  Deep Learning-based Radiomics (DLR) has achieved great success on medical
image analysis. In this study, we aim to explore the capability of DLR for
survival prediction in NPC. We developed an end-to-end multi-modality DLR model
using pretreatment PET/CT images to predict 5-year Progression-Free Survival
(PFS) in advanced NPC. A total of 170 patients with pathological confirmed
advanced NPC (TNM stage III or IVa) were enrolled in this study. A 3D
Convolutional Neural Network (CNN), with two branches to process PET and CT
separately, was optimized to extract deep features from pretreatment
multi-modality PET/CT images and use the derived features to predict the
probability of 5-year PFS. Optionally, TNM stage, as a high-level clinical
feature, can be integrated into our DLR model to further improve prognostic
performance. For a comparison between CR and DLR, 1456 handcrafted features
were extracted, and three top CR methods were selected as benchmarks from 54
combinations of 6 feature selection methods and 9 classification methods.
Compared to the three CR methods, our multi-modality DLR models using both PET
and CT, with or without TNM stage (named PCT or PC model), resulted in the
highest prognostic performance. Furthermore, the multi-modality PCT model
outperformed single-modality DLR models using only PET and TNM stage (PT model)
or only CT and TNM stage (CT model). Our study identified potential
radiomics-based prognostic model for survival prediction in advanced NPC, and
suggests that DLR could serve as a tool for aiding in cancer management.
