Objectives Our objective is to develop a deep learning-based artificial intelligence (AI) model capable of analyzing digital mammography (DM) images to predict axillary lymph node (ALN) status subsequent to neoadjuvant therapy (NAT) in breast cancer patients. Materials and methods We developed and validated an AI model for predicting post-NAT ALN status using images and clinical data of 956 invasive non-specific breast cancer patients with positive ALN metastasis from three medical centers. During development, four image cropping methods and five backbone networks were compared for classification architecture construction. The AI model was evaluated via internal and external test sets, with performance assessed using the ROC curve and AUC. Results Experiments showed that the AI model using “fixed 5 cm” image clipping and Swin Transformer V2 as the backbone feature extraction network for primary image processing achieved the best ALN status prediction performance. Compared with merely inputting the primary lesion, adding the pre-training model and clinical features further improved the prediction performance of the AI model, in the training set (AUC = 0.823, 95% CI: 0.797–0.846, p < 0.001), internal validation set (AUC = 0.774, 95% CI: 0.722–0.818, p < 0.001), internal test set (AUC = 0.778, 95% CI: 0.739–0.813, p = 0.034) and external test set (AUC = 0.756, 95% CI: 0.700–0.805, p = 0.013). After inputting primary and auxiliary region images and clinical features into the AI model, the AUC value was further improved, reaching above 0.8 in all four datasets. Conclusion This study constructed an AI model based on baseline DM images that demonstrates good performance in predicting ALN status in breast cancer patients after NAT, providing decision support to avoid excessive surgery. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Due to the lack of reliable methods to accurately judge the status of ALNs in breast cancer patients after NAT, some patients are overtreated. <Emphasis Type="BoldItalic">Findings</Emphasis> The AI model we constructed based on the primary lesion of DM before NAT can predict the status of ALNs accurately after NAT. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> The AI model can predict the status of ALNs after NAT, which may help clinical selection of more beneficial treatment modalities. Graphical Abstract

Mammography-based artificial intelligence model for predicting axillary lymph node status after neoadjuvant therapy in breast cancer

期刊论文
发表日期：

2026年4月18日

查看全文

Keyu Mao,
Zheren Li,
Jun Li,
Yu Xie,
Man Long,
Jing Ai,
Lichi Zhang,
Depei Gao,
Dinggang Shen,
Pinxiong Li,
Zhenhui Li

摘要

Objectives

Our objective is to develop a deep learning-based artificial intelligence (AI) model capable of analyzing digital mammography (DM) images to predict axillary lymph node (ALN) status subsequent to neoadjuvant therapy (NAT) in breast cancer patients.

Materials and methods

We developed and validated an AI model for predicting post-NAT ALN status using images and clinical data of 956 invasive non-specific breast cancer patients with positive ALN metastasis from three medical centers. During development, four image cropping methods and five backbone networks were compared for classification architecture construction. The AI model was evaluated via internal and external test sets, with performance assessed using the ROC curve and AUC.

Results

Experiments showed that the AI model using “fixed 5 cm” image clipping and Swin Transformer V2 as the backbone feature extraction network for primary image processing achieved the best ALN status prediction performance. Compared with merely inputting the primary lesion, adding the pre-training model and clinical features further improved the prediction performance of the AI model, in the training set (AUC = 0.823, 95% CI: 0.797–0.846, p < 0.001), internal validation set (AUC = 0.774, 95% CI: 0.722–0.818, p < 0.001), internal test set (AUC = 0.778, 95% CI: 0.739–0.813, p = 0.034) and external test set (AUC = 0.756, 95% CI: 0.700–0.805, p = 0.013). After inputting primary and auxiliary region images and clinical features into the AI model, the AUC value was further improved, reaching above 0.8 in all four datasets.

Conclusion

This study constructed an AI model based on baseline DM images that demonstrates good performance in predicting ALN status in breast cancer patients after NAT, providing decision support to avoid excessive surgery.

Key Points

Question Due to the lack of reliable methods to accurately judge the status of ALNs in breast cancer patients after NAT, some patients are overtreated.

Findings The AI model we constructed based on the primary lesion of DM before NAT can predict the status of ALNs accurately after NAT.

Clinical relevance The AI model can predict the status of ALNs after NAT, which may help clinical selection of more beneficial treatment modalities.

Graphical Abstract