Ultrasound and clinicopathological characteristics-based model for prediction of pathologic response to neoadjuvant chemotherapy in HER2-positive breast cancer: a case–control study

乳腺癌 医学 肿瘤科 阶段(地层学) 接收机工作特性 单变量分析 内科学 化疗 癌症 多元分析 曲妥珠单抗 曲线下面积 生物 古生物学
作者
Sui Lin,Yuqi Yan,Tian Jiang,Di Ou,Chen Chen,Min Lai,Ni Chen,Xi Zhu,Liping Wang,Chen Yang,Wei Li,Jincao Yao,Dong Xu
出处
期刊:Breast Cancer Research and Treatment [Springer Science+Business Media]
卷期号:202 (1): 45-55 被引量:4
标识
DOI:10.1007/s10549-023-07057-0
摘要

Abstract Background The objective of this study was to develop a model combining ultrasound (US) and clinicopathological characteristics to predict the pathologic response to neoadjuvant chemotherapy (NACT) in human epidermal growth factor receptor 2 (HER2)-positive breast cancer. Materials and methods This is a retrospective study that included 248 patients with HER2-positive breast cancer who underwent NACT from March 2018 to March 2022. US and clinicopathological characteristics were collected from all patients in this study, and characteristics obtained using univariate analysis at p < 0.1 were subjected to multivariate analysis and then the conventional US and clinicopathological characteristics independently associated with pathologic complete response (pCR) from the analysis were used to develop US models, clinicopathological models, and their combined models by the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity to assess their predictive efficacy. Results The combined model had an AUC of 0.808, a sensitivity of 88.72%, a specificity of 60.87%, and an accuracy of 75.81% in predicting pCR of HER2-positive breast cancer after NACT, which was significantly better than the clinicopathological model (AUC = 0.656) and the US model (AUC = 0.769). In addition, six characteristics were screened as independent predictors, namely the Clinical T stage, Clinical N stage, PR status, posterior acoustic, margin, and calcification. Conclusion The conventional US combined with clinicopathological characteristics to construct a combined model has a good diagnostic effect in predicting pCR in HER2-positive breast cancer and is expected to be a useful tool to assist clinicians in effectively determining the efficacy of NACT in HER2-positive breast cancer patients.
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