Skip to main content

Table 3 Selected texture features for predicting percentage mammographic density (PMD)

From: Using automated texture features to determine the probability for masking of a tumor on mammography, but not ultrasound

Feature family Number of features Correlation with PMD
Median (min., max.)c
All Lassoa Boostinga Commonb All Lasso Boosting
Fourier 12 9 9 6 0.16 (0.03, 0.28) 0.10 (0.03, 0.28) 0.12 (0.03, 0.28)
Histogram 14 13 13 12 0.18 (0.00, 0.25) 0.19 (0.00, 0.25) 0.17 (0.00, 0.25)
Markovian 37 24 24 20 0.44 (0.00, 0.72) 0.39 (0.00, 0.72) 0.43 (0.00, 0.72)
Moment-based 70 54 33 32 0.21 (0.00, 0.61) 0.21 (0.00, 0.61) 0.21 (0.00, 0.61)
Regional 45 36 32 28 0.22 (0.01, 0.52) 0.23 (0.03, 0.52) 0.20 (0.01, 0.52)
Run length 28 18 15 12 0.59 (0.04, 0.71) 0.60 (0.15, 0.70) 0.62 (0.04, 0.71)
Wavelet 12 10 8 8 0.24 (0.01, 0.42) 0.26 (0.06, 0.42) 0.26 (0.06, 0.31)
Total 218 164 134 118    
  1. aSelected number of features using lasso and boosting method, respectively, to predict PMD. Prediction models were fitted on the complete dataset. The tuning parameters were estimated by cross-validation
  2. bNumber of features selected both by lasso and boosting
  3. cEach feature was correlated with PMD. Summary statistics (median, minimum, maximum) of Spearman correlation coefficients between (all and selected) features and PMD are shown