TY - JOUR AU - Court, Laurence E. AU - Fave, Xenia AU - Mackin, Dennis AU - Lee, Joonsang AU - Yang, Jinzhong AU - Zhang, Lifei PY - 2016 TI - Computational resources for radiomics JF - Translational Cancer Research; Vol 5, No 4 (August 08, 2016): Translational Cancer Research (Focused Issue: Radiomics in Radiation Oncology) Y2 - 2016 KW - N2 - Radiomics has the potential to individualize patient treatment by using images that are already being routinely acquired. Defined as the extraction of quantitative imaging features from clinical images for use in statistical models, radiomics has had success in a variety of tumor sites and imaging modalities. Researchers new to the field must start by choosing software to segment tumors [or other regions of interest (ROI)], extract quantitative image features, and analyze the results. This review describes the various software programs available for these tasks and gives examples of the use of these programs in radiomics research. UR - https://tcr.amegroups.org/article/view/8409