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基于超声断层图像的乳腺体积密度评估。

Volumetric breast density evaluation from ultrasound tomography images.

作者信息

Glide-Hurst Carri K, Duric Neb, Littrup Peter

机构信息

William Beaumont Hospital, 3601 West Thirteen Mile Road, Royal Oak, Michigan 48073, USA.

出版信息

Med Phys. 2008 Sep;35(9):3988-97. doi: 10.1118/1.2964092.

Abstract

Previous ultrasound tomography work conducted by our group showed a direct correlation between measured sound speed and physical density in vitro, and increased in vivo sound speed with increasing mammographic density, a known risk factor for breast cancer. Building on these empirical results, the purpose of this work was to explore a metric to quantify breast density using our ultrasound tomography sound speed images in a manner analogous to computer-assisted mammogram segmentation for breast density analysis. Therefore, volumetric ultrasound percent density (USPD) is determined by segmenting high sound speed areas from each tomogram using a k-means clustering routine, integrating these results over the entire volume of the breast, and dividing by whole-breast volume. First, a breast phantom comprised of fat inclusions embedded in fibroglandular tissue was scanned four times with both our ultrasound tomography clinical prototype (with 4 mm spatial resolution) and CT. The coronal transmission tomograms and CT images were analyzed using semiautomatic segmentation routines, and the integrated areas of the phantom's fat inclusions were compared between the four repeated scans. The average variability for inclusion segmentation was approximately 7% and approximately2%, respectively, and a close correlation was observed in the integrated areas between the two modalities. Next, a cohort of 93 patients was imaged, yielding volumetric coverage of the breast (45-75 sound speed tomograms/patient). The association of USPD with mammographic percent density (MPD) was evaluated using two measures: (1) qualitative, as determined by a radiologist's visual assessment using BI-RADS Criteria and (2) quantitative, via digitization and semiautomatic segmentation of craniocaudal and mediolateral oblique mammograms. A strong positive association between BI-RADS category and USPD was demonstrated [Spearman rho = 0.69 (p < 0.001)], with significant differences between all BI-RADS categories as assessed by one-way ANOVA and Scheffé posthoc analysis. Furthermore, comparing USPD to calculated mammographic density yielded moderate to strong positive associations for CC and MLO views (r2 = 0.75 and 0.59, respectively). These results support the hypothesis that utilizing USPD as an analogue to mammographic breast density is feasible, providing a nonionizing, whole-breast analysis.

摘要

我们团队之前进行的超声断层扫描研究表明,体外测量的声速与物理密度之间存在直接关联,并且在体内,随着乳腺钼靶密度(已知的乳腺癌风险因素)的增加,声速也会升高。基于这些实证结果,本研究的目的是探索一种度量标准,以类似于计算机辅助乳腺钼靶分割进行乳腺密度分析的方式,利用我们的超声断层扫描声速图像来量化乳腺密度。因此,体积超声百分比密度(USPD)的确定方法是:使用k均值聚类程序从每个断层图像中分割出高声速区域,将这些结果整合到整个乳房体积中,然后除以全乳房体积。首先,用我们的超声断层扫描临床原型(空间分辨率为4毫米)和CT对一个由嵌入纤维腺组织中的脂肪包涵体组成的乳房模型进行了四次扫描。使用半自动分割程序分析冠状面透射断层图像和CT图像,并比较四次重复扫描之间模型脂肪包涵体的积分面积。包涵体分割的平均变异性分别约为7%和约2%,并且在两种模态的积分面积之间观察到密切相关性。接下来,对93名患者进行了成像,获得了乳房的体积覆盖(每位患者45 - 75幅声速断层图像)。使用两种方法评估USPD与乳腺钼靶百分比密度(MPD)之间的关联:(1)定性评估,由放射科医生使用BI-RADS标准进行视觉评估确定;(2)定量评估,通过对头尾位和内外侧斜位乳腺钼靶进行数字化和半自动分割。BI-RADS类别与USPD之间显示出强烈的正相关关系[斯皮尔曼相关系数rho = 0.69(p < 0.001)],通过单因素方差分析和谢费尔事后分析评估,所有BI-RADS类别之间存在显著差异。此外,将USPD与计算出的乳腺钼靶密度进行比较,对于头尾位和内外侧斜位视图产生了中度至强的正相关关系(r2分别为0.75和0.59)。这些结果支持了以下假设:利用USPD作为乳腺钼靶乳房密度的类似物是可行的,可提供一种非电离的全乳房分析。

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