Nakayama Ryohei, Watanabe Ryoji, Namba Kiyoshi, Takeda Kan, Yamamoto Koji, Katsuragawa Shigehiko, Doi Kunio
Department of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, 514-8507, Japan.
Acad Radiol. 2006 Oct;13(10):1219-28. doi: 10.1016/j.acra.2006.07.005.
Our purpose in this study was to investigate the usefulness of follow-up magnification mammograms (i.e., both current and previous magnification mammograms) in a computer-aided diagnosis (CAD) scheme for identifying the histological classification of clustered microcalcifications.
Our database consisted of current and previous magnification mammograms obtained from 93 patients before and after 3-month follow-up: 11 invasive carcinomas, 19 noninvasive carcinomas of the comedo type, 25 noninvasive carcinomas of the noncomedo type, 23 mastopathies, and 15 fibroadenomas. In our CAD scheme, we extracted five objective features of clustered microcalcifications from each of the current and previous magnification mammograms by taking into account image features that experienced radiologists commonly use to identify histological classifications. These features were then merged by a modified Bayes discriminant function for distinguishing among five histological classifications. For the input of the modified Bayes discriminant function, we used five objective features obtained from the previous magnification mammogram (previous features), five objective features obtained from the current magnification mammogram (current features), and the set of the five previous features and the five current features.
The classification accuracies with the five current features were higher than those with the five previous features. These classification accuracies were improved substantially by using the set of the five previous features and the five current features. For the set of the five previous features and the five current features, the classification accuracies of our CAD scheme were 81.8% (9 of 11) for invasive carcinoma, 84.2% (16 of 19) for noninvasive carcinoma of the comedo type, 76.0% (19 of 25) for noninvasive carcinoma of the noncomedo type, 73.9% (17 of 23) for mastopathy, and 86.8% (13 of 15) for fibroadenoma.
Our CAD scheme with use of follow-up magnification mammograms improved classification performance for mammographic clustered microcalcifications.
本研究的目的是探讨在计算机辅助诊断（CAD）方案中，随访放大乳腺X线摄影（即当前和先前的放大乳腺X线摄影）对鉴别成簇微小钙化的组织学分类的有用性。
我们的数据库包括93例患者在3个月随访前后获得的当前和先前的放大乳腺X线摄影：11例浸润性癌、19例粉刺型非浸润性癌、25例非粉刺型非浸润性癌、23例乳腺病和15例纤维腺瘤。在我们的CAD方案中，我们通过考虑经验丰富的放射科医生常用于识别组织学分类的图像特征，从当前和先前的放大乳腺X线摄影中提取成簇微小钙化的五个客观特征。然后，通过改进的贝叶斯判别函数将这些特征合并，以区分五种组织学分类。对于改进的贝叶斯判别函数的输入，我们使用从先前放大乳腺X线摄影获得的五个客观特征（先前特征）、从当前放大乳腺X线摄影获得的五个客观特征（当前特征）以及五个先前特征和五个当前特征的集合。
使用五个当前特征的分类准确率高于使用五个先前特征的分类准确率。通过使用五个先前特征和五个当前特征的集合，这些分类准确率得到了显著提高。对于五个先前特征和五个当前特征的集合，我们的CAD方案对浸润性癌的分类准确率为81.8%（11例中的9例），对粉刺型非浸润性癌的分类准确率为84.2%（19例中的16例），对非粉刺型非浸润性癌的分类准确率为76.0%（25例中的19例），对乳腺病的分类准确率为73.9%（23例中的17例），对纤维腺瘤的分类准确率为86.8%（15例中的13例）。
我们使用随访放大乳腺X线摄影的CAD方案提高了乳腺X线摄影成簇微小钙化的分类性能。
