Modeling correlated pairs of mammogram images.
Modeling correlated pairs of mammogram images.
复制标题
对相关的乳房 X 光图像对进行建模。
DOI：
10.1002/sim.10002
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发表时间：
2024
影响因子：
2
通讯作者：
Colditz,GrahamA
中科院分区：
文献类型：
--
作者：
Jiang,Shu;Colditz,GrahamA
乳房X光检查仍然是乳腺癌的主要筛查策略，乳腺癌仍然是全球女性中最常见的癌症诊断。因为筛查乳房X光照片同时捕捉到了左乳房和右乳房，所以这对图像之间存在着不可忽视的相关性。以往的研究探讨了图像配准后图像对之间的平均概念，但在直接利用图像对的问题上还没有进行比较。在本文中，我们将二元泛函主成分分析扩展到三角剖分上，以联合表征限定在不规则区域内的两个成像数据，然后将所提取的特征嵌套在生存模型中以预测乳腺癌的发生。该方法被应用于我们来自Siteman癌症中心Joanne Knight乳房健康队列的激励数据。我们的发现表明，在对图像对平均和对两个图像联合建模之间，模型识别性能在统计上没有显著差异。虽然乳腺癌研究没有发现任何显著的差异，但值得注意的是，这里提出的方法可以很容易地扩展到其他涉及配对或多变量成像数据的研究。
Mammography remains the primary screening strategy for breast cancer, which continues to be the most prevalent cancer diagnosis among women globally. Because screening mammograms capture both the left and right breast, there is a nonnegligible correlation between the pair of images. Previous studies have explored the concept of averaging between the pair of images after proper image registration; however, no comparison has been made in directly utilizing the paired images. In this paper, we extend the bivariate functional principal component analysis over triangulations to jointly characterize the pair of imaging data bounded in an irregular domain and then nest the extracted features within the survival model to predict the onset of breast cancer. The method is applied to our motivating data from the Joanne Knight Breast Health Cohort at Siteman Cancer Center. Our findings indicate that there was no statistically significant difference in model discrimination performance between averaging the pair of images and jointly modeling the two images. Although the breast cancer study did not reveal any significant difference, it is worth noting that the methods proposed here can be readily extended to other studies involving paired or multivariate imaging data.