## 1 Introduction
[PERSON] et al. [8] in their survival prediction study, extracted 4524 radiomic features which are segmented from tumor regions, the important features are selected using cross validation and decision tree. Finally, they used the random forest model to achieve 61% classification success in prediction of short versusu long-term survivors. Recently, the deep learning methods have been utilized for survival prediction. [PERSON] et al. [9] proposed a deep learning architecture, named segmentation then prediction, which was based on 3D U-Net. The proposed architecture comprised a segmentation component, which delineates the tumor into the three main subregions (i.e., whole tumor, enhancing tumor and tumor core), and a feature extractor, by computing features from (i) whole tumor only, and (ii) from final layer of segmentation component. Global and local features were used together to predict survival in gliomas, yielding the validation accuracy of 65.5%. In yet another study on survival prediction of gliomas by [PERSON] et al. [10], authors used multi-regional radiomics analysis by employing the three-subregions radiomics, six-subregions radiomics and twenty one-subregions radiomics model. The top performance was obtained using 3-subregions radiomics model with (mean AUC = 0.73). [PERSON] et al. [11] proposed a model that is multilevel layer and dynamic architecture in a faster R-CNN (MLL-CNN) approach. This model utilizes the relative description model based on feature weight factor to build selected features to predict glioblastoma survival. Some researchers also show the effectiveness of combining traditional features extracted from MRI images and the deep learning features extracted by the deep learning models. [PERSON] et al. [12] extracted intensity, wavelet, texture, shape along with radiological features taken from tumor area, and a CNN network based deep features. They used the random forest regression model for prediction of OS in gliomas and reported a root mean square error of 311.5. [PERSON] [2], used 4 MR images (T1, T2, T1c and FLAIR), extracted an initial set of 1353 features. They used deep learning CNN1 and CNN2 strategies. Their method performed well, but they used only radiomics sets and their characteristics and features. Some researchers used pathology features obtained from the regions demarcated on pathology images [13] [PERSON] et al. [13] trained a deep neural network on 663 pathology images acquired from TCIA to estimate parameters of microvascular proliferation, nuclear atypia, mitotic activity and the presence of necrosis, and eventually predict OS. They reported a c-statistic of 0.82 (\(p\)-value = 4.8 \(\times\) 10\({}^{-5}\)) between overall survival and risk scores of proposed deep learning model. [PERSON] et al. [14] also confirmed these findings using the same dataset. Similarly, [PERSON] et al. [15] used a survival neural network on 769 pathology images acquired from TCIA to predict OS in gliomas. They reported a c-statistic of 0.77 between OS deep learning model with risk score. Despite the various studies showing the potential of MRI and pathology images alone in the prediction of OS, the previous studies lack in exploiting the integrated potential of radiology and pathology images, which provide diagnostically relevant information. In these prior studies, only radiology or pathology images commonly employed, in contrast to synergistically to combine both imaging modalities to build more robust predictive and most powerful model. To overcome shortcomings, we analyze and integrate entirety of pathology and radiology data and leverage the power of advanced pattern analysis methods to achieve better prognosis of gliomas. We hypothesise in this study that the integration of MRI and digital pathology images can be utilized for better prognosis of gliomas compared to that achieved with either of these imaging modalities. The proposed method utilizes the MRI images, as well as digital pathology images, as input data, and the combination thereof to achieve better prognosis of gliomas.