Elter M, Schulz-Wendtland R, Wittenberg T
Fraunhofer Institute for Integrated Circuits (IIS), Am Wolfsmantel 33, 91058 Erlangen, Germany.
Med Phys. 2007 Nov;34(11):4164-72. doi: 10.1118/1.2786864.
Mammography is the most effective method for breast cancer screening available today. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. To reduce the high number of unnecessary breast biopsies, several computer-aided diagnosis (CAD) systems have been proposed in the last several years. These systems help physicians in their decision to perform a breast biopsy on a suspicious lesion seen in a mammogram or to perform a short term follow-up examination instead. We present two novel CAD approaches that both emphasize an intelligible decision process to predict breast biopsy outcomes from BI-RADS findings. An intelligible reasoning process is an important requirement for the acceptance of CAD systems by physicians. The first approach induces a global model based on decison-tree learning. The second approach is based on case-based reasoning and applies an entropic similarity measure. We have evaluated the performance of both CAD approaches on two large publicly available mammography reference databases using receiver operating characteristic (ROC) analysis, bootstrap sampling, and the ANOVA statistical significance test. Both approaches outperform the diagnosis decisions of the physicians. Hence, both systems have the potential to reduce the number of unnecessary breast biopsies in clinical practice. A comparison of the performance of the proposed decision tree and CBR approaches with a state of the art approach based on artificial neural networks (ANN) shows that the CBR approach performs slightly better than the ANN approach, which in turn results in slightly better performance than the decision-tree approach. The differences are statistically significant (p value < 0.001). On 2100 masses extracted from the DDSM database, the CRB approach for example resulted in an area under the ROC curve of A(z) = 0.89 +/- 0.01, the decision-tree approach in A(z) = 0.87 +/- 0.01, and the ANN approach in A(z) = 0.88 +/- 0.01.
乳房X线摄影是目前可用的乳腺癌筛查最有效的方法。然而，乳房X线摄影解读导致的乳房活检低阳性预测值会带来约70%结果为良性的不必要活检。为了减少大量不必要的乳房活检，在过去几年中已经提出了几种计算机辅助诊断（CAD）系统。这些系统帮助医生决定对乳房X线摄影中发现的可疑病变进行乳房活检，还是进行短期随访检查。我们提出了两种新颖的CAD方法，它们都强调了一个可理解的决策过程，以便根据乳腺影像报告和数据系统（BI-RADS）的结果预测乳房活检结果。一个可理解的推理过程是医生接受CAD系统的一项重要要求。第一种方法基于决策树学习诱导出一个全局模型。第二种方法基于案例推理，并应用熵相似性度量。我们使用接收器操作特性（ROC）分析、自助抽样和方差分析统计显著性检验，在两个大型公开可用的乳房X线摄影参考数据库上评估了这两种CAD方法的性能。两种方法都优于医生的诊断决策。因此，这两种系统都有潜力在临床实践中减少不必要的乳房活检数量。将所提出的决策树和基于案例推理（CBR）方法的性能与基于人工神经网络（ANN）的一种先进方法进行比较表明，CBR方法的性能略优于ANN方法，而ANN方法的性能又略优于决策树方法。差异具有统计学显著性（p值<0.001）。例如，在从数字数据库乳腺摄影（DDSM）数据库中提取的2100个肿块上，CBR方法的ROC曲线下面积为A(z)=0.89±0.01，决策树方法为A(z)=0.87±0.01，ANN方法为A(z)=0.88±0.01。
