## 3 Results
### Survival Analysis  
Based on the predictions of the proposed models by utilizing Kaplan-Meier survival curves, the predictions were computed for both short and long term survival. The performance was also computed in entire and validation data. The model accurately predict the long and short survivors, higher and lower actual survivors respectively. The trained models were applied on the test dataset set, and the scores predicted by the regression model were noted. A cutoff that maximized the survival difference was developed on the scores of training data to identify the relatively long and short survivors. This cutoff was then applied on the scores of the test dataset to determine long and short survivor groups and overall survival was then compared between the predicted long and short survivor groups using the log-rank test. Hazard ratios (HR) with 95% confidence intervals (CI) and Kaplan-Meier curves on the predicted groups were calculated with level of statistical significance for a two-sided comparison set at \(P<0.05\). The x-axis shows actual in days and y-axis shows predictions generated by proposed model.  
The model trained on rad-path features divided the patients of the replication set (configuration-III) into long and short survivor groups (HGGs + LGGs: \(HR=3.314\)\([95\%CI:1.718-6.394]\), \(log-rank(P)=2\times 10^{-4}\); HGGs: \(HR=2.172\)\([95\%CI:1.019-4.627]\), \(log-rank(P)=0.04\); LGGs: \(HR=5.028\)\([95\%CI:0.9728-25.99]\), \(log-rank(P)=0.04\)). Patients were also stratified into long and short survivor groups in configuration-I (HGGs + LGGs: \(HR=4.910\)\([95\%CI:3.176-7.593]\), \(log-rank(P)=2\times 10^{-14}\); HGGs: \(HR=5.197\)\([95\%CI:2.922-9.240]\), \(log-rank(P)=2\times 10^{-9}\); LGGs: \(HR=2.735[95\%CI:1.186-6.306]\), \(log-rank(P)=0.01\) and configuration-II (HGGs + LGGs: \(HR=7.118\)\([95\%CI:4.591-11.03]\), \(log-rank(P)<2\times 10^{-16}\); HGGs: \(HR=3.895\)\([95\%CI:2.317-6.547]\), \(log-rank(P)=6\times 10^{-8}\); LGGs: \(HR=8.155\)\([95\%CI:3.393-19.6]\), \(log-rank(P)=1\times 10^{-7}\)).  
Ensemble methods supply significant amounts of information, when the combination of both datasets and both joint rad-path groups are gathered collectively. Applying LOO cross-validation yielded a best correlation coefficient score of \(R=0.84\) on rad-path features, which is highest performance achieved in this work and is also consistent with very low \(p=2.2\times 10^{-16}\) in this experiment. While discussing the situation in which another LOOCV performed robustness for LGGs dataset includes only path images holding the highest value \(R=0.76\) and other minimum value that is \(p=2.2\times 10^{-16}\), the rad images utilized form LGGs dataset obtains the highest value \(R=0.74\) and minimum \(p=2.2\times 10^{-16}\), while performing 5-fold cross-validation as reflected in Figure 3. The alone HGGs dataset did not perform well throughout the work. The individual features are not sustainable enough for better results. The average performance is shown to increase using a LGGs and HGGs combined dataset for all of three groups: (i) radiology rad images (ii) pathology Path images, and (iii) rad-path images.  
The model evaluations in terms of MAE to predict survival is reflected in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline
**Configuration-I (Leave-One-Out)** & **Configuration-II (5-fold)** & **Configuration-III (Split-Train-Test)** \\ \hline High-grade gliomas \(\ast\) Low-grade gliomas & & \\ \hline Rad & 548.04 & 444.13 & 359.45 \\ Path & 407.15 & 479.83 & 338.60 \\ Rad-path & 399.32 & 379.66 & 259.19 \\ \hline \end{tabular}
\end{table}
Table 2: Quantitative evaluation of model in terms of mean absolute error (MAE) in predicting survival using rad, path, and rad-path feature sets in leave-one-out cross-validation, 5-fold cross-validation, and split-train-test at 60–40%. The results are separately shown for combined dataset (HGGs + LGGs) and separate cancer grades (HGGs and LGGs).