Identification of intrinsic imaging phenotype for endometrial carcinoma based on multi-modality MRI using multi-Omics clustering algorithm

Motivation: Heterogeneity of endometrial carcinoma (EC) leads to differences in prognosis among different patients. The method of unsupervised machine learning can classify tumors into different subtypes by identifying heterogeneity and similarity in radiomics features, which may have the ability of preoperative risk stratification. Goal(s): To identify the intrinsic imaging phenotype for EC using multi-modality MR-based radiomics features. Approach: Ten multi-omics clustering methods were used for imaging phenotypes identification and reached a consensus. Results: Among the three identified imaging phenotypes, multiple pathological features and disease-free survival time showed significant differences. Impact: Based on multi-modality MRI using an unsupervised machine learning approach to classify EC into different imaging phenotypes, which were associated with clinicopathological features and prognosis, and can be used for preoperative risk stratification.