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已发表论文
新辅助化疗后淋巴结转移的乳腺癌患者预后预测的机器学习模型的开发与验证
Authors Fan Y, Jin Y, Tian C, Zhang Y, Zhang C, Yu H , Liu S
Received 29 April 2025
Accepted for publication 19 September 2025
Published 30 September 2025 Volume 2025:17 Pages 883—896
DOI https://doi.org/10.2147/BCTT.S534964
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Pranela Rameshwar
Yanjia Fan,1,* Yudi Jin,2,* Cheng Tian,1 Yu Zhang,1 Chi Zhang,1 Haochen Yu,1 Shengchun Liu1
1Department of Breast and Thyroid Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Shengchun Liu, Department of Breast and Thyroid Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China, Email liushengchun1968@163.com
Background: Lymph node (LN) status is a critical prognostic factor for breast cancer patients undergoing neoadjuvant chemotherapy (NAC). This study aims to develop and validate machine learning models to predict LN response in breast cancer patients with LN metastases.
Methods: Breast cancer patients who received NAC in our hospital were retrospectively analyzed. Clinicopathological data, and MRI imaging were collected. Patients were randomly divided into a training set and a testing set in 7:3 ratio. Radiomic features were extracted from pre-treatment imaging. Random forests and logistic regression were employed alongside Clinical, Clinical-Radiomics and Clinical-Deep-learning-radiomics (Clinical-DLR) in training set. Model performance was evaluated using metrics including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), accuracy and F1-score. Finally, patients were divided into high-risk and low-risk groups according to the model with the best performance.
Results: Overall, 447 patients were enrolled. In the Clinical, Clinical-Radiomics, and Clinical-DLR logistic regression models, the AUC values in the testing set were 0.738, 0.798, and 0.911, respectively. For the random forest models, the AUC values in the testing set were 0.754, 0.801, and 0.921, respectively. Based on the predictions from the Clinical-DLR model, patients can be stratified into high-risk and low-risk groups. The survival outcomes for high-risk patients were significantly worse compared to those of low-risk patients.
Conclusion: The deep learning radiomics offers a promising approach to predict LN status and survival outcome in breast cancer patients undergoing NAC. This could facilitate personalized treatment strategies and improve clinical decision-making.
Keywords: breast cancer, lymph nodes metastasis, pathological complete response, radiomics, deep learning
1Department of Breast and Thyroid Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Shengchun Liu, Department of Breast and Thyroid Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China, Email liushengchun1968@163.com
Background: Lymph node (LN) status is a critical prognostic factor for breast cancer patients undergoing neoadjuvant chemotherapy (NAC). This study aims to develop and validate machine learning models to predict LN response in breast cancer patients with LN metastases.
Methods: Breast cancer patients who received NAC in our hospital were retrospectively analyzed. Clinicopathological data, and MRI imaging were collected. Patients were randomly divided into a training set and a testing set in 7:3 ratio. Radiomic features were extracted from pre-treatment imaging. Random forests and logistic regression were employed alongside Clinical, Clinical-Radiomics and Clinical-Deep-learning-radiomics (Clinical-DLR) in training set. Model performance was evaluated using metrics including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), accuracy and F1-score. Finally, patients were divided into high-risk and low-risk groups according to the model with the best performance.
Results: Overall, 447 patients were enrolled. In the Clinical, Clinical-Radiomics, and Clinical-DLR logistic regression models, the AUC values in the testing set were 0.738, 0.798, and 0.911, respectively. For the random forest models, the AUC values in the testing set were 0.754, 0.801, and 0.921, respectively. Based on the predictions from the Clinical-DLR model, patients can be stratified into high-risk and low-risk groups. The survival outcomes for high-risk patients were significantly worse compared to those of low-risk patients.
Conclusion: The deep learning radiomics offers a promising approach to predict LN status and survival outcome in breast cancer patients undergoing NAC. This could facilitate personalized treatment strategies and improve clinical decision-making.
Keywords: breast cancer, lymph nodes metastasis, pathological complete response, radiomics, deep learning