Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

Soliman, Radwa K.; Essa, Abdelhakeem A.; Elhakeem, Ahmed A. S.; Gamal, Sara A.; Zaitoun, Mohamed

doi:10.1016/j.diii.2020.12.001

Cited by 19 publications

(19 citation statements)

References 36 publications

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“…This is in complete agreement with previous results. 19,20,[35][36][37] Significantly, are decreased with glioma grade increase (Figure 5). SWI imaging protocol is very sensitive and advanced MRI imaging.…”

Section: Discussionmentioning

confidence: 91%

“…An increasing number of studies have found that the tumor cellularity was decreased with tumor grade. [19][20][21][22] ADC map provides information about the water molecules' motility and diffusion coefficient in the tissue. With cellularity increase in the tissues, motility of water molecules was restricted, and ADC value was decreased.…”

Section: Discussionmentioning

confidence: 99%

“…19 Therefore, much work on ADC's potential for glioma grading has been carried out. [19][20][21][22] Fluid attenuated inversion recovery (FLAIR) is an anatomical and conventional MRI inversion recovery pulse sequence that removes cerebrospinal fluid (CSF) signals from the images. The contrast of the brain on FLAIR images is similar to T2-weighted images, but CSF is dark.…”

Section: Introductionmentioning

confidence: 99%

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Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images

Khorasani

Tavakoli

Saboori

2021

Polish Journal of Medical Physics and Engineering

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Introduction: Based on the tumor’s growth potential and aggressiveness, glioma is most often classified into low or high-grade groups. Traditionally, tissue sampling is used to determine the glioma grade. The aim of this study is to evaluate the efficiency of the Laplacian Re-decomposition (LRD) medical image fusion algorithm for glioma grading by advanced magnetic resonance imaging (MRI) images and introduce the best image combination for glioma grading. Material and methods: Sixty-one patients (17 low-grade and 44 high-grade) underwent Susceptibility-weighted image (SWI), apparent diffusion coefficient (ADC) map, and Fluid attenuated inversion recovery (FLAIR) MRI imaging. To fuse different MRI image, LRD medical image fusion algorithm was used. To evaluate the effectiveness of LRD in the classification of glioma grade, we compared the parameters of the receiver operating characteristic curve (ROC). Results: The average Relative Signal Contrast (RSC) of SWI and ADC maps in high-grade glioma are significantly lower than RSCs in low-grade glioma. No significant difference was detected between low and high-grade glioma on FLAIR images. In our study, the area under the curve (AUC) for low and high-grade glioma differentiation on SWI and ADC maps were calculated at 0.871 and 0.833, respectively. Conclusions: By fusing SWI and ADC map with LRD medical image fusion algorithm, we can increase AUC for low and high-grade glioma separation to 0.978. Our work has led us to conclude that, by fusing SWI and ADC map with LRD medical image fusion algorithm, we reach the highest diagnostic accuracy for low and high-grade glioma differentiation and we can use LRD medical fusion algorithm for glioma grading.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images

Khorasani

Tavakoli

Saboori

2021

Polish Journal of Medical Physics and Engineering

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“…In general, HGGs are more aggressiveness compared with LGGs. The intratumoral histological heterogeneity in gliomas can re ect tumor grading [21]. Histologically, HGGs are different from LGGs in respect of nuclear anaplasia, mitoses, cellularity, neovascularization, and necrosis [22].…”

Section: Discussionmentioning

confidence: 99%

Synthetic MRI for Evaluation of Brain Gliomas Grade and Tumor Proliferative Activity：A Retrospective Study

