## 2 Materials and Methods  
### Dataset  
The dataset utilized in this study comprises 171 glioma patients (gender: 91 male and 80 female, tumor grade: 96 LGG and 75 HGG) collected from The Cancer Image Archive (TCIA) and The Cancer Genomic Atlas (TCGA) repository. The data comprised (1) preoperative MRI images, including T1 post contrast gadolinium (T1-GD), T1-weighted (T1), T2-weighted (T2), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR), (2) digital pathology images collected from paraffin-embedded H&E-stained tissue sections, and (3) survival information. Survival outcome was collected from medical records and was defined as the time from the initial presentation of the disease to death or last follow-up time, whatever happened earlier. In our study, some patients' data was censored because the last follow-up interval preceded the end of the study as well as the death. As such, the survival time can be stated conclusively and is just known to be longer than the latest follow-up. Table 1 provides more details on the dataset. Figure 1 provides a schematic overview of the proposed ensemble regression-based survival prediction method. Several pre-processing steps are applied both on MRI and digital pathology images. The MRI images were smoothed using the adaptive nonlocal means algorithm [7; 8; 9] followed by the registration of T1, T2, and T2-FLAIR images to T1-CE images using the FLIRT registration method [10], and brain extraction tool was used using skull stripping. The N4 biased field correction method [11] was also applied on the images to remove intensity inhomogeneities happening because of image acquisition. The digital pathology images were converted to grayscale and were calibrated to a reference digital pathology image to correct for color inconsistencies in images acquired across multiple institutions. Feature extraction methods were then applied on the pre-processed images to quantify the imaging profiles of these glioma patients into meaningful quantitative measures. In this study, features extracting techniques, such as GLCM, GLSZM, GLRLM, LBP, and NGTDM, are used to extract relevant information from rad and path images. The \begin{table}
\begin{tabular}{c c c c c} \hline \hline _Genetic, Clinical Characteristics_ & _Complete Set_ & _Training Group_ & _Testing Group_ & _p-Value_ \\ \multicolumn{2}{c}{_} & _(n = 171)_ & _(n = 103, \(\sim\)60\%)_ & _(n = 68, \(\sim\)40\%)_ & _p-Value_ \\ \hline Median overall survival & 467 & 467 & 489.5 & 0.999 \\ No. of uncensored patients & 104 & 63 & 39 & 0.619 \\ Age & 51.22 \(\pm\) 15.35 & 52.50 \(\pm\) 15.66 & 49.27 \(\pm\) 14.76 & 0.436 \\ Sex & & & & 0.493 \\ Male & 91 & 57 & 34 & \\ Female & 80 & 46 & 34 & \\ Tumor grade & & & & 0.956 \\ High grade glioma & 75 & 45 & 30 & \\ Low grade glioma & 96 & 58 & 38 & \\ IDH Mutation & & & & 0.691 \\ Wildtype & 83 & 50 & 33 & \\ Mutant & 77 & 44 & 33 & \\ NA & 11 & 9 & 2 & \\
1p19q mutation & & & & 0.887 \\ Wildtype & 53 & 30 & 23 & \\ Mutant & 24 & 14 & 10 & \\ NA & 94 & 59 & 35 & \\ \hline \hline \end{tabular} Note. – Except where otherwise noted, data are numbers of patients with percentages in parentheses. \(p\) values were computed by employing Student’s t-test for continuous variables and \(\chi\)2 test for computing categorical variables. Percentage for 1p/19q computed by utilizing only number of patients having 1p/19q status available. IDH = isocitrate dehydrogenase, 1p/19q = deletion of chromosome arm 1p and/or 19q, NA = not applicable.  
\end{table} 
Table 1: Glioma patients’ characteristics. final step of the pipeline involves an ensemble of various regression techniques to predict survival outcome of glioma patients. These features were given as input to the final step of the pipeline, which is an ensemble of various regression techniques, including support vector regression (SVR), SVR with RBF kernel and linear kernel, AdaBoost, gradient boost, and random forest, for prediction of survival outcome of glioma patients.