Academic radiology · 2026
Interpretable MRI-Based Machine Learning Model for Noninvasive Prediction of Axillary Lymph Node Metastasis After Neoadjuvant Chemotherapy in Breast Cancer.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Lai Xiaoyu, He Han, Liang Bo, Xu Zhifeng, Yang Lu, Wu Tingxi, Han Kaiting, Li Weiling, Liu Qing, Zhu Cuiling, Zhao Ruijun, Cai Gengxi, Dong Hongmei, Yang Yunjun
Аннотация
RATIONALE AND OBJECTIVES: Accurate prediction of axillary lymph node metastasis (ALNM) after neoadjuvant chemotherapy (NAC) remains challenging in breast cancer. This study aimed to develop an interpretable machine learning model integrating MRI-based radiomics, deep learning features, and the Node-RADS score for noninvasive ALNM prediction after NAC.
MATERIALS AND METHODS: In this multicenter retrospective study, 641 patients with pathologically confirmed breast cancer who underwent surgery between April 2017 and December 2024 across three institutions were enrolled. Preoperative dynamic contrast-enhanced MRI and clinicopathologic data were analyzed. Quantitative radiomics and ResNet50-derived deep learning features were extracted. Patients were divided into a training cohort (n = 397), an internal validation cohort (n = 99), and two external validation cohorts (n = 90 and n = 55). Three models-a clinical model, a deep learning-radiomics (DLR) model, and a combined clinical-deep learning-radiomics (CDLR) model-were constructed using five machine learning algorithms. Model performance was evaluated by ROC analysis, AUC, calibration, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to interpret feature importance.
RESULTS: The CDLR model demonstrated superior predictive performance, with AUCs of 0.879, 0.805, 0.737, and 0.781 in the training, internal, and two external cohorts, respectively, outperforming both the DLR and clinical models. The CDLR model also showed good calibration and the highest net clinical benefit. SHAP analysis identified Node-RADS, lbp_3D_m1_glcm_Correlation, and DL_50 as the most influential predictors.
CONCLUSION: The interpretable CDLR model enables accurate, noninvasive prediction of ALNM after NAC in breast cancer and may assist in individualized clinical decision-making.
MATERIALS AND METHODS: In this multicenter retrospective study, 641 patients with pathologically confirmed breast cancer who underwent surgery between April 2017 and December 2024 across three institutions were enrolled. Preoperative dynamic contrast-enhanced MRI and clinicopathologic data were analyzed. Quantitative radiomics and ResNet50-derived deep learning features were extracted. Patients were divided into a training cohort (n = 397), an internal validation cohort (n = 99), and two external validation cohorts (n = 90 and n = 55). Three models-a clinical model, a deep learning-radiomics (DLR) model, and a combined clinical-deep learning-radiomics (CDLR) model-were constructed using five machine learning algorithms. Model performance was evaluated by ROC analysis, AUC, calibration, and decision curve analysis. SHapley Additive exPlanations (SHAP) were used to interpret feature importance.
RESULTS: The CDLR model demonstrated superior predictive performance, with AUCs of 0.879, 0.805, 0.737, and 0.781 in the training, internal, and two external cohorts, respectively, outperforming both the DLR and clinical models. The CDLR model also showed good calibration and the highest net clinical benefit. SHAP analysis identified Node-RADS, lbp_3D_m1_glcm_Correlation, and DL_50 as the most influential predictors.
CONCLUSION: The interpretable CDLR model enables accurate, noninvasive prediction of ALNM after NAC in breast cancer and may assist in individualized clinical decision-making.