  Outcome prediction is crucial for head and neck cancer patients as it can
provide prognostic information for early treatment planning. Radiomics methods
have been widely used for outcome prediction from medical images. However,
these methods are limited by their reliance on intractable manual segmentation
of tumor regions. Recently, deep learning methods have been proposed to perform
end-to-end outcome prediction so as to remove the reliance on manual
segmentation. Unfortunately, without segmentation masks, these methods will
take the whole image as input, such that makes them difficult to focus on tumor
regions and potentially unable to fully leverage the prognostic information
within the tumor regions. In this study, we propose a radiomics-enhanced deep
multi-task framework for outcome prediction from PET/CT images, in the context
of HEad and neCK TumOR segmentation and outcome prediction challenge (HECKTOR
2022). In our framework, our novelty is to incorporate radiomics as an
enhancement to our recently proposed Deep Multi-task Survival model (DeepMTS).
The DeepMTS jointly learns to predict the survival risk scores of patients and
the segmentation masks of tumor regions. Radiomics features are extracted from
the predicted tumor regions and combined with the predicted survival risk
scores for final outcome prediction, through which the prognostic information
in tumor regions can be further leveraged. Our method achieved a C-index of
0.681 on the testing set, placing the 2nd on the leaderboard with only 0.00068
lower in C-index than the 1st place.
