scholarly journals Correlation of apparent diffusion coefficient values measured by diffusion MRI and MGMT promoter methylation semiquantitatively analyzed with MS-MLPA in patients with glioblastoma multiforme

2012 ◽  
Vol 37 (2) ◽  
pp. 351-358 ◽  
Author(s):  
Leonard Sunwoo ◽  
Seung Hong Choi ◽  
Chul-Kee Park ◽  
Jin Wook Kim ◽  
Kyung Sik Yi ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Glioblastoma Multiforme ◽  
Apparent Diffusion Coefficient ◽  
Promoter Methylation ◽  
Diffusion Mri ◽  
Mgmt Promoter Methylation ◽  
Apparent Diffusion Coefficient Values ◽  
Mgmt Promoter ◽  
Apparent Diffusion

Accuracy of apparent diffusion coefficient in differentiation of glioblastoma from metastasis

2021 ◽  
pp. 197140092098367
Author(s):  
Sanaz Beig Zali ◽  
Farbod Alinezhad ◽  
Mahnaz Ranjkesh ◽  
Mohammad H Daghighi ◽  
Masoud Poureisa
Keyword(s):  
Diffusion Coefficient ◽  
Standard Deviation ◽  
Glioblastoma Multiforme ◽  
Brain Metastasis ◽  
Apparent Diffusion Coefficient ◽  
Predictive Value ◽  
Magnetic Resonance Images ◽  
Peritumoral Edema ◽  
Apparent Diffusion Coefficient Values ◽  
Apparent Diffusion

Background Brain metastasis and glioblastoma multiforme are two of the most common malignant brain neoplasms. There are many difficulties in distinguishing these diseases from each other. Purpose The purpose of this study was to determine whether the mean apparent diffusion coefficient and absolute standard deviation derived from apparent diffusion coefficient measurements can be used to differentiate glioblastoma multiforme from brain metastasis based on cellularity levels. Material and methods Magnetic resonance images of 34 patients with histologically verified brain tumors were evaluated retrospectively. Apparent diffusion coefficient and standard deviation values were measured in the enhancing tumor, peritumoral region, and contralateral healthy white matter. Then, to determine whether there was a statistical difference between brain metastasis and glioblastoma multiforme, we analyzed different variables between the two groups. Results Neither mean apparent diffusion coefficient values and ratios nor standard deviation values and ratios were significantly different between glioblastoma multiforme and brain metastasis. Receiver operating characteristic curve analysis of the logistic model with backward stepwise feature selection yielded an area under the curve of 0.77, a specificity of 84%, a sensitivity of 67%, a positive predictive value of 83.33%, and a negative predictive value of 78.26% for distinguishing between glioblastoma multiforme and brain metastasis. The absolute standard deviation and standard deviation ratios were significantly higher in the peritumoral edema compared to the tumor region in each case. Conclusion Apparent diffusion coefficient values and ratios, as well as standard deviation values and ratios in peritumoral edema, cannot be used to differentiate edema with infiltration of tumor cells from vasogenic edema. However, standard deviation values could successfully characterize areas of peritumoral edema from the tumoral region in each case.

Added Value of Quantitative Apparent Diffusion Coefficient Values for Neuroprognostication After Cardiac Arrest

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000011991
Author(s):  
Anke Wouters ◽  
Lauranne Scheldeman ◽  
Sam Plessers ◽  
Ronald Peeters ◽  
Sarah Cappelle ◽  
...  
Keyword(s):  
Cardiac Arrest ◽  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Brain Mri ◽  
Multivariate Model ◽  
Added Value ◽  
Neurological Recovery ◽  
Adc Value ◽  
Apparent Diffusion Coefficient Values ◽  
Apparent Diffusion

