Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.

Background And Objectives: The aim of this study is to develop a radiomic and deep learning-based signature for survival analysis of patients with Non-Small Cell Lung Cancer.

Methods: Four-hundred twenty-two patients from "Lung1" dataset were included in the study. A 3D convolutional autoencoder (AE) was built and features from the latent space extracted for further analysis.