Automatic detection of the meningioma tumor firmness in MRI images

Alkubeyyer, Atheer; Ismail, Mohamed Maher Ben; Bchir, Ouiem; Alkubeyyer, Metab

doi:10.3233/xst-200644

Cited by 9 publications

(10 citation statements)

References 34 publications

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“…In medicine, GLCM was initially investigated as a method for evaluation of radiological images such as those obtained using ultrasound, computerized tomography, and nuclear magnetic resonance (Chen et al, 2015; AlKubeyyer et al, 2020). Only later, after the year 2010, the method became popular in microscopy research, as a potential tool for detecting and identifying pathological cell and tissues.…”

Section: Discussionmentioning

confidence: 99%

“…This method was also successfully applied for the quantification of changes in nuclear chromatin organization and distribution during the processes of physiological aging, programmed cell death, as well as carcinogenesis (Pantic et al, 2017; Kanai et al, 2020; Lee et al, 2020). Today, various supplementary techniques to GLCM are also occasionally used in medicine, the most important probably being the discrete wavelet transform as a form of textural analysis (Vidya et al, 2015; AlKubeyyer et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

et al. 2021

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

et al. 2021

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“…Some other features of meningiomas, which could be relevant for surgical planning, might be determined using radiomic analysis. For example, the consistency of meningioma (hard or soft) could be predicted using machine-based learning radiomic analysis with an AUC of 0.87-0.96 [92,93]. Prediction of postoperative cerebral edema could be achieved using machine-based learning radiomic analysis and achieved an AUC of 0.8 in a validation cohort [94].…”

Section: Meningiomamentioning

confidence: 99%

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Kalasauskas

Kosterhon

Keric

et al. 2022

Cancers

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The field of radiomics is rapidly expanding and gaining a valuable role in neuro-oncology. The possibilities related to the use of radiomic analysis, such as distinguishing types of malignancies, predicting tumor grade, determining the presence of particular molecular markers, consistency, therapy response, and prognosis, can considerably influence decision-making in medicine in the near future. Even though the main focus of radiomic analyses has been on glial CNS tumors, studies on other intracranial tumors have shown encouraging results. Therefore, as the main focus of this review, we performed an analysis of publications on PubMed and Web of Science databases, focusing on radiomics in CNS metastases, lymphoma, meningioma, medulloblastoma, and pituitary tumors.

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“…AlKubeyyer et al [ 4 ] demonstrated that local binary pattern features from T2-weighted images yielded an AUC of 0.87 when coupled with a k-nearest neighbor classifier for meningioma tumor firmness prediction. In line with this, Zhai et al [ 23 ] validated a radiomics nomogram for predicting meningiomas consistency, based on a logistic regression classifier, which showed an AUC of 0.960 in the test cohort ( Figure 3 ).…”

Section: Tumor Consistencymentioning

confidence: 99%

Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization

Ugga

Spadarella

Pinto

et al. 2022

Cancers

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Meningiomas are the most common extra-axial tumors of the central nervous system (CNS). Even though recurrence is uncommon after surgery and most meningiomas are benign, an aggressive behavior may still be exhibited in some cases. Although the diagnosis can be made by radiologists, typically with magnetic resonance imaging, qualitative analysis has some limitations in regard to outcome prediction and risk stratification. The acquisition of this information could help the referring clinician in the decision-making process and selection of the appropriate treatment. Following the increased attention and potential of radiomics and artificial intelligence in the healthcare domain, including oncological imaging, researchers have investigated their use over the years to overcome the current limitations of imaging. The aim of these new tools is the replacement of subjective and, therefore, potentially variable medical image analysis by more objective quantitative data, using computational algorithms. Although radiomics has not yet fully entered clinical practice, its potential for the detection, diagnostic, and prognostic characterization of tumors is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.

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Automatic detection of the meningioma tumor firmness in MRI images

Cited by 9 publications

References 34 publications

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

Effects of Iron Oxide Nanoparticles on Structural Organization of Hepatocyte Chromatin: Gray Level Co-occurrence Matrix Analysis

Beyond Glioma: The Utility of Radiomic Analysis for Non-Glial Intracranial Tumors

Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization

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