Gong Xuantong, Li Qingfeng, Gu Lishuang, Chen Chen, Liu Xuefeng, Zhang Xuan, Wang Bo, Sun Chao, Yang Di, Li Lin, Wang Yong
Department of Ultrasound, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
School of Computer Science and Engineering, Beihang University, Beijing, China.
Front Oncol. 2023 May 23;13:1158736. doi: 10.3389/fonc.2023.1158736. eCollection 2023.
This study aimed to explore the value of conventional ultrasound (CUS) and contrast-enhanced ultrasound (CEUS) radiomics to diagnose breast cancer and predict its molecular subtype.
A total of 170 lesions (121 malignant, 49 benign) were selected from March 2019 to January 2022. Malignant lesions were further divided into six categories of molecular subtype: (non-)Luminal A, (non-)Luminal B, (non-)human epidermal growth factor receptor 2 (HER2) overexpression, (non-)triple-negative breast cancer (TNBC), hormone receptor (HR) positivity/negativity, and HER2 positivity/negativity. Participants were examined using CUS and CEUS before surgery. Regions of interest images were manually segmented. The pyradiomics toolkit and the maximum relevance minimum redundancy algorithm were utilized to extract and select features, multivariate logistic regression models of CUS, CEUS, and CUS combined with CEUS radiomics were then constructed and evaluated by fivefold cross-validation.
The accuracy of the CUS combined with CEUS model was superior to CUS model (85.4% vs. 81.3%, p<0.01). The accuracy of the CUS radiomics model in predicting the six categories of breast cancer is 68.2% (82/120), 69.3% (83/120), 83.7% (100/120), 86.7% (104/120), 73.5% (88/120), and 70.8% (85/120), respectively. In predicting breast cancer of Luminal A, HER2 overexpression, HR-positivity, and HER2 positivity, CEUS video improved the predictive performance of CUS radiomics model [accuracy=70.2% (84/120), 84.0% (101/120), 74.5% (89/120), and 72.5% (87/120), p<0.01].
CUS radiomics has the potential to diagnose breast cancer and predict its molecular subtype. Moreover, CEUS video has auxiliary predictive value for CUS radiomics.
本研究旨在探讨常规超声（CUS）和超声造影（CEUS）的影像组学在乳腺癌诊断及分子亚型预测中的价值。
选取2019年3月至2022年1月期间的170个病灶（121个恶性，49个良性）。恶性病灶进一步分为六种分子亚型：（非）腔面A型、（非）腔面B型、（非）人类表皮生长因子受体2（HER2）过表达型、（非）三阴性乳腺癌（TNBC）、激素受体（HR）阳性/阴性以及HER2阳性/阴性。术前对参与者进行CUS和CEUS检查。手动分割感兴趣区域图像。利用pyradiomics工具包和最大相关最小冗余算法提取并选择特征，然后构建CUS、CEUS以及CUS联合CEUS影像组学的多变量逻辑回归模型，并通过五折交叉验证进行评估。
CUS联合CEUS模型的准确性优于CUS模型（85.4%对81.3%，p<0.01）。CUS影像组学模型预测六种乳腺癌的准确性分别为68.2%（82/120）、69.3%（83/120）、83.7%（100/120）、86.7%（104/120）、73.5%（88/120）和70.8%（85/120）。在预测腔面A型、HER2过表达型、HR阳性和HER2阳性的乳腺癌时，CEUS视频提高了CUS影像组学模型的预测性能[准确性分别为70.2%（84/120）、84.0%（101/120）、74.5%（89/120）和72.5%（87/120），p<0.01]。
CUS影像组学具有诊断乳腺癌及其分子亚型预测的潜力。此外，CEUS视频对CUS影像组学具有辅助预测价值。
