Keywords: Diagnosis/Prediction, Cancer
Motivation: Endometrial cancer（EC） is a common malignant tumor of the female reproductive system, and the incidence is still rising.
Goal(s): To develop a machine learning model to noninvasively assess microsatellite instability（MSI） in endometrial cancer.
Approach: Different machine learning models were constructed based on T2WI, DWI, arterial phase, equilibrium phase images as well as clinical and pathological data, and then the model with the best performance was selected.
Results: The predictive efficacy of the combined model Nomogram is comparable to that of the multimodal imaging omics model, and both have higher clinical decision benefits.
Impact: These machine learning models can perform non-invasive MSI status assessment for patients who are not suitable for surgery, help clinical better assess patient prognosis, evaluate the feasibility of immunotherapy in advanced patients, and provide clinical decision support.
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