Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study.
Relationships between computer-extracted mammographic texture pattern features and BRCA1/2 mutation status: a cross-sectional study.
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DOI：
10.1186/s13058-014-0424-8
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Giger ML
中科院分区：
文献类型：
--
作者：
Gierach GL;Li H;Loud JT;Greene MH;Chow CK;Lan L;Prindiville SA;Eng-Wong J;Soballe PW;Giambartolomei C;Mai PL;Galbo CE;Nichols K;Calzone KA;Olopade OI;Gail MH;Giger ML
患有散发性或BRCA1/2相关乳腺癌风险的女性的乳房X光照相密度相似。有人提出，数字化的乳房X光摄影图像在实质模式中包含计算机可提取的信息，这可能有助于区分BRCA1/2突变携带者和非携带者。我们比较了患有有害BRCA1/2突变的女性(n=137)和非携带者(n=100)女性数字化乳房X光摄影中的纹理特征。受试者被分成训练(107名携带者，70名非携带者)和测试(30名携带者，30名非携带者)数据集。在每个受试者的数字化乳房X光照片中，从感兴趣的乳晕后区域提取纹理特征，以屏蔽突变状态。训练数据集的逐步线性回归分析标识了要包括在旨在区分BRCA1/2携带者和非携带者的放射图像纹理分析(RTA)分类器模型中的变量。选择的特征使用贝叶斯人工神经网络(Bann)算法进行组合，该算法产生一个概率分数，对每个受试者属于突变阳性组的可能性进行评级。这些概率分数在独立的测试数据集中进行评估，以确定它们在BRCA1/2突变携带者和非携带者之间的分布是否不同。通过对接收机工作特性的分析，估计了模型的识别能力。在测试数据集中，来自Bann训练的分类器的概率分数每增加一个标准差(SD)，预测BRCA1/2突变状态的几率增加两倍：未调整的优势比(OR)=2.00，95%可信区间(CI)：1.59，2.51，P=0.02；年龄调整后的OR=1.93，95%CI：1.53，2.42，P=0.03。对乳房X光摄影密度百分比的额外调整对OR几乎没有影响。Bann训练的分类器用于区分BRCA1/2突变携带者和非携带者的曲线下面积为0.68，特征加乳房X光摄影密度百分比为0.72。我们的发现表明，与百分比乳房X光照相密度不同，计算机提取的乳房X光照相纹理模式特征与携带BRCA1/2突变有关。虽然仍处于早期阶段，但我们的新型RTA分类器通过允许在接受筛查乳房X光检查的女性中进行实时风险分层，具有改善乳房X光图像解释的潜力。本文的在线版本(doi：10.1186/s13058-0140424-8)包含补充材料，授权用户可以使用。
Mammographic density is similar among women at risk of either sporadic or BRCA1/2-related breast cancer. It has been suggested that digitized mammographic images contain computer-extractable information within the parenchymal pattern, which may contribute to distinguishing between BRCA1/2 mutation carriers and non-carriers. We compared mammographic texture pattern features in digitized mammograms from women with deleterious BRCA1/2 mutations (n = 137) versus non-carriers (n = 100). Subjects were stratified into training (107 carriers, 70 non-carriers) and testing (30 carriers, 30 non-carriers) datasets. Masked to mutation status, texture features were extracted from a retro-areolar region-of-interest in each subject’s digitized mammogram. Stepwise linear regression analysis of the training dataset identified variables to be included in a radiographic texture analysis (RTA) classifier model aimed at distinguishing BRCA1/2 carriers from non-carriers. The selected features were combined using a Bayesian Artificial Neural Network (BANN) algorithm, which produced a probability score rating the likelihood of each subject’s belonging to the mutation-positive group. These probability scores were evaluated in the independent testing dataset to determine whether their distribution differed between BRCA1/2 mutation carriers and non-carriers. A receiver operating characteristic analysis was performed to estimate the model’s discriminatory capacity. In the testing dataset, a one standard deviation (SD) increase in the probability score from the BANN-trained classifier was associated with a two-fold increase in the odds of predicting BRCA1/2 mutation status: unadjusted odds ratio (OR) = 2.00, 95% confidence interval (CI): 1.59, 2.51, P = 0.02; age-adjusted OR = 1.93, 95% CI: 1.53, 2.42, P = 0.03. Additional adjustment for percent mammographic density did little to change the OR. The area under the curve for the BANN-trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers was 0.68 for features alone and 0.72 for the features plus percent mammographic density. Our findings suggest that, unlike percent mammographic density, computer-extracted mammographic texture pattern features are associated with carrying BRCA1/2 mutations. Although still at an early stage, our novel RTA classifier has potential for improving mammographic image interpretation by permitting real-time risk stratification among women undergoing screening mammography. The online version of this article (doi:10.1186/s13058-014-0424-8) contains supplementary material, which is available to authorized users.
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影响因子：
3.8
作者：
Kataoka, Masako;Antoniou, Antonis;Easton, Doug
通讯作者：
Easton, Doug
影响因子：
4.4
作者：
Li, Hui;Giger, Maryellen L.;Chinander, Michael R.
通讯作者：
Chinander, Michael R.
使用通用纹理特征来表征乳腺 X 光图像。
DOI：
10.1186/bcr3163
发表时间：
2012-04-10
期刊：
Breast cancer research : BCR
影响因子：
--
作者：
Häberle L;Wagner F;Fasching PA;Jud SM;Heusinger K;Loehberg CR;Hein A;Bayer CM;Hack CC;Lux MP;Binder K;Elter M;Münzenmayer C;Schulz-Wendtland R;Meier-Meitinger M;Adamietz BR;Uder M;Beckmann MW;Wittenberg T
通讯作者：
Wittenberg T
影响因子：
3.8
作者：
Gierach, Gretchen L.;Loud, Jennifer T.;Greene, Mark H.
通讯作者：
Greene, Mark H.
影响因子：
6.2
作者：
Hoskins, Kent F.;Zwaagstra, Alice;Ranz, Michael
通讯作者：
Ranz, Michael