  With a high rate of morbidity and mortality, colorectal cancer (CRC) ranks
third in mortality among cancers. By analyzing the texture properties of images
and quantifying the heterogeneity of tumors, radiomics and radiogenomics are
non-invasive methods to determine the biological properties of CRC. Recently,
several articles have discussed the application of radiomics in different
aspects of CRC. Therefore, given the large amount of data published, this
review aims to discuss how radiomics can be used for distinguishing benign and
malignant colorectal lesions, diagnosing, staging, predicting prognosis and
treatment response, and predicting lymph node and hepatic metastasis of CRC,
based on radiomic features extracted from magnetic resonance imaging (MRI),
computed tomography (CT), esophageal ultrasonography (EUS), and positron
emission tomography-CT (PET-CT). Challenges in bringing radiomics to clinical
application and future solutions have also been discussed. With the progress
made in radiomics and the application of deep and machine learning in this
area, radiomics can become one of the main tools for the personalized
management of CRC patients shortly.
