Texture Analysis in Cerebral Gliomas: A Review of the Literature

Soni, Neetu; Priya, Sarv; Bathla, Girish

doi:10.3174/ajnr.a6075

Cited by 84 publications

(88 citation statements)

References 75 publications

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“…The earliest reports have indicated that TA based on CT images has the potential of differential diagnosis of tumour heterogeneity 19 20. To date, there have been some reports on glioma grading using MRI TA 21. However, the results have been inconclusive.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis

et al. 2019

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ObjectivesTexture analysis (TA) is a method used for quantifying the spatial distributions of intensities in images using scanning software. MRI TA could be applied to grade gliomas. This meta-analysis was performed for assessing the accuracy of MRI TA in differentiating low-grade gliomas from high-grade ones.MethodsPubMed, Cochrane Library, Science Direct and Embase were searched for identifying suitable studies from their inception to 1 September 2018. The quality of the studies was evaluated on the basis of the Quality Assessment of Diagnostic Accuracy Studies guidelines. We estimated the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) using the summary receiver operating characteristic (SROC) for identifying the accuracy of MRI TA in grading gliomas. Fagan nomogram was applied for assessing the clinical utility of TA.ResultsSix studies including 440 patients were included and analysed. The pooled sensitivity, specificity, PLR, NLR and DOR with 95% CIs were 0.93 (95% CI 0.88 to 0.96), 0.86 (95% CI 0.81 to 0.89), 6.4 (95% CI 4.8 to 8.6), 0.08 (95% CI 0.05 to 0.15) and 78 (95% CI 39 to 156), respectively. The SROC curve showed an area under the curve of 0.96 (95% CI 0.93 to 0.97). Deeks test confirmed no significant publication bias in all studies. Fagan nomogram revealed that the post-test probability increased by 43% in patients with positive pre-test.ConclusionsThe findings of this meta-analysis suggested that MRI TA has high accuracy in differentiating low-grade gliomas from high-grade ones. A standardised methodology is warranted to guide the use of this technique for clinical decision-making.

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Section: Discussionmentioning

confidence: 99%

Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis

et al. 2019

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“…Table 1 [41], reflecting texture/spatial arrangements of pixel intensities present in the ROI [42]. Two-dimensional (2D) GLCM is usually quantified in 4 directions (0°, 45°, 90°, and 135°) and three-dimensional (3D) GLCM in 13 directions [43]. GLCM features are further defined in Table 2.…”

Section: Types Of Tamentioning

confidence: 99%

“…GLRLM features are described in Table 3. GLCM and GLRLM provide fine texture in short distance and run, and provide coarse texture in longer distance and run [43]. The coarseness of texture is related to spatial frequency.…”

Section: Types Of Tamentioning

confidence: 99%

Magnetic Resonance Texture Analysis in Alzheimer's disease

Cai

Zhong

et al. 2020

Academic Radiology

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Texture analysis is an emerging field that allows mathematical detection of changes in MRI signals that are not visible among image pixels. Alzheimer's disease, a progressive neurodegenerative disease, is the most common cause of dementia. Recently, multiple texture analysis studies in patients with Alzheimer's disease have been performed. This review summarizes the main contributors to Alzheimer's disease-associated cognitive decline, presents a brief overview of texture analysis, followed by review of various MR imaging texture analysis applications in Alzheimer's disease. We also discuss the current challenges for widespread clinical utilization. MR texture analysis could potentially be applied to develop neuroimaging biomarkers for use in Alzheimer's disease clinical trials and diagnosis.

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“…This data are also sought to predict clinical outcomes, such as patients' survival and responses to therapy (7). The features commonly included in this type of analysis are: volume, shape, intensity (MRI signal) and other texture features, referring to pixel intensities, their distribution pattern, and their interrelationships (20). Nowadays, the radiomics analysis has been used for various types of cancer including lung cancer (21)(22)(23) and prostate cancer (24)(25)(26)).…”

Section: Introductionmentioning

confidence: 99%

The Current State of Radiomics for Meningiomas: Promises and Challenges

Zhang

Russo

et al. 2020

Front. Oncol.

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Meningiomas are the most common primary tumors of the central nervous system. Given the fact that the majority of meningiomas are benign, the preoperative risk stratification and treatment strategy decision-making highly rely on the conventional subjective radiologic evaluation. However, this traditional diagnostic and treatment modality may not be effective in patients with aggressive-growing tumors or symptomatic patients with potential risk of recurrence after surgical resection or radiotherapy, as this passive "wait and see" strategy could miss the optimal opportunity of intervention. Radiomics, a new rising discipline, translates high-dimensional image information into abundant mathematical data by multiple computational algorithms. It provides an objective and quantitative approach to interpret the imaging data, rather than the subjective and qualitative interpretation from relatively limited human visual observation. In fact, the enormous amount of information generated by radiomics analyses provides radiological to histopathological tumor information, which are visually imperceptible, and offers technological basis to its applications amid diagnosis, treatment, and prognosis. Here, we review the latest advancements of radiomics and its applications in the prediction of the pathological grade, pathological subtype, recurrence possibility, and differential diagnosis of meningiomas, and the potential and challenges in general clinical applications. In this review, we highlight the generalization of shared radiomic features among different studies and compare different performances of popular algorithms. At last, we discuss several possible aspects of challenges and future directions in the development of radiomic applications in meningiomas.

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Texture Analysis in Cerebral Gliomas: A Review of the Literature

Cited by 84 publications

References 75 publications

Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis

Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis

Magnetic Resonance Texture Analysis in Alzheimer's disease

The Current State of Radiomics for Meningiomas: Promises and Challenges

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