Department of Medical Physics, Faculty of Medicine, University of Patras, 26500 Patras, Greece.
Br J Radiol. 2010 Apr;83(988):296-309. doi: 10.1259/bjr/50743919.
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.
本研究旨在探讨利用纹理分析量化病变增强动力学异质性的可行性，以区分良恶性乳腺病变。共分析了 74 名女性 82 个经活检证实的乳腺病变（51 个恶性，31 个良性）的动态对比增强磁共振成像（DCE-MRI）数据。对 DCE-MRI 病变数据进行逐像素分析，生成初始增强、初始后增强和信号增强比（SER）参数图；随后对这些图像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从每个参数图中提取的纹理特征的区分能力，并与从第一对比后帧中提取的相同纹理特征的区分能力进行比较。从 SER 图中提取的选定纹理特征的受试者工作特征曲线下面积为 0.922 +/- 0.029，与初始后增强图特征（0.906 +/- 0.032）的性能相似，且统计学上显著高于初始增强图（0.767 +/- 0.053）和第一对比后帧（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。
