Preoperative identification of deep myometrial invasion in endometrial cancer: a multicenter MRI study with a vision foundation model-enhanced multimodal deep learning framework.

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.