Gradient of Apparent Diffusion Coefficient Values in Peritumoral Edema Helps in Differentiation of Glioblastoma From Solitary Metastatic Lesions

2014 ◽  
Vol 203 (1) ◽  
pp. 163-169 ◽  
Author(s):  
Pierre Lemercier ◽  
Silvia Paz Maya ◽  
James T. Patrie ◽  
Lucía Flors ◽  
Carlos Leiva-Salinas
Keyword(s):  
Diffusion Coefficient ◽  
Apparent Diffusion Coefficient ◽  
Peritumoral Edema ◽  
Metastatic Lesions ◽  
Apparent Diffusion Coefficient Values ◽  
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.

Diagnostic value of apparent diffusion coefficient in differentiating between high-grade gliomas and brain metastases

2017 ◽  
Vol 59 (5) ◽  
pp. 599-605 ◽  
Author(s):  
Ionut Caravan ◽  
Cristiana Augusta Ciortea ◽  
Alexandra Contis ◽  
Andrei Lebovici
Keyword(s):  
Diffusion Coefficient ◽  
Brain Metastases ◽  
Apparent Diffusion Coefficient ◽  
Curve Analysis ◽  
High Grade ◽  
Peritumoral Edema ◽  
Roc Curve Analysis ◽  
Apparent Diffusion ◽  
Adc Values ◽  
The Mean

Background High-grade gliomas (HGGs) and brain metastases (BMs) can display similar imaging characteristics on conventional MRI. In HGGs, the peritumoral edema may be infiltrated by the malignant cells, which was not observed in BMs. Purpose To determine whether the apparent diffusion coefficient values could differentiate HGGs from BMs. Material and Methods Fifty-seven patients underwent conventional magnetic resonance imaging (MRI) and diffusion-weighted imaging (DWI) before treatment. The minimum and mean ADC in the enhancing tumor (ADCmin, ADCmean) and the minimum ADC in the peritumoral region (ADCedema) were measured from ADC maps. To determine whether there was a statistical difference between groups, ADC values were compared. A receiver operating characteristic (ROC) curve analysis was used to determine the cutoff ADC value for distinguishing between HGGs and BMs. Results The mean ADCmin values in the intratumoral regions of HGGs were significantly higher than those in BMs. No differences were observed between groups regarding ADCmean values. The mean ADCmin values in the peritumoral edema of HGGs were significantly lower than those in BMs. According to ROC curve analysis, a cutoff value of 1.332 × 10−3 mm2/s for the ADCedema generated the best combination of sensitivity (95%) and specificity (84%) for distinguishing between HGGs and BMs. The same value showed a sensitivity of 95.6% and a specificity of 100% for distinguishing between GBMs and BMs. Conclusion ADC values from DWI were found to distinguish between HGGs and solitary BMs. The peritumoral ADC values are better than the intratumoral ADC values in predicting the tumor type.

The Potential Role of Peritumoral Apparent Diffusion Coefficient Evaluation in Differentiating Glioblastoma and Solitary Metastatic Lesions of the Brain

2021 ◽  
Vol 17 ◽  
Author(s):  
Murat Tepe ◽  
Suzan Saylisoy ◽  
Ugur Toprak ◽  
Ibrahim Inan
Keyword(s):  
Diffusion Coefficient ◽  
White Matter ◽  
Apparent Diffusion Coefficient ◽  
Sensitivity And Specificity ◽  
Brain Mri ◽  
Solitary Metastasis ◽  
Metastatic Lesions ◽  
Institutional Review ◽  
Apparent Diffusion ◽  
Adc Values

Objective: Differentiating glioblastoma (GBM) and solitary metastasis is not always possible using conventional magnetic resonance imaging (MRI) techniques. In conventional brain MRI, GBM and brain metastases are lesions with mostly similar imaging findings. In this study, we investigated whether apparent diffusion coefficient (ADC) ratios, ADC gradients, and minimum ADC values in the peritumoral edema tissue can be used to discriminate between these two tumors. Methods: This retrospective study was approved by the local institutional review board with a waiver of written informed consent. Prior to surgical and medical treatment, conventional brain MRI and diffusion-weighted MRI (b = 0 and b = 1000) images were taken from 43 patients (12 GBM and 31 solitary metastasis cases). Quantitative ADC measurements were performed on the peritumoral tissue from the nearest segment to the tumor (ADC1), the middle segment (ADC2), and the most distant segment (ADC3). The ratios of these three values were determined proportionally to calculate the peritumoral ADC ratios. In addition, these three values were subtracted from each other to obtain the peritumoral ADC gradients. Lastly, the minimum peritumoral and tumoral ADC values, and the quantitative ADC values from the normal appearing ipsilateral white matter, contralateral white matter and ADC values from cerebrospinal fluid (CSF) were recorded. Results: For the differentiation of GBM and solitary metastasis, ADC3 / ADC1 was the most powerful parameter with a sensitivity of 91.7% and specificity of 87.1% at the cut-off value of 1.105 (p < 0.001), followed by ADC3 / ADC2 with a cut-off value of 1.025 (p = 0.001), sensitivity of 91.7%, and specificity of 74.2%. The cut-off, sensitivity and specificity of ADC2 / ADC1 were 1.055 (p = 0.002), 83.3%, and 67.7%, respectively. For ADC3 – ADC1, the cut-off value, sensitivity and specificity were calculated as 150 (p < 0.001), 91.7% and 83.9%, respectively. ADC3 – ADC2 had a cut-off value of 55 (p = 0.001), sensitivity of 91.7%, and specificity of 77.4 whereas ADC2 – ADC1 had a cut-off value of 75 (p = 0.003), sensitivity of 91.7%, and specificity of 61.3%. Among the remaining parameters, only the ADC3 value successfully differentiated between GBM and metastasis (GBM 1802.50 ± 189.74 vs. metastasis 1634.52 ± 212.65, p = 0.022). Conclusion: The integration of the evaluation of peritumoral ADC ratio and ADC gradient into conventional MR imaging may provide valuable information for differentiating GBM from solitary metastatic lesions.

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."

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