An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging

Drabycz, Sylvia; Roldán, Glòria; Robles, Paula de; Adler, Daniel; McIntyre, John B.; Magliocco, Anthony M.; Cairncross, J. Gregory; Mitchell, J. Ross

doi:10.1016/j.neuroimage.2009.09.049

Cited by 179 publications

(168 citation statements)

References 19 publications

Supporting

Mentioning

151

Contrasting

Unclassified

Order By: Relevance

“…12,34 Rim enhancement has been reported to be associated with unmethylated primary GBM, whereas a mixed-nodular enhancement pattern was associated with methylated and secondary GBM. 23,35,36 We did not find a significant difference in edema levels or tumor location between methylated and unmethylated tumors, similar to findings in prior reports. 35,36 However, we did find that edema was able to stratify survival in methylated but not in unmethylated tumors.…”

Section: Discussionsupporting

confidence: 91%

Relationship between Tumor Enhancement, Edema,IDH1Mutational Status,MGMTPromoter Methylation, and Survival in Glioblastoma

Carrillo¹,

Lai²,

Nghiemphu³

et al. 2012

AJNR Am J Neuroradiol

268

203

View full text Add to dashboard Cite

show abstract

Section: Discussionsupporting

confidence: 91%

Relationship between Tumor Enhancement, Edema,IDH1Mutational Status,MGMTPromoter Methylation, and Survival in Glioblastoma

Carrillo¹,

Lai²,

Nghiemphu³

et al. 2012

AJNR Am J Neuroradiol

268

203

View full text Add to dashboard Cite

show abstract

“…In a similar manner, Stockwell transform analyses of T2-weighted images have shown potential for identifying the patients with high-grade gliomas (glioblastoma multiforme) most amenable to therapy. 39 Specifically, Drabycz et al 39 compared visual assessment with the analysis of spatial frequency with respect to their capacity to identify the methylation status of a gene promoter associated with good chemotherapeutic outcomes. After examination of the Stockwell spectra for T2, FLAIR, and postcontrast T1 images, the authors found that the spectral features extracted from T2-weighted images were more specific markers of methylation status than visual identification of ring-enhancement (65% versus 39%) but at a loss of sensitivity (79% versus 93% for spectral and visual textural features, respectively).…”

Section: Brain Tumorsmentioning

confidence: 99%

Texture Analysis: A Review of Neurologic MR Imaging Applications

Kassner¹,

Thornhill²

2010

AJNR Am J Neuroradiol

352

265

View full text Add to dashboard Cite

SUMMARY:Texture analysis describes a variety of image-analysis techniques that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Texture analysis may be particularly well-suited for lesion segmentation and characterization and for the longitudinal monitoring of disease or recovery. We begin this review by outlining the general procedure for performing texture analysis, identifying some potential pitfalls and strategies for avoiding them. We then provide an overview of some intriguing neuro-MR imaging applications of texture analysis, particularly in the characterization of brain tumors, prediction of seizures in epilepsy, and a host of applications to MS.ABBREVIATIONS: ABSV ϭ absolute gradient value; AIS ϭ acute ischemic stroke; ANN ϭ artificial neural network; CIS ϭ clinically isolated syndrome; d ϭ distance; DCE ϭ dynamic contrastenhanced; FCD ϭ focal cortical dysplasia; FLAIR ϭ fluid-attenuated inversion recovery; f x ϭ second-order gray-level co-occurrence feature number "x"; GLCM ϭ gray-level co-occurrence matrix; GM ϭ gray matter; HT ϭ hemorrhagic transformation; LDA ϭ linear discriminant analysis; MGL ϭ mean gray level; MGR ϭ mean gradient; MS ϭ multiple sclerosis; MTR ϭ magnetization transfer ratio; NAWM ϭ normal-appearing white matter; N g ϭ number of gray levels; PCA ϭ principal components analysis; PPMS ϭ primary-progressive MS; RLM ϭ run-length matrix; ROC ϭ receiver-operator characteristic; ROI ϭ region of interest; RRMS ϭ relapsing-remitting MS; rtPA ϭ recombinant tissue plasminogen activator; SPMS ϭ secondary-progressive MS; SVM ϭ support vector machine; ϭ direction; VGL ϭ variance of gray levels; VGR ϭ variance gradient; WM ϭ white matter

show abstract

“…Cancer genetics Assessment associated with biological gene sets, such as cell cycle phase, DNA recombination, immune system regulatory process [1]. Moreover, various mutations of glioblastoma (GBM), such as 1p/19q deletion, MGMT methylation, TP53, EGFR, and NF1, were predicted by magnetic resonance imaging (MRI) volumetric measures, including tumor volume, necrosis volume, and contrast enhancing volume [28][29][30]. Radiomic feature extracted with FDG PET scan of cervix cancer deals with the Texture analysis of tumor [15,29,30].…”

Section: Discussionmentioning

confidence: 99%

Quantitative Radiomic Phenotyping of Cervix Cancer

Anbumani¹

2018

ICPJL

View full text Add to dashboard Cite

Radiomics aims to extract huge amount of quantifiable features from medical images with data-characterization algorithms [1][2][3][4][5]. Radiologists cannot appreciate all the disease characteristics in generic diagnostic images [6]. Using these radiomic features, it is possible to uncover the physiognomies of disease progression. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalized therapy [1,7,8]. Radiomics emerged from oncology [3] is the most cutting-edge innovation. However, the technique can be useful in any medical study involving tomographic imaging. This article analyzes currently available phenotyping algorithms for Lung and Head & Neck malignancies. And It can help researchers to understand the concept to apply the same for cervix cancer.

show abstract

An analysis of image texture, tumor location, and MGMT promoter methylation in glioblastoma using magnetic resonance imaging

Cited by 179 publications

References 19 publications

Relationship between Tumor Enhancement, Edema,IDH1Mutational Status,MGMTPromoter Methylation, and Survival in Glioblastoma

Relationship between Tumor Enhancement, Edema,IDH1Mutational Status,MGMTPromoter Methylation, and Survival in Glioblastoma

Texture Analysis: A Review of Neurologic MR Imaging Applications

Quantitative Radiomic Phenotyping of Cervix Cancer

Contact Info

Product

Resources

About