Fig. 1

Pipeline of the general processing steps for radiomic studies. The flowchart presented the major processing steps needed for analysis of radiomic features from MRI in glioma. After skull stripping and artifact removal (bias field, noise, etc.), acquired MRI images are subjected to standardization and segmentation to extract regions of interests (ROIs). Radiomic features are then extracted from the image masks of ROIs via conventional radiomics or deep-learning approaches. After selecting relevant features, advanced statistical analysis is performed to classify and correlate radiomic features, involving machine/deep-learning methods for feature selection, classification, and cross-validation. Finally, endpoints are predicted to evaluate the models, such as patient’s survival, genomics, response to therapy, subsequent location of recurrence, or tumor micro-environment