ArticleFrontiers in medicine2025
Development and internal validation of a mammography-based model fusing clinical, radiomics, and deep learning models for sentinel lymph node metastasis prediction in breast cancer.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To develop a mammography (MG)-based post-fusion model combined with Clinical, Radiomics, and Deep Learning Models to evaluate the status of sentinel lymph node (SLN) in patients with breast cancer. Methods: A total of 290 breast cancer patients who underwent MG were randomly divided into a training set ( Results: The post-fusion model Clinical+Rad+DL combined probabilities of single modal models, showed the best discrimination ability in the internal validation set (AUC [95%CI]: 0.845 [0.769-0.921]) and test set (AUC [95%CI]: 0.825 [0.812-0.932]). Conclusion: The proposed post-fusion model Clinical+Rad+DL demonstrated the method of probabilities fusion was effective and showed promise for predicting SLN metastasis in breast cancer.
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