im3D, Research and Development Department, Turin, Italy.
Med Phys. 2012 Apr;39(4):1704-15. doi: 10.1118/1.3691178.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a radiological tool for the detection and discrimination of breast lesions. The aim of this study is to evaluate a computer-aided diagnosis (CAD) system for discriminating malignant from benign breast lesions at DCE-MRI by the combined use of morphological, kinetic, and spatiotemporal lesion features.
Fifty-four malignant and 19 benign breast lesions in 51 patients were retrospectively evaluated. Images were acquired at two centers at 1.5 T. Mass-like lesions were automatically segmented after image normalization and elastic coregistration of contrast-enhanced frames. For each lesion, a set of 28 3D features were extracted: ten morphological (related to shape, margins, and internal enhancement distribution); nine kinetic (computed from signal-to-time curves); and nine spatiotemporal (related to the variation of the signal between adjacent frames). A support vector machine (SVM) was trained with feature subsets selected by a genetic search. Best subsets were composed of the most frequent features selected by majority rule. The performance was measured by receiver operator characteristics analysis with a stratified tenfold cross-validation and bootstrap method for confidence intervals.
SVM training by the three separated classes of features resulted in an area under the curve (AUC) of 0.90 ± 0.04 (mean ± standard deviation), 0.87 ± 0.06, and 0.86 ± 0.06 for morphological, kinetic, and spatiotemporal feature, respectively. Combined training with all 28 features resulted in AUC of 0.96 ± 0.02 obtained with a selected feature subset composed by two morphological, one kinetic, and two spatiotemporal features.
Quantitative combination of morphological, kinetic, and spatiotemporal features is feasible and provides a higher discriminating power than using the three different classes of features separately.
动态对比增强磁共振成像（DCE-MRI）是一种用于检测和鉴别乳腺病变的影像学工具。本研究旨在通过联合使用形态学、动力学和时空病变特征来评估一种用于 DCE-MRI 区分良恶性乳腺病变的计算机辅助诊断（CAD）系统。
回顾性评估了 51 名患者的 54 个恶性和 19 个良性乳腺病变。图像在 1.5T 两个中心采集。在对对比增强帧进行图像归一化和弹性配准后，自动对肿块样病变进行分割。对于每个病变，提取了一组 28 个 3D 特征：10 个形态学特征（与形状、边缘和内部增强分布有关）；9 个动力学特征（从信号-时间曲线计算得出）；9 个时空特征（与相邻帧之间信号的变化有关）。使用遗传搜索选择特征子集对支持向量机（SVM）进行训练。最佳子集由多数规则选择的最常见特征组成。通过分层十折交叉验证和 bootstrap 方法进行置信区间评估，以接收者操作特征曲线分析来衡量性能。
分别使用三种不同类别的特征进行 SVM 训练，得到的曲线下面积（AUC）分别为 0.90±0.04（平均值±标准差）、0.87±0.06 和 0.86±0.06，用于形态学、动力学和时空特征。使用所有 28 个特征进行联合训练，得到的 AUC 为 0.96±0.02，使用由两个形态学特征、一个动力学特征和两个时空特征组成的选择特征子集获得。
形态学、动力学和时空特征的定量组合是可行的，比分别使用三种不同类别的特征提供了更高的鉴别能力。
