Full field digital mammography and breast density: comparison of calibrated and noncalibrated measurements.
Full field digital mammography and breast density: comparison of calibrated and noncalibrated measurements.
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DOI：
10.1016/j.acra.2011.07.011
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发表时间：
2011-11
影响因子：
4.8
通讯作者：
Flowers, Chris I.
中科院分区：
文献类型：
--
作者：
Heine, John J.;Fowler, E. E.;Flowers, Chris I.
乳腺密度是乳腺癌的一个重要和广泛接受的危险因素。在乳房X线摄影报告中说明乳房密度已成为许多国家的一项要求。然而，在放射科医生对乳腺密度的解释中，观察者之间存在显着的差异。一个设计合理的自动化系统可以在保持一致性和可重复性方面提供好处。我们开发了一种新的自动化和校准的测量乳腺密度使用全视野数字乳腺X射线摄影（FFDM）。这种新的测量方法评估了乳房X光片中的空间变化，并在一项小型研究中与乳腺癌产生了显着的关联。这种自动化的成本是在研究处理之前进行高级图像和数据分析的延迟。我们使用比以前更大的数据集来评估这个新的校准变异度量。我们还探索了从未经处理的（原始数据）乳房X线照片开发自动测量的可能性，作为这种校准的乳房密度测量的近似值。一项病例对照研究包括160例病例和160例对照，年龄、筛查史和激素替代治疗相匹配，用于比较校准的乳腺密度变异测量值与非校准的空间变异测量值的三种变体。使用操作员辅助的乳腺密度测量（PD）百分比作为标准参考进行比较。比值比（OR）四分位数分析用于比较这些措施。应用线性回归分析来评估校准对原始像素分布的影响。所有乳腺密度测量均显示与乳腺癌有显著相关性。校准的空间变异测量产生了最强的关联[OR：1.0（参考文献），4.6、4.3、7.4]。相比之下，PD的相关性降低[OR：1.0（参考文献），2.7、2.9、5.2]。另外两个受区域大小限制的非校准测量也显示出显著相关性[OR：1.0（参考文献），2.9，4.4，5.4]和[OR：1.0（参考），3.5、3.1、4.9]。回归分析表明，原始图像的平均值是校准的影响比它的标准偏差。乳腺密度测量可以自动化。相关校准产生的风险信息无法从原始数据表示中检索。虽然校准测量产生更强的关联，但在进一步评估后，非校准测量可以提供PD和其他基于操作员的方法的替代方案，因为它们可以用简单的处理算法自动实现。
Mammographic breast density is an important and widely accepted risk factor for breast cancer. A statement about breast density in the mammographic report is becoming a requirement in many States. However, there is significant inter-observer variation between radiologists in their interpretation of breast density. A properly designed automated system could provide benefits in maintaining consistency and reproducibility. We have developed a new automated and calibrated measure of breast density using full field digital mammography (FFDM). This new measure assesses spatial variation within a mammogram and produced significant associations with breast cancer in a small study. The costs of this automation are delays from advanced image and data analyses before the study can be processed. We evaluated this new calibrated variation measure using a larger dataset than previously. We also explored the possibility of developing an automated measure from unprocessed (raw data) mammograms as an approximation for this calibrated breast density measure. A case-control study comprised of 160 cases and 160 controls matched by age, screening history, and hormone replacement therapy was used to compare the calibrated variation measure of breast density with three variants of a non-calibrated measure of spatial variation. The operator-assisted percentage of breast density measure (PD) was used as a standard reference for comparison. Odds ratio (OR) quartile analysis was used to compare these measures. Linear regression analysis was applied to assess the calibration’s impact on the raw pixel distribution. All breast density measures showed significant breast cancer associations. The calibrated spatial variation measure produced the strongest associations [OR: 1.0 (ref.), 4.6, 4.3, 7.4]. The associations for PD were diminished in comparison [OR: 1.0 (ref.), 2.7, 2.9, 5.2]. Two additional non-calibrated measures restricted in region size also showed significant associations [OR: 1.0 (ref.), 2.9, 4.4, 5.4], and [OR: 1.0 (ref.), 3.5, 3.1, 4.9]. Regression analyses indicated the raw image mean is influenced by the calibration more so than its standard deviation. Breast density measures can be automated. The associated calibration produced risk information not retrievable from the raw data representation. Although the calibrated measure produced the stronger association, the non-calibrated measures may offer an alternative to PD and other operator based methods after further evaluation, because they can be implemented automatically with a simple processing algorithm.
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影响因子：
10.6
作者：
van Engeland, S;Snoeren, PR;Karssemeijer, N
通讯作者：
Karssemeijer, N
DOI：
10.1158/1055-9965.epi-10-1150
发表时间：
2011-07
期刊：
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
影响因子：
--
作者：
Shepherd JA;Kerlikowske K;Ma L;Duewer F;Fan B;Wang J;Malkov S;Vittinghoff E;Cummings SR
通讯作者：
Cummings SR
影响因子：
3.8
作者：
Heine, JJ;Deans, SR;Clarke, LP
通讯作者：
Clarke, LP
影响因子：
4.8
作者：
Heine, John J.;Cao, Ke;Rollison, Dana E.
通讯作者：
Rollison, Dana E.
影响因子：
3.8
作者：
Mawdsley, Gordon E.;Tyson, Albert H.;Yaffe, Martin J.
通讯作者：
Yaffe, Martin J.