The multi-parametric MRI has the potential to compensate for the non-specific contrast-enhancing imaging in delineating tumor margin. The purpose of this study was to propose a method by integrating machine learning with image inpainting to predict the glioblastoma invasion using advanced multi-parametric MRI. The predictive tumor regions using this approach showed significance for patient prognosis, in a cohort containing 115 glioblastoma patients. This approach could advance the scenario of mathematical image analysis by considering both imaging features and brain structure. The predictive region may have significant clinical impact on personalized and targeted surgical treatment of patients.
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