Zhou Lei, Wu Yingnan, Wen Xin, Guo Xu, Zhang Lei, Zhao Tianzhuo, Song Weijian, Xin Yue, Su Zehui, Sun Litao, Tian Jiawei
Department of Ultrasound Medicine, The Second Affiliated Hospital of Harbin Medical University, No. 246 Xuefu Road, Nan Gang Dist, Harbin, Heilongjiang Province, 150086, China.
Cancer Center, Department of Ultrasound Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou, Zhejiang Province, 310014, China.
BMC Womens Health. 2025 Jul 3;25(1):291. doi: 10.1186/s12905-025-03828-7.
The assessment of Human Epidermal Growth Factor Receptor 2 (HER2) expression status is crucial for determining the eligibility of breast cancer (BC) patients for HER2-targeted therapies. This study aims to develop a nomogram model that incorporates multimodal ultrasound imaging features alongside clinicopathological characteristics to evaluate HER2 status.
A retrospective analysis was conducted on 456 breast cancer patients who underwent breast ultrasound between January 2019 and December 2021. The dataset was randomly divided into a training cohort (n = 319) and a validation cohort (n = 137) in a 7:3 ratio. Independent factors predicting HER2 status in the training cohort were evaluated using univariate and multivariate logistic regression. Subsequently, a combined model was developed and validated in the validation cohort. Model performance was assessed through receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and calibration curves to evaluate discrimination, net clinical benefit, and calibration, respectively.
Of the 456 patients enrolled, 120 (26.32%) were HER2-positive and 336 (73.68%) were HER2-negative. The area under the ROC curve (AUC) for the combined model distinguishing HER2-negative from HER2-positive patients was 0.864 (95% CI: 0.823-0.904) in the training cohort and 0.874 (95% CI: 0.815-0.933) in the validation cohort. Significant predictors included estrogen receptor (ER) status, Ki67, ultrasound lesion size, calcification, and posterior acoustic features. Additionally, the calibration curves for the combined model indicated good fit in both the training and validation cohorts.
A nomogram constructed from clinical and ultrasound features may serve as a promising non-invasive tool for determining HER2 expression status, aiding in the prediction of eligibility for HER2-targeted therapy in clinical practice.
Not applicable.
评估人表皮生长因子受体2（HER2）表达状态对于确定乳腺癌（BC）患者是否适合接受HER2靶向治疗至关重要。本研究旨在开发一种列线图模型，该模型纳入多模态超声成像特征以及临床病理特征来评估HER2状态。
对2019年1月至2021年12月期间接受乳腺超声检查的456例乳腺癌患者进行回顾性分析。数据集以7:3的比例随机分为训练队列（n = 319）和验证队列（n = 137）。使用单因素和多因素逻辑回归评估训练队列中预测HER2状态的独立因素。随后，在验证队列中开发并验证了一个组合模型。通过受试者操作特征（ROC）曲线、决策曲线分析（DCA）和校准曲线评估模型性能，分别用于评估区分能力、净临床获益和校准情况。
在纳入的456例患者中，120例（26.32%）为HER2阳性，336例（73.68%）为HER2阴性。在训练队列中，区分HER2阴性和HER2阳性患者的组合模型的ROC曲线下面积（AUC）为0.864（95%可信区间：0.823 - 0.904），在验证队列中为0.874（95%可信区间：0.815 - 0.933）。显著预测因素包括雌激素受体（ER）状态、Ki67、超声病变大小、钙化和后方声学特征。此外，组合模型的校准曲线表明在训练和验证队列中拟合良好。
由临床和超声特征构建的列线图可能是一种有前景的非侵入性工具，用于确定HER2表达状态，有助于在临床实践中预测HER2靶向治疗的适用性。
不适用。
