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全视野数字化乳腺钼靶片中恶性与良性钙化灶计算机分类的独立评估

Independent evaluation of computer classification of malignant and benign calcifications in full-field digital mammograms.

作者信息

Rana Rich S, Jiang Yulei, Schmidt Robert A, Nishikawa Robert M, Liu Bei

机构信息

Department of Radiology, The University of Chicago, 5841 South Maryland Avenue, Chicago, IL 60637, USA.

出版信息

Acad Radiol. 2007 Mar;14(3):363-70. doi: 10.1016/j.acra.2006.12.012.

Abstract

RATIONALE AND OBJECTIVES

To evaluate whether a computer-aided diagnosis (CADx) technique can accurately classify breast calcifications in full-field digital mammograms (FFDMs) as malignant or benign. The computer technique was developed previously on screen-film mammograms (SFMs) in which individual calcifications were identified manually. The present study evaluated the computer technique independently on a new database of FFDM images with automatic detection of the individual calcifications.

MATERIALS AND METHODS

We analyzed 49 consecutive FFDM cases (19 cancers) that showed suspicious calcifications. Four mammography radiologists read soft-copy mammograms retrospectively and electronically indicated the region of calcifications in each image. The computer then automatically detected the individual calcifications within the indicated region and analyzed eight features of calcification morphology and distribution to arrive at an estimated likelihood of malignancy. The radiologists entered Breast Imaging Report and Data System assessments before and after seeing the computer results. Performance was analyzed using receiver operating characteristic analysis.

RESULTS

Despite variability in radiologist-indicated regions of calcifications, the computer achieved consistently high performance taking input from the four radiologists (receiver operating characteristic curve area, A(z): 0.80, 0.80, 0.78, and 0.77; differences not statistically significant). Previous results showed that the computer technique achieved an A(z) value of 0.80 on SFMs, which improved radiologists' performance significantly.

CONCLUSIONS

The computer technique appears to maintain consistently high performance in classifying calcifications in FFDMs as malignant or benign without requiring substantial modification from its initial development on SFMs. The computer performance appears to be robust with respect to variations in radiologists' input.

摘要

原理与目的

评估一种计算机辅助诊断(CADx)技术能否准确将全视野数字化乳腺钼靶摄影(FFDM)中的乳腺钙化灶分类为恶性或良性。该计算机技术先前是在屏-片乳腺钼靶摄影(SFM)上开发的,其中单个钙化灶是手动识别的。本研究在一个具有自动检测单个钙化灶功能的FFDM图像新数据库上独立评估了该计算机技术。

材料与方法

我们分析了49例连续的FFDM病例(19例癌症),这些病例显示有可疑钙化灶。四位乳腺钼靶放射科医生回顾性地阅读软拷贝乳腺钼靶图像,并以电子方式指出每张图像中钙化灶的区域。然后计算机自动检测指定区域内的单个钙化灶,并分析钙化灶形态和分布的八个特征,以得出恶性可能性的估计值。放射科医生在查看计算机结果前后输入乳腺影像报告和数据系统评估。使用受试者操作特征分析来分析性能。

结果

尽管放射科医生指出的钙化灶区域存在差异,但计算机从四位放射科医生的输入中获得了一致的高性能(受试者操作特征曲线面积,A(z):0.80、0.80、0.78和0.77;差异无统计学意义)。先前的结果表明,该计算机技术在SFM上的A(z)值为0.80,这显著提高了放射科医生的性能。

结论

该计算机技术在将FFDM中的钙化灶分类为恶性或良性方面似乎保持了一致的高性能,无需对其最初在SFM上的开发进行实质性修改。计算机性能在放射科医生输入的变化方面似乎很稳健。

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