Gland surgery · 2026
Habitat-based ultrasound radiomics for preoperative prediction of axillary lymph node metastasis in breast cancer: a two-center study.
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Fang Zimeng, Yan Xiaoyu, Zhan Yunyun, Bi Yu, Wu Xiabi, Fang Mingdi, Zhang Yufan, Wang Jiajia, Peng Mei
Аннотация
BACKGROUND: Accurate preoperative prediction of axillary lymph node metastasis (ALNM) remains challenging. This study investigates whether habitat-based ultrasound radiomics of suspicious axillary lymph nodes can predict preoperative ALNM in patients with breast cancer.
METHODS: This retrospective two-center study included 416 breast cancer patients with suspicious axillary lymph nodes and pathologically confirmed nodal status. Patients from the Second Affiliated Hospital of Anhui Medical University were randomly assigned to the training and internal validation cohorts at a ratio of 7:3, whereas patients from Taihe County People's Hospital constituted the external test cohort. Suspicious lymph nodes were manually segmented. Pixel-level features were clustered using the K-means algorithm to divide each node into four habitat subregions. Radiomics features were extracted from the intranodal region of interest and each habitat subregion to construct the intranodal radiomics, individual habitat, and habitat radiomics models. A combined nomogram integrating the optimal radiomics signature with independent clinical predictors was developed.
RESULTS: Cortical thickness and lymph node vascular pattern were independent predictors of ALNM. The habitat radiomics model outperformed the intranodal radiomics and individual habitat models, with areas under the receiver operating characteristic curve (AUCs) of 0.859, 0.809, and 0.791 in the training, internal validation, and external test cohorts, respectively. The combined nomogram achieved the highest AUCs of 0.866, 0.819, and 0.806 in the corresponding cohorts, and its clinical utility was confirmed by decision curve analysis.
CONCLUSIONS: Habitat-based ultrasound radiomics improved ALNM prediction by capturing intranodal heterogeneity. The nomogram may provide a practical noninvasive tool for individualized preoperative assessment of suspicious axillary lymph nodes.
METHODS: This retrospective two-center study included 416 breast cancer patients with suspicious axillary lymph nodes and pathologically confirmed nodal status. Patients from the Second Affiliated Hospital of Anhui Medical University were randomly assigned to the training and internal validation cohorts at a ratio of 7:3, whereas patients from Taihe County People's Hospital constituted the external test cohort. Suspicious lymph nodes were manually segmented. Pixel-level features were clustered using the K-means algorithm to divide each node into four habitat subregions. Radiomics features were extracted from the intranodal region of interest and each habitat subregion to construct the intranodal radiomics, individual habitat, and habitat radiomics models. A combined nomogram integrating the optimal radiomics signature with independent clinical predictors was developed.
RESULTS: Cortical thickness and lymph node vascular pattern were independent predictors of ALNM. The habitat radiomics model outperformed the intranodal radiomics and individual habitat models, with areas under the receiver operating characteristic curve (AUCs) of 0.859, 0.809, and 0.791 in the training, internal validation, and external test cohorts, respectively. The combined nomogram achieved the highest AUCs of 0.866, 0.819, and 0.806 in the corresponding cohorts, and its clinical utility was confirmed by decision curve analysis.
CONCLUSIONS: Habitat-based ultrasound radiomics improved ALNM prediction by capturing intranodal heterogeneity. The nomogram may provide a practical noninvasive tool for individualized preoperative assessment of suspicious axillary lymph nodes.