Leichter I, Lederman R, Bamberger P, Novak B, Fields S, Buchbinder S S
Department of Electro-Optics, Jerusalem College of Technology, Israel.
Invest Radiol. 1999 Jun;34(6):394-400. doi: 10.1097/00004424-199906000-00002.
Mammography is relatively nonspecific for the early detection of breast cancer. This study evaluates the accuracy of mammographic interpretation using quantitative features characterizing microcalcifications, which are extracted by a computerized system.
A computer-aided diagnosis (CAD) system enabling digitization of film-screen mammograms and automatic feature extraction was developed. A classification scheme (discriminant analysis) based on these features was constructed and trained on 217 cases with known pathology. The diagnostic performance of the classification scheme was tested against the radiologist's conventional interpretation on 45 additional cases of microcalcifications, each analyzed independently by four radiologists.
The sensitivity of the CAD system analysis (95.7%) was significantly better than that of conventional interpretation (84.8%). The positive predictive value of interpretation increased significantly, as did the area under the receiver operating characteristic curve.
This classification scheme for microcalcifications, based on quantitative features characterizing the lesion, significantly improved the accuracy of mammographic interpretation.
乳腺钼靶摄影对于早期乳腺癌的检测相对缺乏特异性。本研究使用计算机系统提取的表征微钙化的定量特征来评估乳腺钼靶摄影解读的准确性。
开发了一种计算机辅助诊断(CAD)系统,该系统能够对胶片-屏片乳腺钼靶图像进行数字化处理并自动提取特征。基于这些特征构建了一种分类方案(判别分析),并在217例已知病理情况的病例上进行训练。该分类方案的诊断性能在另外45例微钙化病例中与放射科医生的传统解读进行对比测试,每位放射科医生对每个病例独立进行分析。
CAD系统分析的敏感性(95.7%)显著优于传统解读(84.8%)。解读的阳性预测值显著提高,受试者操作特征曲线下面积也显著增加。
这种基于表征病变的定量特征的微钙化分类方案显著提高了乳腺钼靶摄影解读的准确性。