Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era.
Using quantitative image analysis to classify axillary lymph nodes on breast MRI: a new application for the Z 0011 Era.
复制标题
DOI：
10.1016/j.ejrad.2014.12.003
复制
发表时间：
2015-03
影响因子：
3.3
通讯作者：
Giger ML
中科院分区：
文献类型：
--
作者：
Schacht DV;Drukker K;Pak I;Abe H;Giger ML
目的评价计算机提取的腋窝淋巴结动态对比增强（DCE）磁共振图像特征分析的性能。确定哪些定量特征最能预测淋巴结转移。这项机构委员会批准的符合HIPAA的研究，在该研究中，患者无需知情同意，收集了乳腺癌患者腋下的增强T1图像。使用实验室开发的定量图像分析（QIA）工作站对192个节点进行病灶分割和特征分析。评估了28个特征的重要性。分类使用这些特征作为神经网络分类器的输入，进行留一例交叉验证，并使用接收者工作特征（ROC）分析进行评估。在区分阳性和阴性节点的任务中，特征的ROC曲线下面积（AUC）值从0.50到0.70不等。五个特征的auc大于0.65：两个形态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正节点和负节点的任务中获得了0.88 （SE 0.03）的AUC。DCE MRI的QIA在鉴别腋窝淋巴结阳性和阴性方面表现良好。
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 importances 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.