Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis
Assessing heterogeneity of lesion enhancement kinetics in dynamic contrast-enhanced MRI for breast cancer diagnosis
复制标题
DOI：
10.1259/bjr/50743919
复制
发表时间：
2010-04-01
影响因子：
2.6
通讯作者：
Costaridou, L.
中科院分区：
文献类型：
--
作者：
Karahaliou, A.;Vassiou, K.;Costaridou, L.
目前的研究探讨了使用纹理分析来量化病变增强动力学的异质性，以区分恶性和良性乳腺病变的可行性。共82个活检证实的乳腺病变（51恶性，31良性），来自74名妇女进行动态对比增强磁共振成像（DCE-MRI）进行了分析。对DCE-MRI病变数据进行逐像素分析，以生成初始增强、初始增强后和信号增强比（SER）参数图;随后对这些图进行共生矩阵纹理分析。使用最小二乘最小距离分类器研究了从每个参数图中提取的纹理特征的区分能力，并进一步与从第一个对比后帧中提取的相同纹理特征的区分能力进行了比较。从SER图中提取的所选纹理特征实现了0.922 +/-0.029的接收器操作特征曲线下的面积，性能类似于初始化后的增强图特征（0.906 +/- 0.032），在统计学上显著高于初始增强图（0.767 +/- 0.053）和第一个造影后帧（0.756 +/- 0.060）特征。量化反映病变洗脱特性的参数图的异质性可能有助于DCE-MRI中乳腺病变的计算机辅助诊断。
The current study investigates the feasibility of using texture analysis to quantify the heterogeneity of lesion enhancement kinetics in order to discriminate malignant from benign breast lesions. A total of 82 biopsy-proven breast lesions (51 malignant, 31 benign), originating from 74 women subjected to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) were analysed. Pixel-wise analysis of DCE-MRI lesion data was performed to generate initial enhancement, post-initial enhancement and signal enhancement ratio (SER) parametric maps; these maps were subsequently subjected to co-occurrence matrix texture analysis. The discriminating ability of texture features extracted from each parametric map was investigated using a least-squares minimum distance classifier and further compared with the discriminating ability of the same texture features extracted from the first post-contrast frame. Selected texture features extracted from the SER map achieved an area under receiver operating characteristic curve of 0.922 +/- 0.029, a performance similar to post-initial enhancement map features (0.906 +/- 0.032) and statistically significantly higher than for initial enhancement map (0.767 +/- 0.053) and first post-contrast frame (0.756 +/- 0.060) features. Quantifying the heterogeneity of parametric maps that reflect lesion washout properties could contribute to the computer-aided diagnosis of breast lesions in DCE-MRI.