Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.
Reduction of false-positive recalls using a computerized mammographic image feature analysis scheme.
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DOI：
10.1088/0031-9155/59/15/4357
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发表时间：
2014-08-07
影响因子：
3.5
通讯作者：
Zheng B
中科院分区：
文献类型：
--
作者：
Tan M;Pu J;Zheng B
高假阳性回忆率是显著降低乳房x光筛查效果的主要困境之一，它损害了很大一部分妇女并增加了医疗成本。本研究旨在探讨通过开发一种新的计算机辅助诊断（CAD）方案来帮助减少假阳性回忆的可行性，该方案基于从四视图图像中计算的全局乳房x线摄影纹理和密度特征的分析。我们的数据库包括从1052名被召回的女性（669名癌症阳性，383名良性）获得的全域数字乳房x线摄影（FFDM）图像。每个病例有四个图像：两个颅侧（CC）和两个中外侧斜位（MLO）视图。我们的CAD方案首先在四张图像的分割乳腺区域上计算与乳房x线摄影密度分布相关的全局纹理特征。其次，将计算得到的特征提供给两个人工神经网络（ANN）分类器，分别在CC和MLO视图图像上进行10倍交叉验证方案的训练和测试。最后，使用一种新的自适应评分融合方法将两个人工神经网络分类得分组合在一起，该方法自动确定分配给两个视图的最优权重。使用接收器工作特性曲线（AUC）下的面积来测试CAD性能。该四视图CAD方案的AUC=0.793±0.026，在5%的显著性水平上显著高于仅使用CC （p = 0.025）或MLO （p = 0.0004）视图图像时的AUC。本研究表明，对乳房x线图像纹理和密度特征的定量评估可以为召回病例中恶性和良性病例的分类提供有用和/或补充信息，最终有助于降低乳房x线筛查中的假阳性召回率。
The high false-positive recall rate is one of the major dilemmas that significantly reduce the efficacy of screening mammography, which harms a large fraction of women and increases healthcare cost. This study aims to investigate the feasibility of helping reduce false-positive recalls by developing a new computer-aided diagnosis (CAD) scheme based on the analysis of global mammographic texture and density features computed from four-view images. Our database includes full-field digital mammography (FFDM) images acquired from 1052 recalled women (669 positive for cancer and 383 benign). Each case has four images: two craniocaudal (CC) and two mediolateral oblique (MLO) views. Our CAD scheme first computed global texture features related to the mammographic density distribution on the segmented breast regions of four images. Second, the computed features were provided to two artificial neural network (ANN) classifiers that were separately trained and tested in a ten-fold cross-validation scheme on CC and MLO view images, respectively. Finally, two ANN classification scores were combined using a new adaptive scoring fusion method that automatically determined the optimal weights to assign to both views. CAD performance was tested using the area under a receiver operating characteristic curve (AUC). The AUC=0.793±0.026 was obtained for this four-view CAD scheme, which was significantly higher at the 5% significance level than the AUCs achieved when using only CC (p = 0.025) or MLO (p = 0.0004) view images, respectively. This study demonstrates that a quantitative assessment of global mammographic image texture and density features could provide useful and/or supplementary information to classify between malignant and benign cases among the recalled cases, which may eventually help reduce the false-positive recall rate in screening mammography.
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影响因子：
39.2
作者：
Hubbard RA;Kerlikowske K;Flowers CI;Yankaskas BC;Zhu W;Miglioretti DL
通讯作者：
Miglioretti DL
影响因子：
7.7
作者：
Kom, Guillaume;Tiedeu, Alain;Kom, Martin
通讯作者：
Kom, Martin
影响因子：
19.7
作者：
Gur, D;Stalder, JS;Rockette, HE
通讯作者：
Rockette, HE
影响因子：
19.7
作者：
Buist, Diana S. M.;Anderson, Melissa L.;Miglioretti, Diana L.
通讯作者：
Miglioretti, Diana L.
DOI：
10.1093/jnci/djr206
发表时间：
2011-08-01
期刊：
JOURNAL OF THE NATIONAL CANCER INSTITUTE
影响因子：
--
作者：
Fenton, Joshua J.;Abraham, Linn;Barlow, William E.
通讯作者：
Barlow, William E.