Xu Rong, You Tao, Liu Chen, Lin Qing, Guo Quehui, Zhong Guodong, Liu Leilei, Ouyang Qiufang
Department of Ultrasound, The Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Department of Breast, The Second Affiliated Hospital of Fujian University of Traditional Chinese Medicine, Fuzhou, Fujian, China.
Front Oncol. 2023 Jul 31;13:1216446. doi: 10.3389/fonc.2023.1216446. eCollection 2023.
Breast cancer (BC) is the most common cancer in women and is highly heterogeneous. BC can be classified into four molecular subtypes based on the status of estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and proliferation marker protein Ki-67. However, they can only be obtained by biopsy or surgery, which is invasive. Radiomics can noninvasively predict molecular expression extracting the image features. Nevertheless, there is a scarcity of data available regarding the prediction of molecular biomarker expression using ultrasound (US) images in BC.
To investigate the prediction performance of US radiomics for the assessment of molecular profiling in BC.
A total of 342 patients with BC who underwent preoperative US examination between January 2013 and December 2021 were retrospectively included. They were confirmed by pathology and molecular subtype analysis of ER, PR, HER2 and Ki-67. The radiomics features were extracted and four molecular models were constructed through support vector machine (SVM). Pearson correlation coefficient heatmaps are employed to analyze the relationship between selected features and their predictive power on molecular expression. The receiver operating characteristic curve was used for the prediction performance of US radiomics in the assessment of molecular profiling.
359 lesions with 129 ER- and 230 ER+, 163 PR- and 196 PR+, 265 HER2- and 94 HER2+, 114 Ki-67- and 245 Ki-67+ expression were included. 1314 features were extracted from each ultrasound image. And there was a significant difference of some specific radiomics features between the molecule positive and negative groups. Multiple features demonstrated significant association with molecular biomarkers. The area under curves (AUCs) were 0.917, 0.835, 0.771, and 0.896 in the training set, while 0.868, 0.811, 0.722, and 0.706 in the validation set to predict ER, PR, HER2, and Ki-67 expression respectively.
Ultrasound-based radiomics provides a promising method for predicting molecular biomarker expression of ER, PR, HER2, and Ki-67 in BC.
乳腺癌（BC）是女性最常见的癌症，具有高度异质性。BC可根据雌激素受体（ER）、孕激素受体（PR）、人表皮生长因子受体2（HER2）和增殖标志物蛋白Ki-67的状态分为四种分子亚型。然而，这些指标只能通过活检或手术获取，具有侵入性。放射组学可以通过提取图像特征来无创地预测分子表达。然而，关于利用超声（US）图像预测BC分子生物标志物表达的数据却很少。
研究US放射组学在评估BC分子特征方面的预测性能。
回顾性纳入2013年1月至2021年12月期间接受术前US检查的342例BC患者。通过ER、PR、HER2和Ki-67的病理及分子亚型分析进行确诊。提取放射组学特征，并通过支持向量机（SVM）构建四种分子模型。采用Pearson相关系数热图分析所选特征与其对分子表达的预测能力之间的关系。采用受试者操作特征曲线评估US放射组学在评估分子特征方面的预测性能。
纳入359个病灶，其中ER- 129个、ER+ 230个，PR- 163个、PR+ 196个，HER2- 265个、HER2+ 94个，Ki-67- 114个、Ki-67+ 245个。从每个超声图像中提取1314个特征。分子阳性和阴性组之间的一些特定放射组学特征存在显著差异。多个特征与分子生物标志物显示出显著相关性。在训练集中预测ER、PR、HER2和Ki-67表达的曲线下面积（AUC）分别为0.917、0.835、0.771和0.896，在验证集中分别为0.868、0.811、0.722和0.706。
基于超声的放射组学为预测BC中ER、PR、HER2和Ki-67的分子生物标志物表达提供了一种有前景的方法。
