  Multiparametric radiological imaging is vital for detection, characterization
and diagnosis of many different diseases. The use of radiomics for quantitative
extraction of textural features from radiological imaging is increasing moving
towards clinical decision support. However, current methods in radiomics are
limited to using single images for the extraction of these textural features
and may limit the applicable scope of radiomics in different clinical settings.
Thus, in the current form, they are not capable of capturing the true
underlying tissue characteristics in high dimensional multiparametric imaging
space. To overcome this challenge, we have developed a multiparametric imaging
radiomic framework termed MPRAD for extraction of radiomic features from high
dimensional datasets. MPRAD was tested on two different organs and diseases;
breast cancer and cerebrovascular accidents in brain, commonly referred to as
stroke. The MPRAD framework classified malignant from benign breast lesions
with excellent sensitivity and specificity of 87% and 80.5% respectively with
an AUC of 0.88 providing a 9%-28% increase in AUC over single radiomic
parameters. More importantly, in breast, the glandular tissue MPRAD were
similar between each group with no significance differences. Similarly, the
MPRAD features in brain stroke demonstrated increased performance in
distinguishing the perfusion-diffusion mismatch compared to single parameter
radiomics and there were no differences within the white and gray matter
tissue. In conclusion, we have introduced the use of multiparametric radiomics
into a clinical setting
