  Radiomics features extract quantitative information from medical images,
towards the derivation of biomarkers for clinical tasks, such as diagnosis,
prognosis, or treatment response assessment. Different image discretization
parameters (e.g. bin number or size), convolutional filters, segmentation
perturbation, or multi-modality fusion levels can be used to generate radiomics
features and ultimately signatures. Commonly, only one set of parameters is
used; resulting in only one value or flavour for a given RF. We propose tensor
radiomics (TR) where tensors of features calculated with multiple combinations
of parameters (i.e. flavours) are utilized to optimize the construction of
radiomics signatures. We present examples of TR as applied to PET/CT, MRI, and
CT imaging invoking machine learning or deep learning solutions, and
reproducibility analyses: (1) TR via varying bin sizes on CT images of lung
cancer and PET-CT images of head & neck cancer (HNC) for overall survival
prediction. A hybrid deep neural network, referred to as TR-Net, along with two
ML-based flavour fusion methods showed improved accuracy compared to regular
rediomics features. (2) TR built from different segmentation perturbations and
different bin sizes for classification of late-stage lung cancer response to
first-line immunotherapy using CT images. TR improved predicted patient
responses. (3) TR via multi-flavour generated radiomics features in MR imaging
showed improved reproducibility when compared to many single-flavour features.
(4) TR via multiple PET/CT fusions in HNC. Flavours were built from different
fusions using methods, such as Laplacian pyramids and wavelet transforms. TR
improved overall survival prediction. Our results suggest that the proposed TR
paradigm has the potential to improve performance capabilities in different
medical imaging tasks.
