Related Experiment Video
Updated: Mar 6, 2026
Sentinel Lymph Node Mapping and Biopsy for Endometrial Cancer at Early Stage with Laparoscopy
Published on: August 19, 2021
Preoperative identification of deep myometrial invasion in endometrial cancer: a multicenter MRI study with a vision
Peijun Li1, Ximiao Wang2, Yuan Chen3
1Department of Medical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, China; Department of Radiology, Jiangmen Central Hospital, Jiangmen, China.
Objective:
To develop and validate a Vision Foundation Model-enhanced Multimodal Deep Learning Radiomics (VFM-MDLR) framework that integrates MR imaging with clinicopathological information for noninvasive prediction of deep myometrial invasion (DMI) in patients with endometrial cancer (EC).
Methods:
This retrospective, multicenter study was conducted across seven independent centers and included 1376 EC patients. We developed a VFM-MDLR model based on two MRI sequences for the prediction of DMI. The framework was designed to first employ a General Knowledge Transfer across Heterogeneous-model (GKTH) subnetwork, which adaptively extracts general representations from vision foundation model (VFM). Building on this foundation, a Cross-Sequence Guided Attention (CSGA) module was incorporated to exploit the complementary information between CE-T1WI and T2WI, thereby achieving semantic alignment and synergistic feature representation. These features were then used to derive a deep-learning signature, termed VFM-enhanced Dual-sequence Knowledge Fusion (VFM-DKF), which was further integrated with key clinicopathological variables to construct the final VFM-MDLR predictor. Model performance was systematically evaluated in the internal validation cohort and four external cohorts, while interpretability was assessed using Grad-CAM and SHAP analyses.
Results:
The VFM-MDLR model, which comprised age, histopathologic grade, the maximum tumor diameter (TMD), and the DLS, demonstrated the best predictive performance, with the highest AUC (0.832-0.877) across all cohorts. No significant difference was observed in performance for DMI detection between the VFM-MDLR model and experienced radiologists' readings (P = 0.915).
Conclusion:
The proposed VFM-MDLR model showed favorable accuracy in identifying DMI, potentially providing clinicians with a tool to facilitate individualized surgical treatment for patients with EC.
