Dai Feihang, Guo Baoliang, Ai Xin, Liu Dandan, Dai Longbin, Zhang Chengzhi, Leng Xiaoping, Wu Tong
Department of Ultrasound, The Second Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China.
Department of General Surgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, People's Republic of China.
Cancer Med. 2025 Sep;14(17):e71156. doi: 10.1002/cam4.71156.
Human epidermal growth factor receptor 2 (HER2) is a key biomarker for clinical management and prognostic evaluation of breast cancer patients. This study was aimed at assisting the preoperative and non-invasive prediction of HER2-low breast cancer using multimodal ultrasound imaging and clinicopathological indicators, providing valuable imaging information for clinical precision diagnosis and personalized treatment strategies, especially in the application of antibody-drug conjugates such as T-DXd.
This retrospective study included 147 pathologically confirmed breast cancer patients from two institutions: 101 in the training set and 46 in the external validation set. All patients underwent multimodal ultrasound (grayscale, color Doppler, elastography, and contrast-enhanced imaging) and had complete clinicopathological data. Patients were categorized as HER2-negative, HER2-low, or HER2-positive based on immunohistochemistry. Logistic regression was used to construct predictive models.
Compared with the HER2-negative group, low Ki-67, PR positivity, longer rise time (RT), and lower Emax values were independent predictors of HER2-low status (p < 0.05), yielding an AUC of 0.876, sensitivity 0.833, and specificity 0.781. Compared with HER2-positive cancers, HER2-low cases showed low Ki-67, ER/PR positivity, low Emax, and a DVPC pattern characterized by an initial increase followed by a subsequent decline as independent predictors (p < 0.05), with an AUC of 0.929, sensitivity 0.905, and specificity 0.856. External validation confirmed robust model performance (AUC = 0.925 and 0.918 for HER2-low vs. negative and positive, respectively).
A model integrating multimodal ultrasound and clinicopathological factors effectively predicts HER2-low breast cancer, offering valuable imaging-based support for clinical decision-making.
人表皮生长因子受体2（HER2）是乳腺癌患者临床管理和预后评估的关键生物标志物。本研究旨在利用多模态超声成像和临床病理指标辅助术前无创预测HER2低表达乳腺癌，为临床精准诊断和个性化治疗策略提供有价值的影像信息，尤其是在T-DXd等抗体药物偶联物的应用方面。
本回顾性研究纳入了来自两家机构的147例经病理确诊的乳腺癌患者：训练集101例，外部验证集46例。所有患者均接受了多模态超声检查（灰阶、彩色多普勒、弹性成像和对比增强成像），并拥有完整的临床病理数据。根据免疫组化结果将患者分为HER2阴性、HER2低表达或HER2阳性。采用逻辑回归构建预测模型。
与HER2阴性组相比，低Ki-67、PR阳性、较长的上升时间（RT）和较低的Emax值是HER2低表达状态的独立预测因素（p < 0.05），曲线下面积（AUC）为0.876，灵敏度为0.833，特异度为0.781。与HER2阳性癌相比，HER2低表达病例表现为低Ki-67、ER/PR阳性、低Emax以及以先升高后下降为特征的动态容积参数曲线（DVPC）模式，这些为独立预测因素（p < 0.05），AUC为0.929，灵敏度为0.905，特异度为0.856。外部验证证实了模型的稳健性能（HER2低表达与阴性和阳性相比，AUC分别为0.925和0.918）。
整合多模态超声和临床病理因素的模型可有效预测HER2低表达乳腺癌，为临床决策提供有价值的基于影像的支持。
