@article{TCR8409,
author = {Laurence E. Court and Xenia Fave and Dennis Mackin and Joonsang Lee and Jinzhong Yang and Lifei Zhang},
title = {Computational resources for radiomics},
journal = {Translational Cancer Research},
volume = {5},
number = {4},
year = {2016},
keywords = {},
abstract = {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.},
issn = {2219-6803}, url = {https://tcr.amegroups.org/article/view/8409}
}