Radiomic features extracted on the FA and MD maps of brain DTI images are useful for noninvasively classification/grading of LGGs vs HGGs, and grade III vs IV.
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DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes.
This study shows that delta-radiomic features derived from DSC MRI data can be used to characterize and determine the tumor grades and may be used for ...
Dec 19, 2022 · We aimed to classify LGG from HGG with high accuracy using the brain white matter (WM) network connectivity matrix constructed using diffusion tensor ...
Aug 27, 2024 · Diffusion tensor imaging (DTI) is a quantitative approach that uses water diffusion as a probe to assess brain WM tissue. Studies that measured ...
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Aug 28, 2024 · DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes.
This study aimed to quantify glioma based on the radiomics analysis and classify its grade into High-grade Glioma (HGG) or Low-grade Glioma (LGG) by various ...
May 20, 2022 · In this study, we evaluated an artificial intelligence architecture that combines both radiomics and CNN image features into a single model for ...
In this study, deep learning and radiomic features derived from axial, coronal, and sagittal plane MPR images were used to construct models for LGG and HGG ...
Radiomics refers to a field of study that extracts quantitative features from radiographic images to understand underlying tumor biology not easily appreciable ...