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已发表论文
多参数MRI深度学习影像组学用于个体化预测乳腺癌新辅助化疗后腋窝淋巴结反应
Authors Li S, Li R, Xu M, Liao J, Cao Z, Gong B, Yan F, Wang M, Tan H
Received 23 September 2025
Accepted for publication 30 December 2025
Published 21 January 2026 Volume 2026:18 568337
DOI https://doi.org/10.2147/BCTT.S568337
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Professor Pranela Rameshwar
Shunian Li,1,2,* Ruimin Li,3,* Mengting Xu,2,4 Jun Liao,1,2 Ziwei Cao,1,2 Bo Gong,4,5 Fengshan Yan,2 Meiyun Wang,1,2 Hongna Tan1,2
1Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 2Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, People’s Republic of China; 3Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 4Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, People’s Republic of China; 5Department of Radiology, The Second People’s Hospital of Henan Province, Zhengzhou, Henan, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hongna Tan, Department of Radiology,Henan Provincial People’s Hospital, No. 7 Wei Wu Road, Jinshui District, Zhengzhou, 450003, People’s Republic of China, Tel +86 13526766042, Email natan2000@126.com
Purpose: To develop a deep learning radiomics (DLR) model based on longitudinal multiparametric breast MRI to predict axillary lymph node (ALN) response following neoadjuvant therapy (NAT) in breast cancer patients.
Patients and Methods: This single-center retrospective study included 254 breast cancer patients who underwent NAT followed by surgery from January 2017 to October 2023. Pre- and post-NAT multiparametric MRI scans were analyzed to extract radiomics and deep learning features. The dataset was randomly divided into a training cohort (n = 144) and a validation cohort (n = 110). Feature selection was performed using the Mann–Whitney U-test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. Eight machine learning algorithms were compared, with logistic regression selected as the final classifier. Four models were constructed: clinical, radiomics, deep learning, and the DLR model. Performance was evaluated using ROC analysis, calibration curves, and decision curve analysis.
Results: Estrogen receptor status, HER2 status, and clinical T stage were independent predictors of axillary pathological complete response (apCR). The DLR model achieved the highest predictive performance, with AUCs of 0.939 (95% CI: 0.905– 0.974) in the training set and 0.856 (95% CI: 0.774– 0.938) in the validation set. DeLong tests showed that the DLR model outperformed only the clinical model (p < 0.0001). A bootstrap analysis (2000 iterations) further showed that the AUC difference between the training and validation cohorts was statistically significant (difference = 0.083; 95% CI: 0.0019– 0.1786; p = 0.043).
Conclusion: This study is among the first to integrate longitudinal multiparametric MRI with deep learning–based radiomics for predicting ALN response after NAT. The proposed DLR model may provide a noninvasive aid to individualized axillary decision-making, pending external validation.
Keywords: breast cancer, magnetic resonance imaging, deep learning, neoadjuvant therapy
1Department of Radiology, People’s Hospital of Zhengzhou University, Zhengzhou, Henan, People’s Republic of China; 2Department of Radiology, Henan Provincial People’s Hospital, Zhengzhou, Henan, People’s Republic of China; 3Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China; 4Department of Radiology, People’s Hospital of Henan University, Zhengzhou, Henan, People’s Republic of China; 5Department of Radiology, The Second People’s Hospital of Henan Province, Zhengzhou, Henan, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Hongna Tan, Department of Radiology,Henan Provincial People’s Hospital, No. 7 Wei Wu Road, Jinshui District, Zhengzhou, 450003, People’s Republic of China, Tel +86 13526766042, Email natan2000@126.com
Purpose: To develop a deep learning radiomics (DLR) model based on longitudinal multiparametric breast MRI to predict axillary lymph node (ALN) response following neoadjuvant therapy (NAT) in breast cancer patients.
Patients and Methods: This single-center retrospective study included 254 breast cancer patients who underwent NAT followed by surgery from January 2017 to October 2023. Pre- and post-NAT multiparametric MRI scans were analyzed to extract radiomics and deep learning features. The dataset was randomly divided into a training cohort (n = 144) and a validation cohort (n = 110). Feature selection was performed using the Mann–Whitney U-test, Spearman correlation analysis, and least absolute shrinkage and selection operator regression. Eight machine learning algorithms were compared, with logistic regression selected as the final classifier. Four models were constructed: clinical, radiomics, deep learning, and the DLR model. Performance was evaluated using ROC analysis, calibration curves, and decision curve analysis.
Results: Estrogen receptor status, HER2 status, and clinical T stage were independent predictors of axillary pathological complete response (apCR). The DLR model achieved the highest predictive performance, with AUCs of 0.939 (95% CI: 0.905– 0.974) in the training set and 0.856 (95% CI: 0.774– 0.938) in the validation set. DeLong tests showed that the DLR model outperformed only the clinical model (p < 0.0001). A bootstrap analysis (2000 iterations) further showed that the AUC difference between the training and validation cohorts was statistically significant (difference = 0.083; 95% CI: 0.0019– 0.1786; p = 0.043).
Conclusion: This study is among the first to integrate longitudinal multiparametric MRI with deep learning–based radiomics for predicting ALN response after NAT. The proposed DLR model may provide a noninvasive aid to individualized axillary decision-making, pending external validation.
Keywords: breast cancer, magnetic resonance imaging, deep learning, neoadjuvant therapy