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

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 ( n = 203) and an internal validation set ( n = 87), with an additional 82 patients included in the test set for independent validation. From the MG images of mediolateral oblique (MLO) and craniocaudal (CC) views, 1726 radiomic (Rad) features and 1,024 deep learning (DL) features were extracted for each patient. After the feature fusion and selection, the single-modal models and pre-fusion models were established by stochastic gradient descent (SGD). Using the probabilities of single-modal models, the post-fusion models were developed by support vector machine (SVM). The area under the receiver operating characteristic curve (AUC) was used for accessing the performance of models. The clinical net benefit and predictive accuracy were evaluated through decision curve analysis (DCA) and calibration curves. 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.