Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.
Identification of intrinsic imaging phenotypes for breast cancer tumors: preliminary associations with gene expression profiles.
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DOI：
10.1148/radiol.14131375
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发表时间：
2014-08
期刊：
影响因子：
19.7
通讯作者：
Kontos D
中科院分区：
文献类型：
--
作者：
Ashraf AB;Daye D;Gavenonis S;Mies C;Feldman M;Rosen M;Kontos D
提出一种识别乳腺癌肿瘤内在成像表型并研究其与预后基因表达谱的关联的方法。作者回顾性分析了 2005 年至 2010 年间诊断为雌激素受体阳性乳腺癌的 56 名女性（平均年龄 55.6 岁；年龄范围 37-74 岁）的乳腺动态对比材料增强 (DCE) 磁共振 (MR) 图像。该研究得到了机构审查委员会的批准并符合 HIPAA。免除了获得知情同意的要求。使用经过验证的基因表达测定对原发性肿瘤进行测定，该测定提供复发可能性的评分。通过使用定量形态、动力学和空间异质性特征，为每个肿瘤提取多参数成像表型向量。进行多元线性回归来测试 DCE MR 成像特征与复发可能性之间的关联。为了识别内在成像表型，对提取的特征向量进行层次聚类。多变量逻辑回归用于对肿瘤复发风险进行高风险与低风险或中等风险的分类。为了确定内在表型的附加价值，将表型类别作为附加变量进行测试。接受者操作特征分析和接受者操作特征曲线下面积（Az）用于评估分类性能。 DCE MR 成像特征与复发评分之间存在中等相关性（r = 0.71，R2 = 0.50，P < .001）。 DCE MR 成像特征可预测替代试验确定的复发风险，Az 为 0.77 (P < .01)。检测到四种主要成像表型，其中两种仅包括低风险和中风险肿瘤。当表型类别用作附加变量时，Az 增加至 0.82 (P < .01)。乳腺癌肿瘤存在固有的成像表型，并且与基因表达谱确定的复发可能性相关。这些成像生物标志物最终可以帮助指导治疗决策。
To present a method for identifying intrinsic imaging phenotypes in breast cancer tumors and to investigate their association with prognostic gene expression profiles. The authors retrospectively analyzed dynamic contrast material–enhanced (DCE) magnetic resonance (MR) images of the breast in 56 women (mean age, 55.6 years; age range, 37–74 years) diagnosed with estrogen receptor–positive breast cancer between 2005 and 2010. The study was approved by the institutional review board and compliant with HIPAA. The requirement to obtain informed consent was waived. Primary tumors were assayed with a validated gene expression assay that provides a score for the likelihood of recurrence. A multiparametric imaging phenotype vector was extracted for each tumor by using quantitative morphologic, kinetic, and spatial heterogeneity features. Multivariate linear regression was performed to test associations between DCE MR imaging features and recurrence likelihood. To identify intrinsic imaging phenotypes, hierarchical clustering was performed on the extracted feature vectors. Multivariate logistic regression was used to classify tumors at high versus low or medium risk of recurrence. To determine the additional value of intrinsic phenotypes, the phenotype category was tested as an additional variable. Receiver operating characteristic analysis and the area under the receiver operating characteristic curve (Az) were used to assess classification performance. There was a moderate correlation (r = 0.71, R2 = 0.50, P < .001) between DCE MR imaging features and the recurrence score. DCE MR imaging features were predictive of recurrence risk as determined by the surrogate assay, with an Az of 0.77 (P < .01). Four dominant imaging phenotypes were detected, with two including only low- and medium-risk tumors. When the phenotype category was used as an additional variable, the Az increased to 0.82 (P < .01). Intrinsic imaging phenotypes exist for breast cancer tumors and correlate with recurrence likelihood as determined with gene expression profiling. These imaging biomarkers could ultimately help guide treatment decisions.