Fifty-eighth annual meeting of the american association of physicists in medicine
SU-F-R-02: Imaging-Genomics for Predicting GBM Molecular Subclasses and Survival
Glioblastoma (GBM) is the most common and fatal primary intracranial neoplasm. GBM is categorized into five sub-classes (classical, G-CIMP, mesenchymal, proneural and neural) that can only be determined by an invasive brain biopsy followed by RNA and DNA methylation profiling. The goal of this study is to develop imaging features extracted from conventional MRI scans and an ensemble-classification method as a potential noninvasive method to predict the five molecular sub-classes and 12-month survival status.
Tumors are segmented into 4 cardinal sub-volumes that are enhancing, non-enhancing, necrosis, and edema by BraTuma software and post-manual modification. After intensity normalization, mean, standard deviation, skewness, and kurtosis of voxel intensities and normalized volume of each of the 4 sub-volumes are calculated. The first four features are extracted from three different modalities (T1 pre & post contrast and T2 FLAIR) and the tumor texture is quantified. A feature selection phase (wrapper algorithm) is preceded the classification stage. Ensembles of binary classifiers with one versus rest strategy, along with a separate binary classifier are employed respectively for molecular classification and survival status. Two different binary classifiers are tested (SVM and K* IBL) and their confidences are used for tie-breaking technique.
With the select combination of features of the MRI modalities, the ROC area and accuracy of the prediction are 0.750 and 95% for classical, 1.000 and 100% for G-CIMP, 0.719 and 85% for mesenchymal, 0.875 and 95% for neural, 0.922 and 95% for proneural, and 0.990 and 95% for survival status respectively.
The study proposes a set of three-dimensional and rotation invariant imaging features that represent texture and volumetric characteristics of GBM tumors. The results show the select features are predictive for five molecular sub-classes and survival status in GBM. These results indicate the feasibility of obviating the biopsy for obtaining genomic information of tumor.