Identifying regions of interest in mammogram images.
Identifying regions of interest in mammogram images.
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DOI：
10.1177/09622802231160551
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发表时间：
2023-05
影响因子：
2.3
通讯作者：
Colditz, Graham A. A.
中科院分区：
文献类型：
--
作者：
Jiang, Shu;Cao, Jiguo;Colditz, Graham A. A.
筛查乳房X光检查是早期发现乳腺癌的主要预防策略，也是预测乳腺癌风险和应用预防/风险管理指南的必要投入。因此，确定乳房X光检查图像中与5年或10年乳腺癌风险相关的感兴趣区域具有临床意义。乳房X光照片中乳房区域的半圆形区域所引起的不规则边界问题使问题变得复杂。在识别感兴趣区域时，适应不规则区域尤其重要，因为真正的信号只来自乳房区域的半圆形区域，而其他地方的噪声。我们通过引入一个比例风险模型来解决这些挑战，该模型的成像预报器以三角剖分上的双变量样条线为特征。模型的稀疏性是用群体套索惩罚函数来加强的。我们将所提出的方法应用于激励乔安妮·奈特乳房健康队列，以说明重要的风险模式，并表明所提出的方法能够获得更高的区分性性能。
Screening mammography is the primary preventive strategy for early detection of breast cancer and an essential input to breast cancer risk prediction and application of prevention/risk management guidelines. Identifying regions of interest within mammogram images that are associated with 5- or 10-year breast cancer risk is therefore clinically meaningful. The problem is complicated by the irregular boundary issue posed by the semi-circular domain of the breast area within mammograms. Accommodating the irregular domain is especially crucial when identifying regions of interest, as the true signal comes only from the semi-circular domain of the breast region, and noise elsewhere. We address these challenges by introducing a proportional hazards model with imaging predictors characterized by bivariate splines over triangulation. The model sparsity is enforced with the group lasso penalty function. We apply the proposed method to the motivating Joanne Knight Breast Health Cohort to illustrate important risk patterns and show that the proposed method is able to achieve higher discriminatory performance.
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