Multimodal MRI-Based Radiomics Combining 3D Deep Transfer Learning for Predicting Cervical Stromal Invasion in Endometrial Carcinoma

Motivation: Cervical stromal invasion (CSI) plays a critical role in distinguishing between stage I and II endometrial carcinoma (EC) and serves as a key prognostic indicator. Goal(s): Assisting clinicians in achieving precise preoperative treatment and prognostic assessments. Approach: This study constructed innovative machine learning models that merge radiomics and 3D deep transfer learning to preoperatively and non-invasively predict CSI. Results: Novel machine learning model has significant superiority over radiologists for preoperative prediction of CSI. Impact: Constructing a non-invasive preoperative prediction model to increase the diagnostic accuracy of CSI, makes up for the limitations of traditional imaging observation in the assessment of CSI and subsequently directs clinicians in preoperative precise treatment and prognostic evaluation.