Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment
Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment
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DOI：
10.1148/radiol.2018180179
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发表时间：
2019-01-01
期刊：
影响因子：
19.7
通讯作者：
Vachon, Celine M.
中科院分区：
文献类型：
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作者：
Kontos, Despina;Winham, Stacey J.;Vachon, Celine M.
目的：通过使用放射组学特征识别乳房X线摄影实质复杂性的表型，并评估其与乳腺密度和其他乳腺癌危险因素的相关性。材料和方法：使用计算机图像分析对2012年9月1日至2013年2月28日期间接受数字乳腺X射线摄影筛查的女性横截面样本的乳腺密度进行量化，并提取实质纹理特征。2013年（n = 2029;年龄范围，35-75岁;平均年龄，55.9岁）。无监督聚类被应用于在单独的训练集（n = 1339）和测试集（n = 690）中识别和再现实质复杂性的表型。采用Fisher精确检验、卡方检验和Kruskal-Wallis检验评估了不同年龄、体重指数、乳腺密度和估计乳腺癌风险的表型差异。条件Logistic回归被用来评估检测到的表型和乳腺癌之间的初步关联，在一个独立的病例对照样本（76名妇女诊断为乳腺癌和158名对照参与者）匹配的年龄。结果：无监督聚类筛选样本中确定了四个表型与增加实质的复杂性，是可重复的训练和测试集之间（P=.001）。乳腺密度与表型类别没有很强的相关性（线性趋势的R-2=0.24）。低至中等复杂性表型（患病率，390/2029 [19%]）的致密乳腺比例最低（8/390 [2.1%]），而在其他表型中观察到相似的比例（从高复杂性表型中的140/291 [48.1%]到低复杂性表型中的275/511 [53.8%]）。在独立的病例对照样本中，表型与乳腺癌有显著相关性（P=.001），当添加到具有乳房密度和体重指数的模型中时，导致更高的辨别能力（曲线下面积，0.84 vs 0.80; P= 0.03用于比较）。放射组学表型捕获了超出常规乳腺密度测量和已确定的乳腺癌风险因素的乳腺摄影实质复杂性。(c)RSNA，2018
Purpose: To identify phenotypes of mammographic parenchymal complexity by using radiomic features and to evaluate their associations with breast density and other breast cancer risk factors.Materials and Methods: Computerized image analysis was used to quantify breast density and extract parenchymal texture features in a cross-sectional sample of women screened with digital mammography from September 1, 2012, to February 28, 2013 (n = 2029; age range, 35-75 years; mean age, 55.9 years). Unsupervised clustering was applied to identify and reproduce phenotypes of parenchymal complexity in separate training (n = 1339) and test sets (n = 690). Differences across phenotypes by age, body mass index, breast density, and estimated breast cancer risk were assessed by using Fisher exact, chi(2), and Kruskal-Wallis tests. Conditional logistic regression was used to evaluate preliminary associations between the detected phenotypes and breast cancer in an independent case-control sample (76 women diagnosed with breast cancer and 158 control participants) matched on age.Results: Unsupervised clustering in the screening sample identified four phenotypes with increasing parenchymal complexity that were reproducible between training and test sets (P=.001). Breast density was not strongly correlated with phenotype category (R-2=0.24 for linear trend). The low-to intermediate-complexity phenotype (prevalence, 390 of 2029 [19%]) had the lowest proportion of dense breasts (eight of 390 [2.1%]), whereas similar proportions were observed across other phenotypes (from 140 of 291 [48.1%] in the high-complexity phenotype to 275 of 511 [53.8%] in the low-complexity phenotype). In the independent case-control sample, phenotypes showed a significant association with breast cancer (P=.001), resulting in higher discriminatory capacity when added to a model with breast density and body mass index (area under the curve, 0.84 vs 0.80; P=.03 for comparison).Conclusion: Radiomic phenotypes capture mammographic parenchymal complexity beyond conventional breast density measures and established breast cancer risk factors. (c) RSNA, 2018