Classification of endometrial carcinomas by histologic and morphologic features is not reproducible and imperfectly reflects tumor biology, especially in high-grade tumors. Several research teams have defined immunohistochemical and mutation profiles to aid in distinguishing endometrial cancer subtypes. Molecular data have also been used to further stratify risk categories, using gene expression profiling and copy number analysis to determine the risk of recurrence, even in apparent low-risk disease. But the extensive genomic characterization is not easy to be translated into clinical practice. Accumulating data showed both ultrasound and radiomic have a role in identifying patients at "high-risk". Extracting high-dimensional data from clinical ultrasonographic images, radiomic identifies the underlying pathophysiology of tumor tissue. Here, we plan to correlate cancer imaging features and gene expression, in order to categorize endometrial cancer patients into different classes of risk.
