MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00159816%3A_____%2F16%3A00068460" target="_blank" >RIV/00159816:_____/16:00068460 - isvavai.cz</a>

Result on the web

<a href="https://aapm.onlinelibrary.wiley.com/doi/full/10.1118/1.4948668" target="_blank" >https://aapm.onlinelibrary.wiley.com/doi/full/10.1118/1.4948668</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1118/1.4948668" target="_blank" >10.1118/1.4948668</a>

Result language

angličtina

Original language name

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas

Original language description

Purpose: Imaging biomarker research focuses on discovering relationships between radiological features and histological findings. In glioblastoma patients, methylation of the O-6-methylguanine methyltransferase (MGMT) gene promoter is positively correlated with an increased effectiveness of current standard of care. In this paper, the authors investigate texture features as potential imaging biomarkers for capturing the MGMT methylation status of glioblastoma multiforme (GBM) tumors when combined with supervised classification schemes. Methods: A retrospective study of 155 GBM patients with known MGMT methylation status was conducted. Co-occurrence and run length texture features were calculated, and both support vector machines (SVMs) and random forest classifiers were used to predict MGMT methylation status. Results: The best classification system (an SVM-based classifier) had a maximum area under the receiver-operating characteristic (ROC) curve of 0.85 (95% CI: 0.78-0.91) using four texture features (correlation, energy, entropy, and local intensity) originating from the T2-weighted images, yielding at the optimal threshold of the ROC curve, a sensitivity of 0.803 and a specificity of 0.813. Conclusions: Results show that supervised machine learning of MRI texture features can predict MGMT methylation status in preoperative GBM tumors, thus providing a new noninvasive imaging biomarker. (C) 2016 Author(s).

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30224 - Radiology, nuclear medicine and medical imaging

Project

<a href="/en/project/ED1.100%2F02%2F0123" target="_blank" >ED1.100/02/0123: St. Anne´s University Hospital Brno - International Clinical Research Center (FNUSA-ICRC)</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Medical physics

ISSN

0094-2405

e-ISSN

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Volume of the periodical

43

Issue of the periodical within the volume

6

Country of publishing house

US - UNITED STATES

Number of pages

10

Pages from-to

2835-2844

UT code for WoS article

000401300500018

EID of the result in the Scopus database

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