International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2026
Transformer-based multimodal fusion model predicts lymph node metastasis in hepatic alveolar echinococcosis patients: A multicenter study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Zhou Yinshu, Tian Fengyuan, Zhao Mingyang, Feng Pengcai, Wang Jinpeng, Shangguan Chaoliang, Wang Xiangqian, Shang Changzhen, Zhu Haihong
Аннотация
OBJECTIVES: To develop a CT-based multimodal transformer model to precisely predict lymph node (LN) metastasis in hepatic alveolar echinococcosis (HAE) patients.
METHODS: A total of 318 HAE patients from three centers were allocated to a training set, an internal validation set, and two external validation sets. Radiomics, 3D deep learning (3DDL), and 2D deep learning (2DDL) features were retrieved from contrast-enhanced CT images of the hepatic hilar LN. Random forest models were constructed utilizing various features. Ultimately, we developed and assessed a transformer-based multimodal fusion model. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).
RESULTS: Compared with both the radiomics and the 2DDL models, the 3DDL model exhibited enhanced discriminative ability for LN status. The transformer model achieved the highest AUC (95%CI) of 0.951 (0.898-1.000), 0.927 (0.850-1.000), and 0.933 (0.847-1.000) for the three validation sets. DCA revealed that the transformer model produced the greatest net clinical advantage.
CONCLUSION: This study innovatively constructed a transformer-based multimodal fusion model, providing a practical and reliable tool for predicting LN metastasis in HAE patients. More importantly, this model provides a foundation for guiding LN dissection in HAE patients and is readily applicable in clinical settings.
METHODS: A total of 318 HAE patients from three centers were allocated to a training set, an internal validation set, and two external validation sets. Radiomics, 3D deep learning (3DDL), and 2D deep learning (2DDL) features were retrieved from contrast-enhanced CT images of the hepatic hilar LN. Random forest models were constructed utilizing various features. Ultimately, we developed and assessed a transformer-based multimodal fusion model. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and decision curve analysis (DCA).
RESULTS: Compared with both the radiomics and the 2DDL models, the 3DDL model exhibited enhanced discriminative ability for LN status. The transformer model achieved the highest AUC (95%CI) of 0.951 (0.898-1.000), 0.927 (0.850-1.000), and 0.933 (0.847-1.000) for the three validation sets. DCA revealed that the transformer model produced the greatest net clinical advantage.
CONCLUSION: This study innovatively constructed a transformer-based multimodal fusion model, providing a practical and reliable tool for predicting LN metastasis in HAE patients. More importantly, this model provides a foundation for guiding LN dissection in HAE patients and is readily applicable in clinical settings.