## 3 Results  
The statistical analysis was performed using R version 3.2.4 developed by R Foundation for Statistical Computing, Vienna, Austria. In this section, the obtained results and discussions of ensemble regression methods are presented for the prediction of survival of glioma patients. The results gained from extracted features LBP, GLCM, GLSZM, GLRLM, and NGTDM from radiology (rad) images, pathology (path) images, and combination (ad-path) images are processed through the ensemble regression machine learning method, comprising support vector regression (SVR), AdaBoost, gradient boost, random forest (RF). To confirm the robustness of the method, while avoiding overfitting on the same patient sets, we evaluated the ensemble regression method in all 171 patients using leave-one-out (LOO) cross-validation (configuration-I), repeated 5-fold cross-validation (configuration-II), and repeated the split-train-test (3:2 ratio) (configuration-III). In all the configurations, rad, path, and rad-path features were separately used to evaluate the performance on all the cancer grades (i.e., HGGs + LGGs, HGGs, and LGGs). The outputs of the models were compared against the actual survival to calculate the performance in terms of [PERSON]'s correlation coefficient (PCC). The value of PCC ranges from \(-1\) to \(+1\), where values lower and higher than zero, respectively, show negative and positive correlation, with \(-1\) and \(+1\) being the highest values of correlation. Performance is also summarized in terms of mean absolute error. Survival prediction results of the proposed method are given in Figure 2, where the x-axis shows the actual survival of the patients and the y-axis shows the predicted survival in terms of the number of days. In split-train-test configuration, the proposed method yielded robust performance, as shown by the calculated [PERSON]'s correlation coefficients for rad-path. \(\big{(}R=0.83,\ p=8.4\times 10^{-11}\big{)}\), followed by pathology (path) images with \(\big{(}R=0.72,\ p=2.6\times 10^{-7}\big{)}\) and radiology (Rad) images \(\big{(}R=0.72,\ p=3.7\times 10^{-7}\big{)}\). The survival prediction of HGGs patients (second row) yielded with test-train split validation for rad-path images, producing PCC of \(\big{(}R=0.79,\ p=1.8\times 10^{-7}\big{)}\) followed by pathology (path) images with \(\big{(}R=0.70,\ p=1.6\times 10^{-5}\big{)}\) and radiology (rad) images \(\big{(}R=0.71,\ p=9.6\times 10^{-6}\big{)}\). The survival prediction combined sample LGGs and HGGs (first row) patients se yielded with test-train split validation for rad-path images producing PCC of \(\big{(}R=0.81,\ p=2.2\times 10^{-16}\big{)}\) followed by pathology (path) images with \(\big{(}R=0.71,\ p=1.1\times 10^{-11}\big{)}\) and radiology (rad) images with \(\big{(}R=0.70,\ p=3.2\times 10^{-11}\big{)}\). The second column in Figure 2 presents the results of 5-fold cross-validation, the survival prediction of sample LGGs patients set (third row) yielded with 5-fold cross-validation for rad-path images producing PCC of \(\big{(}R=0.80,\ p=2.2\times 10^{-16}\big{)}\), followed by pathology (path) images with \(\big{(}R=0.74,\ p=2.2\times 10^{-16}\big{)}\) and radiology (rad) images \(\big{(}R=0.74,\ p=2.2\times 10^{-16}\big{)}\). The survival prediction of the sample HGGs patient's dataset (second row) yielded with 5-fold cross-validation for rad-path images producing PCC of \(\big{(}R=0.81,\ p=2.2\times 10^{-16}\big{)}\) followed by pathology (path) images with \(\big{(}R=0.69,\ p=5.6\times 10^{-12}\big{)}\) and radiology (rad) images \(\big{(}R=0.71,\ p=6\times 10^{-13}\big{)}\). The survival prediction of the sample LGGs and HGGs patients combined dataset first row) yielded with 5-fold cross-validation for rad-path images producing Pearson's correlation coefficient of \(\big{(}R=0.80,\ p=2.2\times 10^{-16}\big{)}\), followed by pathology (path) images with \(\big{(}R=0.74,\ p=2.2\times 10^{-16}\big{)}\) and radiology (rad) images \(\big{(}R=0.73,\ p=2.2\times 10^{-16}\big{)}\).