  Nasopharyngeal Carcinoma (NPC) is a malignant epithelial cancer arising from
the nasopharynx. Survival prediction is a major concern for NPC patients, as it
provides early prognostic information to plan treatments. Recently, deep
survival models based on deep learning have demonstrated the potential to
outperform traditional radiomics-based survival prediction models. Deep
survival models usually use image patches covering the whole target regions
(e.g., nasopharynx for NPC) or containing only segmented tumor regions as the
input. However, the models using the whole target regions will also include
non-relevant background information, while the models using segmented tumor
regions will disregard potentially prognostic information existing out of
primary tumors (e.g., local lymph node metastasis and adjacent tissue
invasion). In this study, we propose a 3D end-to-end Deep Multi-Task Survival
model (DeepMTS) for joint survival prediction and tumor segmentation in
advanced NPC from pretreatment PET/CT. Our novelty is the introduction of a
hard-sharing segmentation backbone to guide the extraction of local features
related to the primary tumors, which reduces the interference from non-relevant
background information. In addition, we also introduce a cascaded survival
network to capture the prognostic information existing out of primary tumors
and further leverage the global tumor information (e.g., tumor size, shape, and
locations) derived from the segmentation backbone. Our experiments with two
clinical datasets demonstrate that our DeepMTS can consistently outperform
traditional radiomics-based survival prediction models and existing deep
survival models.
