Fan Ming, Li Hui, Wang Shijian, Zheng Bin, Zhang Juan, Li Lihua
Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China.
School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, United States of America.
PLoS One. 2017 Feb 6;12(2):e0171683. doi: 10.1371/journal.pone.0171683. eCollection 2017.
The purpose of this study was to investigate the role of features derived from breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and to incorporated clinical information to predict the molecular subtypes of breast cancer. In particular, 60 breast cancers with the following four molecular subtypes were analyzed: luminal A, luminal B, human epidermal growth factor receptor-2 (HER2)-over-expressing and basal-like. The breast region was segmented and the suspicious tumor was depicted on sequentially scanned MR images from each case. In total, 90 features were obtained, including 88 imaging features related to morphology and texture as well as dynamic features from tumor and background parenchymal enhancement (BPE) and 2 clinical information-based parameters, namely, age and menopausal status. An evolutionary algorithm was used to select an optimal subset of features for classification. Using these features, we trained a multi-class logistic regression classifier that calculated the area under the receiver operating characteristic curve (AUC). The results of a prediction model using 24 selected features showed high overall classification performance, with an AUC value of 0.869. The predictive model discriminated among the luminal A, luminal B, HER2 and basal-like subtypes, with AUC values of 0.867, 0.786, 0.888 and 0.923, respectively. An additional independent dataset with 36 patients was utilized to validate the results. A similar classification analysis of the validation dataset showed an AUC of 0.872 using 15 image features, 10 of which were identical to those from the first cohort. We identified clinical information and 3D imaging features from DCE-MRI as candidate biomarkers for discriminating among four molecular subtypes of breast cancer.
本研究的目的是探讨乳腺动态对比增强磁共振成像（DCE-MRI）衍生特征的作用，并纳入临床信息以预测乳腺癌的分子亚型。具体而言，分析了60例具有以下四种分子亚型的乳腺癌：腔面A型、腔面B型、人表皮生长因子受体2（HER2）过表达型和基底样型。对乳腺区域进行分割，并在每个病例的连续扫描MR图像上描绘可疑肿瘤。总共获得了90个特征，包括88个与形态和纹理相关的成像特征以及来自肿瘤和背景实质强化（BPE）的动态特征，以及2个基于临床信息的参数，即年龄和绝经状态。使用进化算法选择用于分类的最佳特征子集。利用这些特征，我们训练了一个多分类逻辑回归分类器，计算受试者操作特征曲线（AUC）下的面积。使用24个选定特征的预测模型结果显示出较高的总体分类性能，AUC值为0.869。该预测模型能够区分腔面A型、腔面B型、HER2型和基底样型亚型，AUC值分别为0.867、0.786、0.888和0.923。利用另外一个包含36例患者的独立数据集来验证结果。对验证数据集进行的类似分类分析显示，使用15个图像特征时AUC为0.872，其中10个与第一个队列中的特征相同。我们将临床信息和DCE-MRI的三维成像特征确定为区分乳腺癌四种分子亚型的候选生物标志物。
