无线电技术
人表皮生长因子受体2
逻辑回归
乳腺癌
接收机工作特性
肿瘤科
医学
超声波
曲妥珠单抗
内科学
癌症
放射科
作者
Yu Du,Fang Li,Manqi Zhang,Jing Pan,Tingting Wang,Yong Zheng,Jing Chen,Meicun Yao,Yi Kuang,Rong Wu,Xuehong Diao
标识
DOI:10.1016/j.acra.2024.01.023
摘要
Rationale and Objectives
To evaluate whether ultrasound-based radiomics features can effectively predict HER2-low expression in patients with breast cancer (BC). Material and Methods
Between January 2021 and June 2023, patients who received US scans with pathologically confirmed BC in this multicenter study were included. In total, 383 patients from institution 1 were comprised of training set, 233 patients from institution 2 were comprised of validation set and 149 patients from institution 3 were comprised of external validation set. Radiomics features were derived from conventional ultrasound (US) images. The minimum redundancy and maximum relevancy and the least absolute shrinkage and selector operation algorithm were used to generate an US-based radiomics score (RS). Multivariable logistic regression analysis was used to select variables associated with HER2 expressions. The diagnostic performance of the RS was evaluated through the area under the receiver operating characteristic curve (AUC). Results
In the training set, the RS yield an AUC of 0.81 (95%CI: 0.76–0.84) for differentiation HER2-zero from HER2-low and -positive cases, and performed well in validation set (AUC 0.84, 95%CI: 0.78–0.88) and external validation set (AUC 0.82, 95%CI: 0.73–0.90). In the subgroups analysis, the RS showed good performance in distinguishing HER2-zero from HER2 1 + , HER2 2 + and HER2-low tumors (AUC range, 0.79–0.87). Conclusion
The RS based on conventional US is proven effective for predicting HER2-low expression in BC.
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