Additional file 1
Supplementary Table 1. Intraclass correlation coefficients (ICC) for masked reliability assessment of computer-extracted features
(n=91 pairs)
Feature Type and
Number
Feature
Definition
ICC
Reference
AVE
Average gray value within ROI; higher values
correspond to denser region
0.986
[1-3]
MinCDF
Gray value corresponding to the 5% region cutoff on
cumulative density function; higher values correspond
to denser region
0.971
[1-3]
Balance
Ratio of (95%CDF-AVE) to (AVE-5%CDF); Related to
skewness; Values less than one correspond to having
a ROI that is skewed towards relatively denser values.
0.959
[1-4]
Energy
Measure of image homogeneity; higher values
correspond to being more homogeneous
0.980
[5-7]
MaxF (COOC)
Largest number of a gray value pair in the cooccurrence matrix; measure of image homogeneity;
higher values correspond to being more homogeneous
0.987
[5-7]
MaxCDF
Gray value corresponding to the 95% region cutoff on
cumulative density function; higher values correspond
to denser region
0.996
[1-3]
70%CDF
Gray value corresponding to the 70% region cutoff on
cumulative density function; higher values correspond
to denser region
0.989
[1-3]
30%CDF
Gray value corresponding to the 30% region cutoff on
cumulative density function; higher values correspond
to denser region
0.982
[1-3]
M7
Balance2
Ratio of (70%CDF-AVE) to (AVE-30%CDF); Related to
skewness; Values larger than one correspond to
denser regions
0.842
[1-4]
M8
Skewness
The denseness measure; Negative values correspond
to denser region
0.981
[1-4]
M9
RMS
Rood mean square variation; quantifies the magnitude
1
of parenchymal patterns
0.990
[1-3, 7]
(cont’d)
SELECTED FEATURES:
Gray level magnitudebased features:
M1
M2
M3
Texture-based
features:
T1
T2
OTHER FEATURES:
Gray level magnitudebased features:
M4
M5
M6
Feature Type and
Number
Feature
Definition
ICC
Reference
D_BC [1-6]
Fractal dimension estimated based on box-counting
method; lower values correspond to coarser texture
Range:
0.8950.995
[3, 8]
D_M
Fractal dimension estimated based on Minkowski
method; lower values correspond to coarser texture
0.993
[3, 8]
Beta [1-8]
Exponent beta from power law spectrum analysis;
characterize the frequency content of texture pattern;
higher values correspond to coarser texture
Range:
0.9600.990
[9]
Contrast (COOC)
Contrast measure calculated from co-occurrence
matrix; measure of image local variations
0.998
[5-7]
T19
Contrast (NGTDM)
Contrast measure calculated from Neighborhood-Graytone-difference matrix; measure of image local
variations
0.989
[1-4]
T20
Correlation (COOC)
Measure of image linearity; larger values correspond to
linear patterns
0.791
[5-7]
T21
Entropy (COOC)
Measure of randomness of gray level pairs
0.996
[5-7]
T22
ZeroMeasure (COOC)
Zero measures in co-occurrence matrix; measure of
image homogeneity
0.982
[5-7]
T23
Skewness (COOC)
Measure of the asymmetry of co-occurrence matrix;
image homogeneity measure
0.983
[5-7]
T24
MeanEdgeGradient
Average of edge gradient; image coarseness measure
0.997
[3, 7]
T25
MaxEdgeGradient
Maximum edge gradient; image coarseness measure
0.905
[3, 7]
T26
MinEdgeGradient
Minimum edge gradient; image coarseness measure
0.933
[3, 7]
T27
StdDevEdgeGradient
Standard deviation of edge gradient; image coarseness
measure
0.996
[3, 7]
T28
Coarseness (NGTDM)
Measure of image coarseness; higher values
correspond to coarser region
0.953
[1-4]
T29
FMP
First moment of power spectrum; spatial frequency
content of parenchymal patterns
0.972
[1-3, 7]
OTHER FEATURES:
Texture-based
features:
T3 - T8
T9
T10 - T17
T18
2
Supplementary Table 2. Correlations between selected computer-extracted features (n=237 women)
Correlation with
AVE
Correlation with
MinCDF
Correlation with
Balance
Feature Type and
Number
Feature
r*
p-value
r*
p-value
r*
Gray level
magnitude-based
features:
M1
M2
M3
AVE
MinCDF
Balance
1.00
0.60
-0.77
<.0001
<.0001
1.00
-0.41
<.0001
1.00
Texture-based
features:
T1
T2
Energy
-0.09
MaxF (COOC) 0.03
0.19
0.69
0.48
0.43
<.0001
<.0001
-0.09
-0.13
*Spearman's correlation coefficient
3
p-value
0.19
0.053
Correlation
with Energy
r*
p-value
1.00
0.90 <.0001
Supplementary Table 3. Sensitivity analyses of the ability of the trained classifier to distinguish between BRCA1/2 mutation carriers and non-carriers in age-matched datasets
Training dataset
Description
Testing dataset
Selected features ¥
Description
Testing dataset results
No. noncarriers
No.
carriers
Mean paired
difference in
probability score
from trained
classifier
SD
p-value †
Mean
paired
difference
in PMD
SD
p-value †
-1.83
18.04
0.83
PMD Alone
Age-matched ~3 years
19
19
Features alone
Age-matched ~3 years
19
19
0.28
0.48
0.02
Age-matched ~3 years
19
19
0.32
0.53
0.02
Age-matched ~1 year
17
17
Age-matched ~1 year
17
17
0.26
0.52
0.06
Age-matched ~1 year
17
17
0.25
0.53
0.08
Age-matched ~3 years
19
19
0.18
0.39
0.055
Age-matched ~3 years
19
19
0.18
0.37
0.06
PMD Alone
Features alone
4 features selected:
MinCDF
Energy
AVE
Max F (COOC)
Features + PMD
Training dataset truncated at
upper age-limit of mutation
carriers §
Features alone
Features + PMD
SE
0.55
0.09
0.71
0.09
0.72
0.08
0.52
0.09
0.73
0.09
0.74
0.09
0.72
0.09
0.71
0.09
Age-matched testing
datasets
Original training dataset*
Features + PMD
AUC
3 features selected:
MinCDF
Max F (COOC)
Balance
-0.77
19.33
1.00
Testing dataset truncated at
upper age-limit of mutation
carriers §
* Original training set includes 70 non-carriers and 107 BRCA1/2 mutation carriers
§ Training dataset truncated at upper age-limit of mutation carriers (i.e., 55 years) includes 48 non-carriers and 48 BRCA1/2 mutation carriers
¥ Selected features: Percent mammographic density was not selected by the trained classifier but was forced into the models where noted
† P-value for differences between age-matched pairs from Wilcoxon signed rank test
AUC, area under the curve; PMD, percent mammographic density; SE, standard error
4
-1.83
18.04
0.83
Supplementary Figure 1. Histogram of the number of times that each feature was selected in the 177 leaveone-case-out stepwise feature selection using linear discriminant analysis of the training dataset. Note the
features M1-M9 correspond to gray level magnitude-based features, and features T1-T29 correspond to
texture-based features. Two gray level magnitude-based features (i.e., M1, AVE; M2, MinCDF) and two
texture-based features (i.e., T1, Energy; T2, MaxF (COOC)) were selected more than half the time (as
indicated by the dashed line in the figure), and were included in subsequent Bayesian Artificial Neural Network
models.
5
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