Schacht David V, Drukker Karen, Pak Iris, Abe Hiroyuki, Giger Maryellen L
Department of Radiology, The University of Chicago, Section of Breast Imaging, 5841 S. Maryland Avenue, MC 2026, Chicago, IL 60637, United States.
Eur J Radiol. 2015 Mar;84(3):392-397. doi: 10.1016/j.ejrad.2014.12.003. Epub 2014 Dec 15.
To assess the performance of computer extracted feature analysis of dynamic contrast enhanced (DCE) magnetic resonance images (MRI) of axillary lymph nodes. To determine which quantitative features best predict nodal metastasis.
This institutional board-approved HIPAA compliant study, in which informed patient consent was waived, collected enhanced T1 images of the axilla from patients with breast cancer. Lesion segmentation and feature analysis were performed on 192 nodes using a laboratory-developed quantitative image analysis (QIA) workstation. The importance of 28 features were assessed. Classification used the features as input to a neural net classifier in a leave-one-case-out cross-validation and evaluated with receiver operating characteristic (ROC) analysis.
The area under the ROC curve (AUC) values for features in the task of distinguishing between positive and negative nodes ranged from just over 0.50 to 0.70. Five features yielded AUCs greater than 0.65: two morphological and three textural features. In cross-validation, the neural net classifier obtained an AUC of 0.88 (SE 0.03) for the task of distinguishing between positive and negative nodes.
QIA of DCE MRI demonstrated promising performance in discriminating between positive and negative axillary nodes.
评估腋窝淋巴结动态对比增强（DCE）磁共振成像（MRI）的计算机提取特征分析性能。确定哪些定量特征能最佳预测淋巴结转移。
本机构委员会批准的符合健康保险流通与责任法案（HIPAA）的研究，在该研究中患者知情同意被豁免，收集了乳腺癌患者腋窝的增强T1图像。使用实验室开发的定量图像分析（QIA）工作站对192个淋巴结进行病变分割和特征分析。评估了28个特征的重要性。分类将这些特征作为输入，用于留一法交叉验证中的神经网络分类器，并通过受试者操作特征（ROC）分析进行评估。
在区分阳性和阴性淋巴结任务中，各特征的ROC曲线下面积（AUC）值范围从略高于0.50到0.70。五个特征的AUC大于0.65：两个形态学特征和三个纹理特征。在交叉验证中，神经网络分类器在区分阳性和阴性淋巴结任务中获得的AUC为0.88（标准误0.03）。
DCE MRI的QIA在区分腋窝淋巴结阳性和阴性方面表现出良好前景。
