[Research on a Deep Learning-Based Model for Predicting Malignancy Risk of Pan-Endocrine System across Thyroid and Breast Nodules].

Objective: To construct a cross-organ AI model based on multi-task deep learning (DL) to achieve unified prediction of malignancy risks of thyroid and breast nodules. Methods: =835), and SHAP analysis was used to explain model decisions. Results: 0.90). SHAP analysis revealed that edge irregularity, calcification type, and internal echo were common important features, while blood flow signals and TI-RADS/BI-RADS classification were organ-specific features. Conclusion: A high-performance cross-organ malignancy risk prediction model for thyroid-breast nodules was successfully constructed, confirming that pan-endocrine nodules can be precisely stratified for risk through a unified deep learning architecture, providing a new paradigm for AI-assisted diagnosis of endocrine tumors.