ObjectiveTo test the prognostic value of brain MRI in addition to clinical and electrophysiological variables in post-cardiac arrest (CA) patients, we explored data from the randomized Neuroprotect post-CA trial (NCT02541591).MethodsIn this trial brain MRI’s were prospectively obtained. We calculated receiver operating characteristic curves for the average Apparent Diffusion Coefficient (ADC) value and percentage of brain voxels with an ADC value < 650 x 10-6 mm2/s and < 450 x 10-6 mm2/s. We constructed multivariable logistic regression models with clinical characteristics, electroencephalogram (EEG), somatosensory evoked potentials (SSEP) and ADC value as independent variables, to predict good neurological recovery.ResultsIn 79/102 patients MRI data were available and in 58/79 patients all other data were available. At 180 days post-CA, 25/58 (43%) patients had good neurological recovery. In univariable analysis of all tested MRI parameters, average ADC value in the postcentral cortex had the highest accuracy to predict good neurological recovery with an AUC of 0.78. In the most optimal multivariate model which also included corneal reflexes and EEG, this parameter remained an independent predictor of good neurological recovery (AUC = 0.96, false positive = 27%). This model provided a more accurate prediction compared to the most optimal combination of EEG, corneal reflexes and SSEP (p=0.03).ConclusionAdding information on brain MRI in a multivariate model may improve the prediction of good neurological recovery in post-CA patients.Classification of Evidence:"This study provides Class III evidence that MRI ADC features predict neurological recovery in post-cardiac arrest patients."

scholarly journals Differentiating glioblastoma multiforme from cerebral lymphoma: application of advanced texture analysis of quantitative apparent diffusion coefficients

2020 ◽  
Vol 33 (5) ◽  
pp. 428-436
Author(s):  
Mehrsad Mehrnahad ◽  
Sara Rostami ◽  
Farnaz Kimia ◽  
Reza Kord ◽  
Morteza Sanei Taheri ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Diffusion Coefficient ◽  
Glioblastoma Multiforme ◽  
Apparent Diffusion Coefficient ◽  
Central Nervous System Lymphoma ◽  
Second Order ◽  
Regions Of Interest ◽  
Apparent Diffusion ◽  
Histogram Feature

Purpose The purpose of this study was to differentiate glioblastoma multiforme from primary central nervous system lymphoma using the customised first and second-order histogram features derived from apparent diffusion coefficients. Methods and materials: A total of 82 patients (57 with glioblastoma multiforme and 25 with primary central nervous system lymphoma) were included in this study. The axial T1 post-contrast and fluid-attenuated inversion recovery magnetic resonance images were used to delineate regions of interest for the tumour and peritumoral oedema. The regions of interest were then co-registered with the apparent diffusion coefficient maps, and the first and second-order histogram features were extracted and compared between glioblastoma multiforme and primary central nervous system lymphoma groups. Receiver operating characteristic curve analysis was performed to calculate a cut-off value and its sensitivity and specificity to differentiate glioblastoma multiforme from primary central nervous system lymphoma. Results Based on the tumour regions of interest, apparent diffusion coefficient mean, maximum, median, uniformity and entropy were higher in the glioblastoma multiforme group than the primary central nervous system lymphoma group ( P ≤ 0.001). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the maximum of 2.026 or less (95% confidence interval (CI) 75.1–99.9%), and the most specific first and second-order histogram feature was smoothness of 1.28 or greater (84.0% CI 70.9–92.8%). Based on the oedema regions of interest, most of the first and second-order histogram features were higher in the glioblastoma multiforme group compared to the primary central nervous system lymphoma group ( P ≤ 0.015). The most sensitive first and second-order histogram feature to differentiate glioblastoma multiforme from primary central nervous system lymphoma was the 25th percentile of 0.675 or less (100% CI 83.2–100%) and the most specific first and second-order histogram feature was the median of 1.28 or less (85.9% CI 66.3–95.8%). Conclusions Texture analysis using first and second-order histogram features derived from apparent diffusion coefficient maps may be helpful in differentiating glioblastoma multiforme from primary central nervous system lymphoma.

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