Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes

10/27/2020

∙

Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture features from the tumor subregions to identify the correlations with gene expression features and overall survival. Consequently, we utilize the radiomics to find associations with the subtypes of glioblastoma. Accordingly, the fractal dimensions of the whole tumor, tumor core, and necrosis regions show a significant difference between the Proneural, Classical and Mesenchymal subtypes. Additionally, the subtypes of GBM are predicted with an average accuracy of 79 radiomics and accuracy over 90

READ FULL TEXT

Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Inferring diagnostic and prognostic gene expression signatures across WHO glioma classifications: A network-based approach

RADIOHEAD: Radiogenomic Analysis Incorporating Tumor Heterogeneity in Imaging Through Densities

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

Biologic and Prognostic Feature scores from Whole-Slide Histology Images Using Deep Learning

Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition

Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes

Related Research

Towards computational fluorescence microscopy: Machine learning-based integrated prediction of morphological and molecular tumor profiles

Inferring diagnostic and prognostic gene expression signatures across WHO glioma classifications: A network-based approach

RADIOHEAD: Radiogenomic Analysis Incorporating Tumor Heterogeneity in Imaging Through Densities

Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm

Biologic and Prognostic Feature scores from Whole-Slide Histology Images Using Deep Learning

Fluorescence molecular optomic signatures improve identification of tumors in head and neck specimens

Inverse Projection Representation and Category Contribution Rate for Robust Tumor Recognition