MDR-SURV: A Multi-Scale Deep Learning-Based Radiomics for Survival Prediction in Pulmonary Malignancies

Abstract

Predicting death in lung cancer patients before initiating treatment is of paramount importance as this may guide decision-making towards more aggressive or combination of different types of treatment. In this work, we propose a Multi-scale Deep learning-based Radiomics model, referred to as "MDR-SURV" that exploits the information from positron emission tomography/computed tomography (PET/CT) images, combined with other clinical factors, to predict the overall survival (OS). Deep learning-based radiomics has the advantage of learning what features to extract, on its own. Furthermore, it does not require the exact segmentation of the tumor. The proposed MDR-SURV, which is a multi-scale framework, incorporates the tumor region and its surroundings, from different scales, and can extract both local and global tumor features. PET/CT images of 132 lung cancer patients who underwent stereotactic body radiotherapy (SBRT) were used to predict OS with the proposed model. Our results show that the MDR-SURV model outperforms its single-scale counterparts in predicting OS. Furthermore, the proposed MDR-SURV model achieves significantly high concordance index (C-index) of 73% in predicting the OS, which is noticeably higher than the results reported in existing literature.