Objective To construct a cross-organ AI model based on multi-task deep learning to achieve unified prediction of malignant risks of thyroid and breast nodules.

Methods Clinical data of patients with thyroid nodules (n=2386) and breast nodules (n=2753) from three hospitals were collected. A multi-task deep learning model is constructed based on the Transformer architecture, adopting a design that combines feature sharing layers with organ-specific layers. Performance was evaluated through five-fold cross-validation, tested on an independent external validation set (n=835), and SHAP analysis was used to explain model decisions.

Results The constructed AI model for generalized endocrine nodules achieved an AUC of 0.932(95%CI:0.914-0.951) in the prediction of thyroid nodules, with a sensitivity of 86.5% and a specificity of 89.2%. The predictive AUC of breast nodules was 0.917(95%CI:0.896-0.938), with a sensitivity of 84.3% and a specificity of 88.7%. Compared with the single-organ model, the pan-model performed better on small sample datasets (P<0.01), and remained stable in external validation (AUC>0.90). SHAP analysis revealed that edge irregularity, calcification type, and internal echo were common important features, while blood flow signals and TIRADS/BI-RADS classification were organ-specific features.

Conclusion A high-performance cross-organ malignant 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.