Lu Hecheng, Yin Jiandong
School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China.
Department of Radiology, ShengJing Hospital of China Medical University, Shenyang, China.
Front Oncol. 2020 Apr 21;10:543. doi: 10.3389/fonc.2020.00543. eCollection 2020.
Breast tumor heterogeneity is related to risk factors that lead to aggressive tumor growth; however, such heterogeneity has not been thoroughly investigated. To evaluate the performance of texture features extracted from heterogeneity subregions on subtraction MRI images for identifying human epidermal growth factor receptor 2 (HER2) 2+ status of breast cancers. Seventy-six patients with HER2 2+ breast cancer who underwent dynamic contrast-enhanced magnetic resonance imaging were enrolled, including 42 HER2 positive and 34 negative cases confirmed by fluorescence hybridization. The lesion area was delineated semi-automatically on the subtraction MRI images at the second, fourth, and sixth phases (P-1, P-2, and P-3). A regionalization method was used to segment the lesion area into three subregions (rapid, medium, and slow) according to peak arrival time of the contrast agent. We extracted 488 texture features from the whole lesion area and three subregions independently. Wrapper, least absolute shrinkage and selection operator (LASSO), and stepwise methods were used to identify the optimal feature subsets. Univariate analysis was performed as well as support vector machine (SVM) with a leave-one-out-based cross-validation method. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the classifiers. In univariate analysis, the variance from medium subregion at P-2 was the best-performing feature for distinguishing HER2 2+ status (AUC = 0.836); for the whole lesion region, the variance at P-2 achieved the best performance (AUC = 0.798). There was no significant difference between the two methods ( = 0.271). In the machine learning with SVM, the best performance (AUC = 0.929) was achieved with LASSO from rapid subregion at P-2; for the whole region, the highest AUC value was 0.847 obtained at P-2 with LASSO. The difference was significant between the two methods ( = 0.021). The texture analysis of heterogeneity subregions based on intratumoral regionalization method showed potential value for recognizing HER2 2+ status in breast cancer.
乳腺肿瘤异质性与导致肿瘤侵袭性生长的风险因素相关；然而，这种异质性尚未得到充分研究。为了评估从减法MRI图像的异质性子区域提取的纹理特征在识别乳腺癌人表皮生长因子受体2（HER2）2+状态方面的性能。招募了76例接受动态对比增强磁共振成像的HER2 2+乳腺癌患者，其中包括通过荧光杂交确认的42例HER2阳性和34例阴性病例。在减法MRI图像的第二、第四和第六阶段（P-1、P-2和P-3）半自动勾勒病变区域。采用一种区域划分方法根据对比剂的峰值到达时间将病变区域分割为三个子区域（快速、中等和缓慢）。我们分别从整个病变区域和三个子区域提取了488个纹理特征。采用包装法、最小绝对收缩和选择算子（LASSO）以及逐步法来识别最佳特征子集。进行单因素分析以及采用基于留一法交叉验证方法的支持向量机（SVM）。计算受试者工作特征曲线（AUC）下的面积以评估分类器的性能。在单因素分析中，P-2时中等子区域的方差是区分HER2 2+状态的最佳性能特征（AUC = 0.836）；对于整个病变区域，P-2时的方差表现最佳（AUC = 0.798）。两种方法之间无显著差异（= 0.271）。在使用SVM的机器学习中，P-2时快速子区域的LASSO实现了最佳性能（AUC = 0.929）；对于整个区域，P-2时LASSO获得的最高AUC值为0.847。两种方法之间的差异具有显著性（= 0.021）。基于肿瘤内区域划分方法的异质性子区域纹理分析显示出在识别乳腺癌HER2 2+状态方面的潜在价值。
