Markey Mia K, Lo Joseph Y, Floyd Carey E
Department of Biomedical Engineering and Radiology, Digital Imaging Research Division, Duke University Medical Center, DUMC 3302, Durham, NC 27710, USA.
Radiology. 2002 May;223(2):489-93. doi: 10.1148/radiol.2232011257.
To compare the performance of a computer-aided diagnosis (CAD) system for diagnosis of previously detected lesions, based on radiologist-extracted findings on masses and calcifications.
A feed-forward, back-propagation artificial neural network (BP-ANN) was trained in a round-robin (leave-one-out) manner to predict biopsy outcome from mammographic findings (according to the Breast Imaging Reporting and Data System) and patient age. The BP-ANN was trained by using a large (>1,000 cases) heterogeneous data set containing masses and microcalcifications. The performances of the BP-ANN on masses and microcalcifications were compared with use of receiver operating characteristic analysis and a z test for uncorrelated samples.
The BP-ANN performed significantly better on masses than microcalcifications in terms of both the area under the receiver operating characteristic curve and the partial receiver operating characteristic area index. A similar difference in performance was observed with a second model (linear discriminant analysis) and also with a second data set from a similar institution.
Masses and calcifications should be considered separately when evaluating CAD systems for breast cancer diagnosis.
基于放射科医生提取的肿块和钙化灶的影像特征，比较计算机辅助诊断（CAD）系统对先前检测到的病变的诊断性能。
采用循环（留一法）方式训练前馈反向传播人工神经网络（BP-ANN），以根据乳腺钼靶影像特征（按照乳腺影像报告和数据系统）及患者年龄预测活检结果。使用一个包含肿块和微钙化灶的大型（>1000例）异质性数据集对BP-ANN进行训练。采用受试者操作特征分析和非相关样本的z检验，比较BP-ANN对肿块和微钙化灶的诊断性能。
就受试者操作特征曲线下面积和部分受试者操作特征面积指数而言，BP-ANN对肿块的诊断性能显著优于微钙化灶。在第二个模型（线性判别分析）以及来自类似机构的第二个数据集中也观察到了类似的性能差异。
在评估用于乳腺癌诊断的CAD系统时，应分别考虑肿块和钙化灶。