Xie

Zhang

Liang

2021

Preprint

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PURPOSE The evaluation of gliomas grade and proliferation contribute to improve prognosis. We explored the clinical value of synthetic MRI in evaluating quantitatively gliomas grade and predicting tumor proliferative activity. METHODS Forty-six patients with histopathologically proven gliomas were included in this study and underwent MAGnetic resonance Image Compilation within 1 week before surgery. T1 values pre- and post-contrast agent (T1-pre, T1-Gd) and T2 values pre-contrast agent (T2-pre) were measured in tumor solid parts and normal white matter of contralateral mirror. T1 and T2 values were compared with gliomas grade and expression of proliferation marker Ki-67. Receiver operating characteristic analysis was performed to assess the diagnostic performance of each metric. RESULTS The values of T1-pre, ΔT1[(T1-pre) – (T1-Gd)] and Ki-67 labeling index (Ki-67 LI) were significantly higher in the group of high-grade gliomas ( HGGs, n = 26) compared with the group of low-grade gliomas ( LGGs, n = 20), whereas T1-Gd and the ratio of T1-Gd (rT1-Gd, tumor-to-normal ratio) values were significantly lower in the group of HGGs compared with the group of LGGs. Receiver operating characteristic analysis curves showed that ΔT1 values had the highest diagnostic value (sensitivity: 96.20%, specificity: 60.00%) for gliomas grade, and the optical cut-off value was 277.88ms. T1-pre (rs=0.47, P = 0.001) and ΔT1 (rs=0.56, P༜0.001) values were positively correlated with Ki-67 LI. T1-Gd (rs=-0.42, P = 0.004) and rT1-Gd (rs=-0.40, P = 0.007) values were negatively correlated with Ki-67 LI. CONCLUSIONS Synthetic MRI is helpful to distinguish HGGs from LGGs and predict the tumor proliferative activity.

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“…This may be due to spatial and temporal heterogeneity based on tumor cell structure, vasogenic edema, degenerative changes (hemorrhage, cystic or mucinous degeneration), and/or compression of normal structures that destroys normal anatomical structures. As a result, signal changes may be additive or cancel each other out when evaluated using average ADC values[46]. The ResNet model, however, could completely analyze the distribution and texture changes of DWI signals and ADC values throughout tumor volumes and surrounding structures, and displayed significant advantages over the traditional ADC value analysis.…”

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confidence: 99%

Diffusion Weighted MRI could precisely predict the pTERT mutation status of GBM using a residual convolutional neural network

Fang

et al. 2023

Preprint

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Background: Telomerase reverse transcriptase promoter (pTERT) mutation status plays a key role in the decision-making and prognosis prediction of glioblastoma (GBM). The purpose of this study was to assess the prediction value of diffusion-weighted imaging (DWI) in the pTERT mutation status of GBM Methods: MR imaging data and molecular information of 266 patients with GBM were obtained from the Second Affiliated Hospital of Zhejiang University (n=266). We trained the same residual convolutional neural network (ResNet) for each MR modality, including structural MRIs (T1-weighted, T2-weighted, contrast enhanced T1-weighted) and DWI and its associated ADC map, and their combinations to compare the predictive capacities between DWI and conventional structural MRI. Moreover, we explored the effect of different Region of interests (ROIs) on the outcome of pTERT mutation status prediction: entire tumor (solid tumor, edema and cystic regions), tumor core (solid tumor), and enhanced tumor. Results: Structural MRI modalities and their combination performed poorly in predicting the pTERT mutation status (accuracy, 51-54%, AUC, 0.545-0.571), while DWI in combination with its ADC maps yielded the best predictive performance (accuracy = 85.2%, AUC= 0.934). The further including of radiological and clinical characteristics could not further improve the predictive performance of pTERT mutation status. Among the three ROI selections, the entire tumor volume yielded the best prediction performance. Conclusion: DWI and its associated ADC maps shows promising prediction value in the pTERT mutation in GBM and are suggested to be included into the MRI protocol of GBM in clinical practice.

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Texture analysis of apparent diffusion coefficient (ADC) map for glioma grading: Analysis of whole tumoral and peri-tumoral tissue

Cited by 19 publications

References 36 publications

Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images

Using of Laplacian Re-decomposition image fusion algorithm for glioma grading with SWI, ADC, and FLAIR images

Synthetic MRI for Evaluation of Brain Gliomas Grade and Tumor Proliferative Activity：A Retrospective Study

Diffusion Weighted MRI could precisely predict the pTERT mutation status of GBM using a residual convolutional neural network

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