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ORIGINAL RESEARCH article

Front. Med.

Sec. Precision Medicine

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1659422

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

Provisionally accepted
Xingyuan  LiuXingyuan Liu1Ye  RuanYe Ruan1Siwei  CaoSiwei Cao1Mingming  ZhaoMingming Zhao1Zhongxing  ShiZhongxing Shi2Yantong  JinYantong Jin1Yang  WangYang Wang1Bo  GaoBo Gao1*
  • 1Departments of Radiology, Second Affiliated Hospital, Harbin Medical University, Harbin, China
  • 2Department of Interventional Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin, China

The final, formatted version of the article will be published soon.

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 1024 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.

Keywords: breast cancer, Radiomics, Sentinel lymph node, machine learning, Full-field digital mammography, information fusion

Received: 04 Jul 2025; Accepted: 22 Aug 2025.

Copyright: © 2025 Liu, Ruan, Cao, Zhao, Shi, Jin, Wang and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Bo Gao, Departments of Radiology, Second Affiliated Hospital, Harbin Medical University, Harbin, China

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