## 5 Conclusions and Future Directions  
In summary, we developed a radiopathomic signature to predict overall survival and stratification of patients with glioma. This signature proves enhanced performance in comparison to radiology and pathology-based features. Several possible future directions this study could take include evaluation of the methods in larger cohorts, and evaluation of sophisticated deep learning methods for prediction of overall survival, though such methods need considerably larger datasets. Even though the texture features used here provided strong survival predictions, future experiments would benefit from incorporating more advanced radiomic and shape features extracted in the vicinity of the tumor to \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Author (Year)** & **Patients** & **Dataset Type** & **Features** & **Machine Learning Model** & **Performance** \\ \hline
**Radiology.data** & & & & & \\ \hline [PERSON] et al. (2015) [6] & 129 & Radiology & Intensity, shape and location features from the tumor area. & Support vector classification & Accuracy = 80\% \\ \hline [PERSON] et al. (2020) [7] & NA & Radiology & First-order, intensity-based length, shape-based, and textural radiomic characteristics. & Neural Network & AUC = 0.57 \\ \hline [PERSON] et al. (2019) [8] & 285 + 191 & Radiology & Radiomic features from segmented tumor regions. & Random forest model & Accuracy = 61\% \\ \hline [PERSON] et al. (2020) [9] & NA & & Local feature extractor and global extractor utilizing the final layer of segmentation model. & Deep learning model & Accuracy = 65.5\% \\ \hline [PERSON] et al. (2022) [10] & 178 & Radiology & Shape and location features & Random forest classifier & AUC = 0.73 \\ \hline [PERSON] et al. (2021) [12] & 369+ 125+ 236 & Radiology & Intensity, texture, wavelet, shape and other radiological features from the tumor area, and CNN network based deep features. & Random forest regression model & RMSE= 311.5 \\ \hline [PERSON] et al. (2018) [36] & NA & Radiology & Intensity, shape, location and deep features. & Support vector regression & 57.1\% \\ \hline
**Gene expression data** & & & & & \\ \hline [PERSON] et al. (2020) [37] & 315 + 252 & Genetic expressions & 13,094 gene & Probabilistic programming & Accuracy = 74\% \\ \hline
**Pathology data** & & & No features were extracted. Images were given directly as input to the model. & Survival convolutional neural networks & c-index = 0.77 \\ \hline [PERSON] et al. (2019) [13] & 663 & Pathology & No features were extracted. Images were given directly as input to the model. & Deep neural network based & c-index = 0.82 \\ \hline
**Proposed model** & & & & & \\ \hline — & 171 & MRI+Pathology & Radiomic features extracted from MRI and pathology images. & Support vector regression & Correlation coefficient = 0.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed method with existing survival prediction techniques. emphasize the biologic nature of the gliomas. Along these lines, using machine learning methods to perform both a supervised and unsupervised selection of features may help the models to account for a possibly complex and nonlinear relation between radiographic and histopathologic features. This work may be further expanded upon by utilising numerous strategies, by using advance deep learning methodologies, by utilizing the combination of both imaging characteristics, and by using most discriminative histology and radiology features through employing histology and pathology correlation analysis. Various other features of both imaging sequences, which are not the part of this study, can be used with other machine learning techniques for optimum predictions. Neural networks can also use morphological, textural, and density of pixels-based features for the same prediction purpose. We also plan to use the machine learning models specifically developed for survival data in the future. F.A.R.