Cancer is among the leading causes of death worldwide. Thus, many ap- proaches have been made to enhance our understanding of the relationship be- tween patients and disease, especially by utilizing Deep Learning methods. This work provides a collection of modules that can be connected piece by piece to form a pipeline for continuous survival estimation on multi-view genetic data. We implement three neural networks that can make use of different methods to preprocess data, minimize dimensions by feature selection and integrate their multi-view aspect. More specifically, we will benchmark 36 different neural net- work settings on four scaling and three feature selection methods. Finally, we will evaluate performances across three distinct cancer types by analyzing key performance indicators, such as the c-index scores, hyperparameter importance, and time complexity.