Ultrasound-based deep learning radiomics for enhanced axillary lymph node metastasis assessment: a multicenter study

作者：Di Zhang, Wang Zhou, Wenwu Lu, Xiachuan Qin, Xian‐Ya Zhang, Yanhong Luo, Jun Wu, Junli Wang, Junjie Zhao, Chaoxue Zhang · 发表于：The Oncologist · 年份：2025 · DOI：10.1093/oncolo/oyaf090 · 被引用次数：10 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Breast Cancer Treatment Studies、AI in cancer detection

BACKGROUND: Accurate preoperative assessment of axillary lymph node metastasis (ALNM) in breast cancer is crucial for guiding treatment decisions. This study aimed to develop a deep-learning radiomics model for assessing ALNM and to evaluate its impact on radiologists' diagnostic accuracy. METHODS: This multicenter study included 866 breast cancer patients from 6 hospitals. The data were categorized into training, internal test, external test, and prospective test sets. Deep learning and handcrafted radiomics features were extracted from ultrasound images of primary tumors and lymph nodes. The tumor score and LN score were calculated following feature selection, and a clinical-radiomics model was constructed based on these scores along with clinical-ultrasonic risk factors. The model's performance was validated across the 3 test sets. Additionally, the diagnostic performance of radiologists, with and without model assistance, was evaluated. RESULTS: The clinical-radiomics model demonstrated robust discrimination with AUCs of 0.94, 0.92, 0.91, and 0.95 in the training, internal test, external test, and prospective test sets, respectively. It surpassed the clinical model and single score in all sets (P < .05). Decision curve analysis and clinical impact curves validated the clinical utility of the clinical-radiomics model. Moreover, the model significantly improved radiologists' diagnostic accuracy, with AUCs increasing from 0.71 to 0.82 for the junior radiologist and from 0.75...