Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma

•A noninvasive and reliable surrogate method of determining MGMT status could serve to complement brain tumor biopsy or as an alternative in those patients who have a contraindication to undergo an invasive procedure.•The significance of magnetic resonance 3D volumetrics and qualitative imaging feat...

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Veröffentlicht in:	Computer methods and programs in biomedicine Jg. 140; S. 249 - 257
Hauptverfasser:	Kanas, Vasileios G., Zacharaki, Evangelia I., Thomas, Ginu A., Zinn, Pascal O., Megalooikonomou, Vasileios, Colen, Rivka R.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Ireland Elsevier B.V 01.03.2017 Elsevier
Schlagworte:	Adolescent Adult Aged Aged, 80 and over Brain Neoplasms - diagnostic imaging Brain Neoplasms - genetics Computer Science DNA Methylation DNA Modification Methylases - genetics DNA Modification Methylases - metabolism DNA Repair Enzymes - genetics DNA Repair Enzymes - metabolism Feature extraction Female Glioblastoma Glioblastoma - diagnostic imaging Glioblastoma - genetics Humans Internal Medicine Machine Learning Magnetic Resonance Imaging Male Medical Imaging MGMT promoter methylation Middle Aged Multivariate analysis Other Prediction model Promoter Regions, Genetic Tumor Suppressor Proteins - genetics Tumor Suppressor Proteins - metabolism Young Adult Feature extraction MGMT promoter methylation Multivariate analysis Glioblastoma Prediction model glioblastoma prediction model feature extraction multivariate analysis
ISSN:	0169-2607, 1872-7565, 1872-7565
Online-Zugang:	Volltext
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