Leichter I, Lederman R, Buchbinder S S, Bamberger P, Novak B, Fields S
Department of Electro-Optics, Jerusalem College of Technology, Jerusalem, Israel.
AJR Am J Roentgenol. 2004 Mar;182(3):705-12. doi: 10.2214/ajr.182.3.1820705.
The objective of this study was to compare the diagnostic role of features reflecting the geometry of clusters with features reflecting the shape of the individual microcalcification in a mammographic computer-aided diagnosis system.
Three hundred twenty-four cases of clustered microcalcifications with biopsy-proven results were digitized at 42-microm resolution and analyzed on a computerized system. The shape factor and number of neighbors were computed for each microcalcification, and the eccentricity of the cluster was computed as well. The shape factor is related to the individual microcalcification; the average number of neighbors and the cluster eccentricity reflect the cluster geometry. Stepwise discriminant analysis was used to evaluate the contribution of the extracted features in predicting malignancy. The performance of a classifier based on the features selected by stepwise discriminant analysis was evaluated by receiver operating characteristic (ROC) analysis.
To obtain the best discrimination model, we used stepwise discriminant analysis to select the average number of neighbors and the shape of the individual microcalcification, but excluded cluster eccentricity. A classification scheme assigned the average number of neighbors a weighting factor, which was 1.49 times greater than that assigned to the shape factor of the individual microcalcification. A scheme based only on these two features yielded an ROC curve with an area under the curve (A(z)) of 0.87, indicating a positive predictive value of 61% for 98% sensitivity.
Computerized analysis permitted calculations reflecting the shape of individual microcalcification and the geometry of clusters of microcalcifications. For the computerized classification scheme studied, the cluster geometry was more effective in differentiating benign from malignant clusters than was the shape of individual microcalcification.
本研究的目的是在乳腺X线计算机辅助诊断系统中，比较反映簇状微钙化几何形状的特征与反映单个微钙化形状的特征的诊断作用。
对324例经活检证实的簇状微钙化病例进行数字化处理，分辨率为42微米，并在计算机系统上进行分析。计算每个微钙化的形状因子和邻域数量，同时计算簇的偏心率。形状因子与单个微钙化有关；邻域平均数量和簇偏心率反映簇的几何形状。采用逐步判别分析来评估提取特征在预测恶性肿瘤方面的贡献。通过受试者操作特征（ROC）分析评估基于逐步判别分析选择的特征的分类器性能。
为了获得最佳判别模型，我们使用逐步判别分析选择邻域平均数量和单个微钙化的形状，但排除了簇偏心率。一种分类方案为邻域平均数量分配了一个权重因子，该因子比分配给单个微钙化形状因子的权重因子大1.49倍。仅基于这两个特征的方案产生的ROC曲线下面积（A(z)）为0.87，表明在98%的灵敏度下阳性预测值为61%。
计算机分析允许进行反映单个微钙化形状和微钙化簇几何形状的计算。对于所研究的计算机分类方案，簇的几何形状在区分良性和恶性簇方面比单个微钙化的形状更有效。
