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Table 3 Associations between identified dense area features and IC versus SC based on multivariate logistic regression modelling

From: Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study

  Odds ratios (95 % Cl) for interval versus screen-detected cancer, estimated by logistic regression modelling
Primary cohort Validation cohort
Covariate Model 1 n = 1403 Model 2 n = 1314 Model 3 n = 1312 Model 3 n = 1182
Percent density 1.37 (1.22 to 1.53) 1.23 (1.08 to 1.41) 0.98 (0,81 to 1.17) 1.04 (0.86 to 1.24)
BMI - 0.84 (0.73 to 0.98) 0.88 (0.76 to 1.02) 0.98 (0.84 to 1.15)
HRT use at diagnosis - 1.57 (1.17 to 2.11) 1.53 (1.14 to 2.07) 1.32 (0.99 to 1.77)
Age at diagnosis - 0.98 (0.87 to 1.12) 1.01 (0.89 to 1.15) 0.90 (0.77 to 1.05)
F40 skewness of the intensity gradient - - 1.32 (1.12 to 1.56) 1.21 (1.04 to 1.41)
F21 eccentricity - - 1.20 (1.04 to 1.39) 1.17 (0.98 to 1.39)
  1. Feature values calculated based on the dense area of the mammogram as identified by Otsu's method, then Box-Cox transformed and standardized. Odds ratios are estimated as per-standard deviation change in the underlying covariate. Validation cohort is an older breast cancer cohort with similar covariate definitions as the primary cohort
  2. 1C interval breast cancer, SC screen-detected breast cancer, 95 % CI 95 % confidence interval, BMI body mass index, HRT hormone replacement therapy