Diagnostic architectural and dynamic features at breast MR imaging:: Multicenter study
Diagnostic architectural and dynamic features at breast MR imaging:: Multicenter study
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DOI：
10.1148/radiol.2381042117
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发表时间：
2006-01-01
期刊：
影响因子：
19.7
通讯作者：
Gatsonis, CA
中科院分区：
文献类型：
--
作者：
Schnall, MD;Blume, J;Gatsonis, CA
目的：前瞻性地确定三维（3D）和动态乳房磁共振（MR）成像和造影剂动力学特征单独作为预测诊断模型的一部分的患病率和预测价值。材料和方法：研究方案经所有参与机构的机构审查委员会或伦理委员会批准，并获得所有参与者的知情同意。虽然研究数据收集是在HIPAA生效之前进行的，但遵循了符合HIPAA的标准。数据来自国际乳腺磁共振协会试验6883被用于分析。女性接受3D（最小空间分辨率，0.7 x 1.4 x 3mm；最小时间分辨率，4分钟）和动态二维（时间分辨率，15秒）磁共振成像检查。读者对增强形状、增强分布、边界结构、增强强度、边缘增强或内部分隔的存在以及造影剂动力学曲线的形状进行了评分。对每个特征分别进行回归，并对相关的乳房x光检查结果进行调整。多元模型也由多个架构和动态特征构建。对所有模型的受试者工作特征曲线下的面积（A(z)值）进行估计。结果：854名有病理资料的女性（平均年龄53岁+/- 12岁[标准差]；范围18 - 80岁）中有995个病变。没有增强与88%的癌症阴性预测值相关。动态增强模式的定性表征与所有病变结构的Az值为0.66相关。病灶肿块边缘（A(z) = 0.76）和信号强度（A(z) = 0.70）是高度预测的影像学特征。建立多变量模型，A(z)值为0.880。结论：乳腺磁共振成像的结构特征和动态特征对判读具有重要意义。涉及特征评估的多变量模型的诊断准确性优于动态增强模式的定性表征。(c) RSNA， 2006年。
Purpose: To prospectively determine the prevalence and predictive value of three-dimensional (3D) and dynamic breast magnetic resonance (MR) imaging and contrast material kinetic features alone and as part of predictive diagnostic models.Materials and Methods: The study protocol was approved by the institutional review board or ethics committees of all participating institutions, and informed consent was obtained from all participants. Although study data collection was performed before HIPAA went into effect, standards that would be compliant with HIPAA were adhered to. Data from the International Breast MR Consortium trial 6883 were used in the analysis. Women underwent 3D ( minimum spatial resolution, 0.7 x 1.4 x 3 mm; minimal temporal resolution, 4 minutes) and dynamic two-dimensional ( temporal resolution, 15 seconds) MR imaging examinations. Readers rated enhancement shape, enhancement distribution, border architecture, enhancement intensity, presence of rim enhancement or internal septations, and the shape of the contrast material kinetic curve. Regression was performed for each feature individually and after adjustment for associated mammographic findings. Multivariate models were also constructed from multiple architectural and dynamic features. Areas under the receiver operating characteristic curve (A(z) values) were estimated for all models.Results: There were 995 lesions in 854 women ( mean age, 53 years +/- 12 [standard deviation]; range, 18 - 80 years) for whom pathology data were available. The absence of enhancement was associated with an 88% negative predictive value for cancer. Qualitative characterization of the dynamic enhancement pattern was associated with an Az value of 0.66 across all lesion architectures. Focal mass margins (A(z) = 0.76) and signal intensity (A(z) = 0.70) were highly predictive imaging features. Multivariate models were constructed with an A(z) value of 0.880.Conclusion: Architectural and dynamic features are important in breast MR imaging interpretation. Multivariate models involving feature assessment have a diagnostic accuracy superior to that of qualitative characterization of the dynamic enhancement pattern. (c) RSNA, 2006.