  Radiomics is an exciting new area of texture research for extracting
quantitative and morphological characteristics of pathological tissue. However,
to date, only single images have been used for texture analysis. We have
extended radiomic texture methods to use multiparametric (mp) data to get more
complete information from all the images. These mpRadiomic methods could
potentially provide a platform for stratification of tumor grade as well as
assessment of treatment response in brain tumors. In brain, multiparametric MRI
(mpMRI) are based on contrast enhanced T1-weighted imaging (T1WI), T2WI, Fluid
Attenuated Inversion Recovery (FLAIR), Diffusion Weighted Imaging (DWI) and
Perfusion Weighted Imaging (PWI). Therefore, we applied our multiparametric
radiomic framework (mpRadiomic) on 24 patients with brain tumors (8 grade II
and 16 grade IV). The mpRadiomic framework classified grade IV tumors from
grade II tumors with a sensitivity and specificity of 93% and 100%,
respectively, with an AUC of 0.95. For treatment response, the mpRadiomic
framework classified pseudo-progression from true-progression with an AUC of
0.93. In conclusion, the mpRadiomic analysis was able to effectively capture
the multiparametric brain MRI texture and could be used as potential biomarkers
for distinguishing grade IV from grade II tumors as well as determining
true-progression from pseudo-progression.
