Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment
Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment
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DOI：
10.1118/1.4921996
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发表时间：
2015-07-01
期刊：
影响因子：
3.8
通讯作者：
Kontos, Despina
中科院分区：
文献类型：
--
作者：
Zheng, Yuanjie;Keller, Brad M.;Kontos, Despina
目的：乳腺摄影密度百分比（PD%）是乳腺癌的一个重要危险因素。最近的研究还表明，实质纹理特征，这是实质模式的更多颗粒描述符，可以提供有关乳腺癌风险的额外信息。迄今为止，大多数研究都测量了乳房中选定的感兴趣区域（ROI）内的乳房X线摄影纹理，这无法充分捕获整个乳房实质模式的复杂性。为了更好地表征模式的实质组织，作者开发了一个全自动化的软件管道的基础上，一种新的基于网格的策略，以提取一系列实质纹理特征从整个breast region.Methods：数字乳腺X线照片从106例318年龄匹配的对照进行了回顾性分析。基于网格的方法是基于一个规则的网格几乎覆盖在每个乳房X线摄影图像。纹理特征是从交点计算的（即，网格）点，使用以每个网格点为中心的局部窗口。使用这种策略，一系列的统计（灰度直方图，共生，和游程）和结构（边缘增强，局部二进制模式，和分形维数）的功能被提取。为了覆盖整个乳房，用于特征提取的局部窗口的大小被设置为等于晶格网格间距，并且通过评估不同的窗口大小来实验地优化。使用逻辑回归与留一交叉验证评估其基于网格的纹理特征与乳腺癌之间的关联，并进一步与乳腺PD%和从乳晕后或中央乳腺区域提取的常用单ROI纹理特征进行比较。使用受试者工作特征（ROC）的曲线下面积（AUC）评价分类性能。DeLong的测试被用来比较不同的ROC在AUC performance.Results方面：平均单变量性能的格子为基础的功能是更高的提取时，从较小的窗口大小比较大的。虽然不是每个单独的纹理特征都上级乳房PD%（AUC：0.59，STD：0.03），但它们在多变量分析中的组合具有显著更好的性能（AUC：0.85，STD：0.02，p < 0.001）。当从乳晕后或中央乳房区域提取时，基于网格的纹理特征也优于单ROI纹理特征（AUC：0.60-0.74，STD：0.03）。添加乳腺PD%并没有使基于网格的纹理特征或单ROI特征的性能得到显著改善（p > 0.05）。结论：所提出的用于乳腺摄影纹理分析的基于网格的策略能够表征整个乳腺上的实质图案。因此，与目前使用的描述符相比，这些特征提供了更丰富的信息，并可能最终改善乳腺癌风险评估。需要进行更大规模的研究来验证这些发现，并与标准的人口统计学和生殖风险因素进行比较。(C)2015年美国医学物理学家协会。
Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region.Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance.Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05).Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare to standard demographic and reproductive risk factors. (C) 2015 American Association of Physicists in Medicine.