  Radiomics is a rapidly growing field that deals with modeling the textural
information present in the different tissues of interest for clinical decision
support. However, the process of generating radiomic images is computationally
very expensive and could take substantial time per radiological image for
certain higher order features, such as, gray-level co-occurrence matrix(GLCM),
even with high-end GPUs. To that end, we developed RadSynth, a deep
convolutional neural network(CNN) model, to efficiently generate radiomic
images. RadSynth was tested on a breast cancer patient cohort of twenty-four
patients(ten benign, ten malignant and four normal) for computation of GLCM
entropy images from post-contrast DCE-MRI. RadSynth produced excellent
synthetic entropy images compared to traditional GLCM entropy images. The
average percentage difference and correlation between the two techniques were
0.07 $\pm$ 0.06 and 0.97, respectively. In conclusion, RadSynth presents a new
powerful tool for fast computation and visualization of the textural
information present in the radiological images.
