## 6 Conclusions  
In this manuscript, we propose a radiomics approach to predict the Overall Survival in a cohort of 97 patients suffering from locally advanced non-small cell lung cancer. NSCLC has been already studied in few other works and, differently from the literature, here we introduce two novel aspects which have not been analysed in this way so far. The first is the analysis of three different VOIs commonly used in clinical practice and segmented on CT images routinely collected. The second, but not secondary, is the introduction of 3D Local Binary Pattern descriptors, already used in computer vision problems. The both constitute novel and challenging directions of this research. Among the different combinations of classification algorithms and VOIs, the best accuracy of proposed approach is equal to 83.51%, achieved using the Clinical Target Volume, the Adaboost learner, and nine features selected from the pool. The fact that the VOI providing the best results is the CTV confirms our initial hypothesis that there is information in the surroundings of the visible tumour and that this is important to predict patients' OS. Furthermore, the LBPs features proved to be determinant, improving the classification performance in comparison with respect to other texture measures. The promising results described so far suggest three future directions worthy of investigation. First, the system performance and its variability could be explored for small variations of the segmentations of the target tumour volume, considering not only the GTV, but also the CTV and PTV. This will permit us to assess how important it is to get a very accurate automatic segmentation. Second, we could further explore the potential of LBPs, validating such descriptors on an external dataset, so to confirm their stability and effectiveness. Third, we are working to enlarge the size of the dataset and to extend the research in the deep learning area, even when considering deep approaches to artificially enlarge the size of the training set, such as the Generative Adversarial Networks. Conceptualization: E.C., R.S., S.R. and P.S. ; Methodology, E.C., N.C.D., L.T., R.S. and P.S. ; Software, E.C., N.C.D., L.T., R.S. and P.S. ; Validation, E.C., N.C.D., L.T., R.S. and P.S. ; Formal analysis, E.C., N.C.D., L.T., R.S. and P.S. ; Resources, C.G., M.F., A.C. and S.R. ; Data Curation, C.G., M.F., A.C. and S.R. ; Writing--original draft preparation, N.C.D., R.S., E.C., L.T., and P.S. ; Writing--review and editing, N.C.D., R.S., E., L.T., C.G., M.F., A.C., G.I., S.R., and P.S. ; Visualization, E.C., N.C.D., L.T., R.S. and P.S. ; Supervision, S.R., G.I. and P.S. ; Funding acquisition, S.R. and P.S. All authors have read and agreed to the published version of the manuscript. This work was partially founded by Universita' Campus Bio-Medico di Roma under the programme \"University Strategic Projects 2018 call\" within the project \"a CoLLaborative multi-sources Radiopathomics approach for personalised Oncology in non-small cell lung cancer (CLARO)\". \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**ResNet50 features**} & \multicolumn{3}{c}{**AlexNet features**} \\ \cline{2-9}  & **AdaBoost** & **DT** & **RF** & **XGB** & **AdaBoost** & **DT** & **RF** & **XGB** \\ \hline Accuracy & **73.33** & 66.67 & 70.0 & 71.11 & 63.33 & 66.67 & 63.33 & 70.0 \\ \hline AUC & **73.42** & 66.08 & 69.5 & 71.17 & 63.08 & 67.67 & 64.00 & 70.67 \\ \hline Precision & **72.81** & 68.02 & 68.12 & 69.94 & 63.86 & 70.52 & 63.89 & 72.16 \\ \hline Recall & **85.67** & 82.17 & 89.67 & 89.67 & 78.17 & 79.00 & 89.17 & 89.17 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of the deep features extracted with two state-of-the-art deep neural networks. The results are computed evaluating the four classification algorithms that were used in the proposed pipeline in 10-fold cross validation. The largest performance is highlighted in bold.