Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: GBM is a markedly heterogeneous brain tumor consisting of three main volumetric phenotypes identifiable on magnetic resonance imaging: necrosis (vN), active tumor (vAT), and edema/invasion (vE). The goal of this study is to identify the three glioblastoma multiforme (GBM) phenotypes using a texture-based gray-level co-occurrence matrix (GLCM) approach and determine whether the texture features of phenotypes are related to patient survival. MR imaging data in 40 GBM patients re analyzed. Phenotypes vN, vAT, and vE re segmented in a preprocessing step using 3D Slicer for rigid registration by T1-ighted imaging and corresponding fluid attenuation inversion recovery images. The GBM phenotypes re segmented using 3D Slicer tools. Texture features re extracted from GLCM of GBM phenotypes. Thereafter, Kruskal–Wallis test was employed to select the significant features. Robust predictive GBM features re identified and undernt numerous classifier analyses to distinguish phenotypes. Kaplan–Meier analysis was also performed to determine the relationship, if any, beten phenotype texture features and survival rate. The simulation results shod that the 22 texture features re significant with p value <0.05. GBM phenotype discrimination based on texture features shod the best accuracy, sensitivity, and specificity of 79.31, 91.67, and 98.75 %, respectively. Three texture features derived from active tumor parts: difference entropy, information measure of correlation, and inverse difference re statistically significant in the prediction of survival, with log-rank p values of 0.001, 0.001, and 0.008, respectively. Among 22 features examined, three texture features have the ability to predict overall survival for GBM patients demonstrating the utility of GLCM analyses in both the diagnosis and prognosis of this patient population.
['Ahmad Chaddad', 'Camel Tanougast']
I am writing a research paper on a idea Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients
[{"from": "human", "value": "I am writing a research paper on a idea Extracted magnetic resonance texture features discriminate between phenotypes and are associated with overall survival in glioblastoma multiforme patients"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: GBM is a markedly heterogeneous brain tumor consisting of three main volumetric phenotypes identifiable on magnetic resonance imaging: necrosis (vN), active tumor (vAT), and edema/invasion (vE). The goal of this study is to identify the three glioblastoma multiforme (GBM) phenotypes using a texture-based gray-level co-occurrence matrix (GLCM) approach and determine whether the texture features of phenotypes are related to patient survival. MR imaging data in 40 GBM patients re analyzed. Phenotypes vN, vAT, and vE re segmented in a preprocessing step using 3D Slicer for rigid registration by T1-ighted imaging and corresponding fluid attenuation inversion recovery images. The GBM phenotypes re segmented using 3D Slicer tools. Texture features re extracted from GLCM of GBM phenotypes. Thereafter, Kruskal\u2013Wallis test was employed to select the significant features. Robust predictive GBM features re identified and undernt numerous classifier analyses to distinguish phenotypes. Kaplan\u2013Meier analysis was also performed to determine the relationship, if any, beten phenotype texture features and survival rate. The simulation results shod that the 22 texture features re significant with p value <0.05. GBM phenotype discrimination based on texture features shod the best accuracy, sensitivity, and specificity of 79.31, 91.67, and 98.75 %, respectively. Three texture features derived from active tumor parts: difference entropy, information measure of correlation, and inverse difference re statistically significant in the prediction of survival, with log-rank p values of 0.001, 0.001, and 0.008, respectively. Among 22 features examined, three texture features have the ability to predict overall survival for GBM patients demonstrating the utility of GLCM analyses in both the diagnosis and prognosis of this patient population."}]