Fowler Erin E E, Hathaway Cassandra, Tillman Fabryann, Weinfurtner Robert, Sellers Thomas A, Heine John
Cancer Epidemiology Department, MCC, Moffitt Cancer Center & Research Institute, 12902 Bruce B. Downs Blvd, Tampa FL, 33612 (MCC).
Diagnostic Imaging, MCC, Moffitt Cancer Center & Research Institute, 12902 Bruce B. Downs Blvd, Tampa FL, 33612 (MCC).
Biomed Phys Eng Express. 2019 Jul;5(4). doi: 10.1088/2057-1976/ab1dad. Epub 2019 May 22.
We present a novel method for evaluating local spatial correlation structure in two-dimensional (2D) mammograms and evaluate its capability for risk prediction as one possible application. Two matched case-control studies were analyzed. Study 1 included women (N = 588 pairs) with mammograms acquired with either Hologic Selenia full field digital mammography (FFDM) units or Hologic Dimensions digital breast tomosynthesis units. Study 2 included women (N =180 pairs) with mammograms acquired with a General Electric Senographe 2000D FFDM unit. Matching variables included age, HRT usage/duration, screening history, and mammography unit. Local autocorrelation functions were determined with Fourier analysis and compared with a template defined as a 2D double-sided exponential function with one spatial extent parameter: n = 4, 12, 24, 50, 74, 100, and 124, where (n+1)×(n+1) is the area of the local spatial extent measured in pixels. The difference between the local correlation and template was gauged within an adjustable parameter kernel and summarized, producing two measures: the mean (m) and standard deviation (s). Both adjustable parameters were varied in Study 1. Select measures that produced significant associations with breast cancer were translated to Study 2. Breast cancer associations were evaluated with conditional logistic regression, adjusted for body mass index and ethnicity. Odds ratios (ORs) were estimated as per standard deviation increment with 95% confidence intervals (CIs). Two measures were selected for breast cancer association analysis in Study 1: m and s. Both measures revealed significant associations with breast cancer: OR = 1.45 (1.23, 1.66) for m and OR = 1.30 (1.14, 1.49) for s When translating to Study 2, these measures also revealed significant associations: OR = 1.49 (1.12, 1.96) for m and OR = 1.34 (1.06, 1.69) for s. Novel correlation metrics presented in this work produced significant associations with breast cancer risk. This approach is general and may have applications beyond mammography.
我们提出了一种评估二维（2D）乳腺X线照片中局部空间相关结构的新方法，并将其作为一种可能的应用来评估风险预测能力。分析了两项匹配的病例对照研究。研究1纳入了使用Hologic Selenia全视野数字乳腺X线摄影（FFDM）设备或Hologic Dimensions数字乳腺断层合成设备获取乳腺X线照片的女性（N = 588对）。研究2纳入了使用通用电气Senographe 2000D FFDM设备获取乳腺X线照片的女性（N = 180对）。匹配变量包括年龄、激素替代疗法（HRT）使用情况/持续时间、筛查史和乳腺X线摄影设备。通过傅里叶分析确定局部自相关函数，并与定义为具有一个空间范围参数的二维双侧指数函数的模板进行比较：n = 4、12、24、50、74、100和124，其中（n + 1）×（n + 1）是以像素为单位测量的局部空间范围的面积。在一个可调整参数内核内测量局部相关性与模板之间的差异并进行汇总，得出两个指标：均值（m）和标准差（s）。在研究1中，两个可调整参数均有所变化。将与乳腺癌产生显著关联的选定指标转换到研究2中。采用条件逻辑回归评估乳腺癌关联，并对体重指数和种族进行调整。按照标准差增量估计比值比（OR），并给出95%置信区间（CI）。在研究1中选择了两个指标进行乳腺癌关联分析：m和s。这两个指标均显示与乳腺癌存在显著关联：m的OR = 1.45（1.23，1.66），s的OR = 1.30（1.14，1.49）。转换到研究2时，这些指标也显示出显著关联：m的OR = 1.49（1.12，1.96），s的OR = 1.34（1.06，1.69）。本研究中提出的新相关指标与乳腺癌风险存在显著关联。这种方法具有通用性，可能在乳腺X线摄影之外还有其他应用。
