RESUMO
In glioblastoma (GBM), promoter methylation of the DNA repair gene O(6)-methylguanine-DNA methyltransferase (MGMT) is associated with benefit from chemotherapy. Correlations between MGMT promoter methylation and visually assessed imaging features on magnetic resonance (MR) have been reported suggesting that noninvasive detection of MGMT methylation status might be possible. Our study assessed whether MGMT methylation status in GBM could be predicted using MR imaging. We conducted a retrospective analysis of MR images in patients with newly diagnosed GBM. Tumor texture was assessed by two methods. First, we analyzed texture by expert consensus describing the tumor borders, presence or absence of cysts, pattern of enhancement, and appearance of tumor signal in T2-weighted images. Then, we applied space-frequency texture analysis based on the S-transform. Tumor location within the brain was determined using automatized image registration and segmentation techniques. Their association with MGMT methylation was analyzed. We confirmed that ring enhancement assessed visually is significantly associated with unmethylated MGMT promoter status (P=0.006). Texture features on T2-weighted images assessed by the space-frequency analysis were significantly different between methylated and unmethylated cases (P<0.05). However, blinded classification of MGMT promoter methylation status reached an accuracy of only 71%. There were no significant differences in the locations of methylated and unmethylated GBM tumors. Our results provide further evidence that individual MR features are associated with MGMT methylation but better algorithms for predicting methylation status are needed. The relevance of this study lies on the application of novel techniques for the analysis of anatomical MR images of patients with GBM allowing the evaluation of subtleties not seen by an observer and facilitating the standardization of the methods, decreasing the potential for interobserver bias.
Assuntos
Neoplasias Encefálicas/metabolismo , Neoplasias Encefálicas/patologia , Metilases de Modificação do DNA/metabolismo , Enzimas Reparadoras do DNA/metabolismo , Glioblastoma/metabolismo , Glioblastoma/patologia , Imageamento por Ressonância Magnética/métodos , Proteínas Supressoras de Tumor/metabolismo , Adulto , Idoso , Idoso de 80 Anos ou mais , Encéfalo/metabolismo , Encéfalo/patologia , Metilação de DNA , Metilases de Modificação do DNA/genética , Enzimas Reparadoras do DNA/genética , Feminino , Humanos , Processamento de Imagem Assistida por Computador/métodos , Masculino , Pessoa de Meia-Idade , Regiões Promotoras Genéticas , Estudos Retrospectivos , Proteínas Supressoras de Tumor/genéticaRESUMO
We present a new efficient approach for characterizing image texture based on a recently published discrete, orthonormal space-frequency transform known as the DOST. We develop a frequency-domain implementation of the DOST in two dimensions for the case of dyadic frequency sampling. Then, we describe a rapid and efficient approach to obtain local spatial frequency information for an image and show that this information can be used to characterize the horizontal and vertical frequency patterns in synthetic images. Finally, we demonstrate that DOST components can be combined to obtain a rotationally invariant set of texture features that can accurately classify a series of texture patterns. The DOST provides the computational efficiency and multi-scale information of wavelet transforms, while providing texture features in terms of Fourier frequencies. It outperforms leading wavelet-based texture analysis methods.
Assuntos
Diagnóstico por Computador/métodos , Processamento de Imagem Assistida por Computador , Imageamento Tridimensional/métodos , Reconhecimento Automatizado de Padrão , Algoritmos , Artefatos , Diagnóstico por Imagem/instrumentação , Diagnóstico por Imagem/métodos , Análise de Fourier , Humanos , Imageamento por Ressonância Magnética/métodos , Modelos Teóricos , Intensificação de Imagem Radiográfica , Tomografia Computadorizada por Raios X/métodosRESUMO
We compare T2-relaxation and diffusion tensor data from normal human brain. The relationships between myelin-water fraction (MWF) and various diffusion tensor measures [e.g., fractional anisotropy (FA), perpendicular diffusivity (ADC perpendicular) and mean diffusivity
Assuntos
Encéfalo/anatomia & histologia , Imagem de Difusão por Ressonância Magnética/métodos , Fibras Nervosas Mielinizadas/ultraestrutura , Adolescente , Adulto , Anisotropia , Mapeamento Encefálico/métodos , Feminino , Humanos , Modelos Lineares , Masculino , Pessoa de Meia-Idade , Água/química , Água/metabolismoRESUMO
In glioblastoma (GBM), promoter methylation of the DNA repair gene MGMT is associated with benefit from chemotherapy. Because MGMT promoter methylation status can not be determined in all cases, a surrogate for the methylation status would be a useful clinical tool. Correlation between methylation status and magnetic resonance imaging features has been reported suggesting that non-invasive MGMT promoter methylation status detection is possible. In this work, a retrospective analysis of T2, FLAIR and T1-post contrast MR images in patients with newly diagnosed GBM is performed using L1-regularized neural networks. Tumor texture, assessed quantitatively was utilized for predicting the MGMT promoter methylation status of a GBM in 59 patients. The texture features were extracted using a space-frequency texture analysis based on the S-transform and utilized by a neural network to predict the methylation status of a GBM. Blinded classification of MGMT promoter methylation status reached an average accuracy of 87.7%, indicating that the proposed technique is accurate enough for clinical use.