to monitor tumor dynamic changes along the course of radiotherapy and to define sub volumes at risk for which dose escalation could be beneficial. Distinguishing true progression from radionecrosis Treatment effect or radiation necrosis after stereotactic radiosurgery (SRS) for brain metastases is a common phenomenon often indistinguishable from true progression. Radiomics demonstrated significant differences in a set of 82 treated lesions in 66 patients with pathological outcomes. Top-ranked Radiomic features feed into an optimized IsoSVM classifier resulted in a sensitivity and specificity of 65.38% and 86.67%, respectively, with an area under the curve of 0.81 on leave-one-out cross-validation. Only 73% of cases were classifiable by the neuroradiologist, with a sensitivity of 97% and specificity of 19%. These results show that radiomics holds promise for differentiating between treatment effect and true progression in brain metastases treated with SRS. Prediction of physiological events Radiomics can also be used to identify challenging physiological events such as brain activity, which is usually studied with imaging techniques such as functional MRI "fMRI". FMRI raw images can undergo radiomic analysis to generate imaging features that can be later correlated with meaningful brain activity. Multiparametric radiomics Multiparametric radiological imaging is vital for detection, characterization and diagnosis of many different diseases. 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. Recently, a Multiparametric imaging radiomic framework termed MPRAD for extraction of radiomic features from high dimensional datasets was developed. The Multiparametric Radiomics was tested on two different organs and diseases; breast cancer and cerebrovascular accidents in brain, commonly referred to as stroke. Breast cancer In breast cancer, 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. MPRAD provided a 9%-28% increase in AUC over single radiomic parameters. More importantly, in breast, normal glandular tissue MPRAD were similar between each group with no significance differences. Stroke 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. The majority of the single radiomic second order features (GLCM) did not show