Spatial Correlation and Breast Cancer Risk.

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<sub>n+1</sub>)<sub>,</sub> and standard deviation (s<sub>n+1</sub>). 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<sub>75</sub> and s<sub>25</sub>. Both measures revealed significant associations with breast cancer: OR = 1.45 (1.23, 1.66) for m<sub>75</sub> and OR = 1.30 (1.14, 1.49) for s<sub>25.</sub> When translating to Study 2, these measures also revealed significant associations: OR = 1.49 (1.12, 1.96) for m<sub>75</sub> and OR = 1.34 (1.06, 1.69) for s<sub>25</sub>. Novel correlation metrics presented in this work produced significant associations with breast cancer risk. This approach is general and may have applications beyond mammography.

Fowler EEE, Hathaway C, Heine J, Sellers TA, Tillman F, Weinfurtner R


Biomed Phys Eng Express, 2019, 5 (4)

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