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• W2754607514 abstract "Abstract Introduction Glioblastoma exhibits profound tumor heterogeneity, which causes inconsistent treatment response. The aim of this study was to propose an interpretation method of diffusion tensor imaging (DTI) using joint histogram analysis of DTI-p and-q. With this method we explored the patterns of tumor infiltration which causes disruption of brain microstructure, and examined the prognostic value of tumor infiltrative patterns for patient survival. Materials and methods A total of 115 primary glioblastoma patients (mean age 59.3 years, 87 males) were prospectively recruited from July 2010 to August 2015. Patients underwent preoperative MRI scans and maximal safe resection. DTI was processed and decomposed into p and q components. The univariate and joint histograms of DTI-p and-q were constructed using the voxels of contrast-enhancing and non-enhancing regions respectively. Eight joint histogram features were obtained and correlated with tumor progression and patient survival. Their prognostic values were compared with clinical factors using receiver operating characteristic curves. Results The subregion of increased DTI-p and decreased DTI-q accounted for the largest proportion. Additional diffusion patterns can be identified via joint histogram analysis. Particularly, higher proportion of decreased DTI-p and increased DTI-q in non-enhancing region contributed to worse progression-free survival (hazard ratio = 1.08, p < 0.001) and overall survival (hazard ratio = 1.11, p < 0.001). Conclusions Joint histogram analysis of DTI can provide a comprehensive measure of tumor infiltration and microstructure change, which showed prognostic values. The subregion of decreased DTI-p and increased DTI-q in non-enhancing regions may indicate a more invasive habitat." @default.
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• W2754607514 date "2017-09-11" @default.
• W2754607514 modified "2023-09-26" @default.
• W2754607514 title "Intratumoral Heterogeneity of Tumor Infiltration of Glioblastoma Revealed by Joint Histogram Analysis of Diffusion Tensor Imaging" @default.
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• W2754607514 doi "https://doi.org/10.1101/187450" @default.
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