Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel.
J Magn Reson Imaging. 2009 Nov;30(5):989-98. doi: 10.1002/jmri.21950.
To investigate a fast, objective, and standardized method for analyzing breast dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) applying principal component analysis (PCA) adjusted with a model-based method.
3D gradient-echo DCE breast images of 31 malignant and 38 benign lesions, recorded on a 1.5T scanner, were retrospectively analyzed by PCA and by the model-based three-timepoints (3TP) method.
Intensity-scaled (IS) and enhancement-scaled (ES) datasets were reduced by PCA yielding a first IS-eigenvector that captured the signal variation between fat and fibroglandular tissue; two IS-eigenvectors and the two first ES-eigenvectors captured contrast-enhanced changes, whereas the remaining eigenvectors captured predominantly noise changes. Rotation of the two contrast-related eigenvectors led to a high congruence between the projection coefficients and the 3TP parameters. The ES-eigenvectors and the rotation angle were highly reproducible across malignant lesions, enabling calculation of a general rotated eigenvector base. Receiver operating characteristic (ROC) curve analysis of the projection coefficients of the two eigenvectors indicated high sensitivity of the first rotated eigenvector to detect lesions (area under the curve [AUC] > 0.97) and of the second rotated eigenvector to differentiate malignancy from benignancy (AUC = 0.87).
PCA adjusted with a model-based method provided a fast and objective computer-aided diagnostic tool for breast DCE-MRI.
研究一种快速、客观、标准化的方法，应用基于模型的方法调整主成分分析（PCA）来分析乳腺动态对比增强磁共振成像（DCE-MRI）。
对 1.5T 扫描仪上记录的 31 例恶性和 38 例良性病变的 3D 梯度回波 DCE 乳腺图像进行回顾性分析，采用 PCA 和基于模型的三时点（3TP）方法进行分析。
强度标度（IS）和增强标度（ES）数据集通过 PCA 减少，得到第一个捕获脂肪和纤维腺体组织之间信号变化的 IS 特征向量；两个 IS 特征向量和前两个 ES 特征向量捕获对比增强变化，而其余特征向量主要捕获噪声变化。两个与对比相关的特征向量的旋转导致投影系数与 3TP 参数之间具有高度一致性。恶性病变之间 ES 特征向量和旋转角度具有高度可重复性，从而能够计算一般旋转特征向量基。两个特征向量的投影系数的受试者工作特征（ROC）曲线分析表明，第一个旋转特征向量对检测病变具有很高的敏感性（曲线下面积[AUC]＞0.97），第二个旋转特征向量对区分良恶性具有很高的特异性（AUC=0.87）。
基于模型的方法调整的 PCA 为乳腺 DCE-MRI 提供了一种快速、客观的计算机辅助诊断工具。
