Our objective is to study the prediction of the survival time of patients with metastatic melanoma in terms of 1-year survival as a binary classification. Dataset used in this study contains CTs of 71 patients with metastatic melanoma who are studied at Universite Clermont Auvergne Hospital. The number of lesions per patient varies from 1 to 11. To reach the objective, the survival time is anticipated using the accessible CT data as input of a 3D CNN. In this category, survival time is anticipated using full CT volumes and also from extracted 3D CT patches containing lesion regions. Here, the patches are extracted given the ground truth masks of the lesions. Moreover, segmentation of lesions coming from different organs is performed using two different 3D CNNs to examine the prediction of survival time based on newly extracted 3D CT patches. These new patches are extracted using our anticipated segmentation predicted masks of lesions. As the final test, it is also inspected whether aggregated deep segmentation feature map can help to predict survival time being an extra input channel to the CT data for 3D CNN or not. Our study shows that using aggregated deep segmentation feature map as an extra input channel to CT data comes about in superior performance in survival time prediction compared to using as it were only 3D CT patches as input. In expansion, the prediction of survival time based on newly extracted 3D CT patches coming from our segmentation predicted masks is similar to the survival prediction using the 3D CT patches coming from ground truth masks. Further investigation of this study can be the addition of radiomic features with aggregated deep segmentation feature map as additional input to CT data besides experimenting on bigger dataset. Including clinical data such as age, sex, etc. can as well play a vital role.