Pretreatment MRI and Ultrasound Features of the Primary Tumor and Axillary Nodes for Predicting Axillary Pathologic Complete Response After Neoadjuvant Chemotherapy: A Cross-Anatomic Comparison

Background/Objectives: Pretreatment imaging may help estimate axillary response after neoadjuvant chemotherapy (NAC) in clinically node-positive (cN+) breast cancer. This study compared routinely available magnetic resonance imaging (MRI) and ultrasound (US) descriptors from the primary tumor and axillary nodes for predicting axillary nodal response, defined as postneoadjuvant pathologic node-negative status (ypN0). Methods: This retrospective multicenter study included 243 patients from three centers: 146 in the training cohort, 59 in the internal validation cohort, and 38 in the external validation cohort. The index breast tumor and axillary node were identified by a senior radiologist, and two radiologists independently assessed structured descriptors based on BI-RADS 2025. Least absolute shrinkage and selection operator regression and logistic regression were used to develop imaging-only, clinical-only, and clinicoradiologic models. Performance was evaluated using area under the curve (AUC), calibration, decision curve analysis, net reclassification improvement, and integrated discrimination improvement. Results: In the internal and external validation cohorts, the trimodal imaging model achieved AUCs of 0.761 and 0.806, and the clinicoradiologic model achieved the highest AUCs of 0.858 and 0.931, respectively. In pooled validation subgroup analyses, AUCs were 0.928, 0.804, and 0.700 for HR-positive/HER2-negative, HER2-positive, and triple-negative tumors, and 0.898 and 0.880 for cN1 and cN2–3 disease, respectively. Conclusions: MRI tumor descriptors provided the strongest imaging signal for ypN0 prediction, while US descriptors offered modest complementary information. The clinicoradiologic model may support pretreatment risk stratification and multidisciplinary planning for post-NAC axillary reassessment, but should not independently determine sentinel lymph node biopsy, axillary lymph node dissection, or omission of axillary staging.

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Journal
Diagnostics
Published
2026-09-04
DOI
https://doi.org/10.3390/diagnostics16172853
Primary Topic
Breast Cancer Treatment Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

Pretreatment MRI and Ultrasound Features of the Primary Tumor and Axillary Nodes for Predicting Axillary Pathologic Complete Response After Neoadjuvant Chemotherapy: A Cross-Anatomic Comparison

Weifeng Liu, 孔庆聪, Jingxuan Guo, Wenjie Tang et al.
Diagnostics
Breast Cancer Treatment Studies
article

Pretreatment MRI and Ultrasound Features of the Primary Tumor and Axillary Nodes for Predicting Axillary Pathologic Complete Response After Neoadjuvant Chemotherapy: A Cross-Anatomic Comparison

Weifeng Liu, 孔庆聪, Jingxuan Guo, Wenjie Tang, Jiaxin Chen, Liwen Pan, Yonghui Feng, Yongxin Chen, Shuang Liu, Xinqing Jiang
article en

Abstract

Background/Objectives: Pretreatment imaging may help estimate axillary response after neoadjuvant chemotherapy (NAC) in clinically node-positive (cN+) breast cancer. This study compared routinely available magnetic resonance imaging (MRI) and ultrasound (US) descriptors from the primary tumor and axillary nodes for predicting axillary nodal response, defined as postneoadjuvant pathologic node-negative status (ypN0). Methods: This retrospective multicenter study included 243 patients from three centers: 146 in the training cohort, 59 in the internal validation cohort, and 38 in the external validation cohort. The index breast tumor and axillary node were identified by a senior radiologist, and two radiologists independently assessed structured descriptors based on BI-RADS 2025. Least absolute shrinkage and selection operator regression and logistic regression were used to develop imaging-only, clinical-only, and clinicoradiologic models. Performance was evaluated using area under the curve (AUC), calibration, decision curve analysis, net reclassification improvement, and integrated discrimination improvement. Results: In the internal and external validation cohorts, the trimodal imaging model achieved AUCs of 0.761 and 0.806, and the clinicoradiologic model achieved the highest AUCs of 0.858 and 0.931, respectively. In pooled validation subgroup analyses, AUCs were 0.928, 0.804, and 0.700 for HR-positive/HER2-negative, HER2-positive, and triple-negative tumors, and 0.898 and 0.880 for cN1 and cN2–3 disease, respectively. Conclusions: MRI tumor descriptors provided the strongest imaging signal for ypN0 prediction, while US descriptors offered modest complementary information. The clinicoradiologic model may support pretreatment risk stratification and multidisciplinary planning for post-NAC axillary reassessment, but should not independently determine sentinel lymph node biopsy, axillary lymph node dissection, or omission of axillary staging.

DiagnosticsVol. 16(17)
Sun Yat-sen University (CN), Panyu Hospital of Chinese Medicine (CN), Guangdong Province Women and Children Hospital (CN), Third Affiliated Hospital of Sun Yat-sen University (CN), Tianjin Medical University (CN), South China University of Technology (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 14%
Breast Cancer Treatment Studies
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