Deep Learning Radiomics for Survival Prediction in Non-Small-Cell Lung Cancer Patients from CT Images.

Abstract

This study aims to apply a multi-modal approach of the deep learning method for survival prediction in patients with non-small-cell lung cancer (NSCLC) using CT-based radiomics. We utilized two public data sets from the Cancer Imaging Archive (TCIA) comprising NSCLC patients, 420 patients and 516 patients for Lung 1 training and Lung 2 testing, respectively. A 3D convolutional neural network (CNN) survival was applied to extract 256 deep-radiomics features for each patient from a CT scan. Feature selection steps are used to choose the radiomics signatures highly associated with overall survival. Deep-radiomics and traditional-radiomics signatures, and clinical parameters were fed into the DeepSurv neural network. The C-index was used to evaluate the model's effectiveness. In the Lung 1 training set, the model combining traditional-radiomics and deep-radiomics performs better than the single parameter models, and models that combine all three markers (traditional-radiomics, deep-radiomics, and clinical) are most effective with C-index 0.641 for Cox proportional hazards (Cox-PH) and 0.733 for DeepSurv approach. In the Lung 2 testing set, the model combining traditional-radiomics, deep-radiomics, and clinical obtained a C-index of 0.746 for Cox-PH and 0.751 for DeepSurv approach. The DeepSurv method improves the model's prediction compared to the Cox-PH, and models that combine all three parameters with the DeepSurv have the highest efficiency in training and testing data sets (C-index: 0.733 and 0.751, respectively). DeepSurv CT-based deep-radiomics method outperformed Cox-PH in survival prediction of patients with NSCLC patients. Models' efficiency is increased when combining multi parameters.