffusion weighted images (59/67 patients), dynamic perfusion weighted images (30/67 patients) and 3D H-1 MR spectroscopy (64/67 patients). Enhancing and non-enhancing lesions were delineated by a neuroradiologist and applied to maps of relative cerebral blood volume (rCBV), apparent diffusion coefficient (ADC), relative anisotropy (RA) and metabolite intensities. Results The median rCBV within enhancing regions of grade IV gliomas was significantly elevated relative to enhancing regions in grade III gliomas and normal brain. ADC was elevated relative to normal brain, but was not significantly different between grades or between enhancing and non-enhancing regions. The RA was higher in the non-enhancing region of grade IV gliomas relative to grade II and grade III. Levels of lactate plus lipid were significantly elevated in grade IV relative to grade II and grade III gliomas. Both enhancing and non-enhancing regions in grade IV gliomas showed significant correlations between CBV, ADC and choline levels. Conclusion The data were consistent with grade IV gliomas having higher membrane turnover, increased cell density and increased vascularity within enhancing lesions. Analysis of the correlations among parameters within grade IV gliomas suggested that high vascularity (high rCBV) was correlated with increased cellularity (low ADC) and increased membrane turnover (high choline) in these lesions. The non-enhancing region of grades II and III gliomas had MR parameters consistent with increased cellularity and/or membrane turnover.

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“…Other studies have reported improved differentiation of intra‐tumoral and peri‐tumoral tissue types by the application of supervised classification methods to MP‐MRI data . Although the spatial heterogeneity of HGGs can be partially explained by the presence of different stages of the disease throughout the lesion , it can also be attributed to the co‐existence of several intra‐tumoral tissue types within the same region, such as the presence of focal necrosis within viable tumor tissue , tumor infiltration in peri‐tumoral edema and the formation of vasogenic edema in the viable tumor region . Therefore, the characterization of tumoral and peri‐tumoral tissue might benefit from a model that incorporates the concept of tissue mixtures.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI

et al. 2015

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Tissue characterization in brain tumors and, in particular, in high-grade gliomas is challenging as a result of the co-existence of several intra-tumoral tissue types within the same region and the high spatial heterogeneity. This study presents a method for the detection of the relevant tumor substructures (i.e. viable tumor, necrosis and edema), which could be of added value for the diagnosis, treatment planning and follow-up of individual patients. Twenty-four patients with glioma [10 low-grade gliomas (LGGs), 14 high-grade gliomas (HGGs)] underwent a multi-parametric MRI (MP-MRI) scheme, including conventional MRI (cMRI), perfusion-weighted imaging (PWI), diffusion kurtosis imaging (DKI) and short-TE (1)H MRSI. MP-MRI parameters were derived: T2, T1 + contrast, fluid-attenuated inversion recovery (FLAIR), relative cerebral blood volume (rCBV), mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis (MK) and the principal metabolites lipids (Lip), lactate (Lac), N-acetyl-aspartate (NAA), total choline (Cho), etc. Hierarchical non-negative matrix factorization (hNMF) was applied to the MP-MRI parameters, providing tissue characterization on a patient-by-patient and voxel-by-voxel basis. Tissue-specific patterns were obtained and the spatial distribution of each tissue type was visualized by means of abundance maps. Dice scores were calculated by comparing tissue segmentation derived from hNMF with the manual segmentation by a radiologist. Correlation coefficients were calculated between each pathologic tissue source and the average feature vector within the corresponding tissue region. For the patients with HGG, mean Dice scores of 78%, 85% and 83% were obtained for viable tumor, the tumor core and the complete tumor region. The mean correlation coefficients were 0.91 for tumor, 0.97 for necrosis and 0.96 for edema. For the patients with LGG, a mean Dice score of 85% and mean correlation coefficient of 0.95 were found for the tumor region. hNMF was also applied to reduced MRI datasets, showing the added value of individual MRI modalities.

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Study on the variations of the apparent diffusion coefficient in areas of solid tumor in high grade gliomas

Cited by 31 publications

References 10 publications

Can the apparent diffusion coefficient be used as a noninvasive parameter to distinguish tumor tissue from peritumoral tissue in cerebral gliomas?

Can the apparent diffusion coefficient be used as a noninvasive parameter to distinguish tumor tissue from peritumoral tissue in cerebral gliomas?

Perfusion, diffusion and spectroscopy values in newly diagnosed cerebral gliomas

Hierarchical non-negative matrix factorization to characterize brain tumor heterogeneity using multi-parametric MRI

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