Mammographic Parenchymal Patterns as an Imaging Marker of Endogenous Hormonal Exposure

Rationale and Objectives

Parenchymal texture patterns have been previously associated with breast cancer risk, yet their underlying biological determinants remain poorly understood. Here, we investigate the potential of mammographic parenchymal texture as a phenotypic imaging marker of endogenous hormonal exposure.

Materials and Methods

A retrospective cohort study was performed. Digital mammography (DM) images in the craniocaudal (CC) view from 297 women, 154 without breast cancer and 143 with unilateral breast cancer, were analyzed. Menopause status was used as a surrogate of cumulative endogenous hormonal exposure. Parenchymal texture features were extracted and mammographic percent density (MD%) was computed using validated computerized methods. Univariate and multivariable logistic regression analysis was performed to assess the association between texture features and menopause status, after adjusting for MD% and hormonally related confounders. The receiver operating characteristic (ROC) area under the curve (AUC) of each model was estimated to evaluate the degree of association between the extracted mammographic features and menopause status.

Results

Coarseness, gray-level correlation, and fractal dimension texture features have a significant independent association with menopause status in the cancer-affected population; skewness and fractal dimension exhibit a similar association in the cancer-free population ( P < .05). The ROC AUC of the logistic regression model including all texture features was 0.70 ( P < .05) for cancer-affected and 0.63 ( P < .05) for cancer-free women. Texture features retained significant association with menopause status ( P < .05) after adjusting for MD%, age at menarche, ethnicity, contraception use, hormone replacement therapy, parity, and age at first birth.

Conclusion

Mammographic texture patterns may reflect the effect of endogenous hormonal exposure on the breast tissue and may capture such effects beyond mammographic density. Differences in texture features between pre- and postmenopausal women are more pronounced in the cancer-affected population, which may be attributed to an increased association to breast cancer risk. Texture features could ultimately be incorporated in breast cancer risk assessment models as markers of hormonal exposure.

As new strategies for breast cancer prevention and early detection become available , it is essential to provide accurate, clinically relevant methods, to identify women at high risk of breast cancer. Although a lot of progress has been made, current approaches still face limitations. Most research to date has focused on identifying women at increased familial risk (ie, BRCA1/2 carriers), which only account for the 5%–10% of incident breast cancers . On the other hand, the National Cancer Institute’s breast cancer risk assessment tool for the general population, the Gail model, has only modest discriminatory accuracy at the individual level . Studies suggest that risk prediction could be improved by incorporating mammographic parenchymal pattern descriptors . Parenchymal texture features characterize the spatial distribution and structure of the breast tissue pattern and could potentially complement the widely used measure of breast density, which is typically captured using coarse measures of the overall percent of mammographically dense tissue in the breast . Studies suggest that texture features, particularly in the low spatial frequencies, are strong predictors of cancer risk , even when breast density is considered .

Mammographic breast density, which is currently the most commonly used parenchymal pattern descriptor, has been identified as a strong independent risk factor for breast cancer and is also shown to correlate with certain modifiable risk factors, such as endogenous cumulative and circulating hormone levels, exogenous hormonal exposure, diet, and body mass index . Studies suggest that the biological basis of these associations can be mediated through a number of mechanisms that include increased hormonal exposure, prolactin secretion, or the production of growth factors and non–growth factor peptides , which may lead to tissue progression from normal growth to hyperplasia to neoplasia . Over the past decade, novel parenchymal descriptors characterizing the texture of the breast tissue have also emerged as potentially additional breast cancer risk indicators . Yet, although the biological basis of breast density as a risk factor is starting to be elucidated, the biological determinants of parenchymal texture and its association to breast cancer risk are still not well understood.

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Materials and methods

Study Population

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Table 1

Characteristics of Our Study Population

Cancer-Free Women Cancer-Affected Women Premenopausal Postmenopausal Premenopausal Postmenopausal Total number 88 66 73 70 Mean age (years) 44.41 ± 7.78 52.39 ± 18.32 45.43 ± 6.02 62.77 ± 7.59 Gail 5-year risk (%) 1.51 ± 1.28 2.85 ± 1.99 1.03 ± 0.70 1.81 ± 0.96 Gail lifetime risk (%) 17.63 ± 8.38 17.62 ± 10.89 12.11 ± 5.18 10.22 ± 4.97 Ethnicity Caucasian 76 (87%) 51 (77%) 59 (81%) 54 (77%) African American 7 (8%) 8 (12%) 8 (11%) 13 (20%) Asian 2 (2%) 2 (3%) 2 (3%) 1 (1%) Mixed 1 (1%) 1 (2%) 1 (1%) 1 (1%) Other, not available 2 (2%) 4 (6%) 3 (4%) 1 (1%) Estrogen therapy Yes 1 (1%) 3 (5%) 1 (1%) 2 (3%) No 87 (99%) 63 (95%) 72 (99%) 68 (97%) Contraceptive use Yes 2 (2%) 0 (0%) 6 (8%) 0 (0%) No 86 (98%) 66 (100%) 67 (92%) 70 (100%) Mean age at menarche (years) 12.57 ± 1.71 12.45 ± 2.74 12.59 ± 1.16 12.33 ± 1.49 Mean age at first birth (years) 27.32 ± 5.68 25.59 ± 6.47 28.40 ± 5.09 24.27 ± 5.33 Parity Yes 55 (63%) 56 (85%) 55 (75%) 59 (84%) No 33 (37%) 10 (15%) 18 (25%) 11 (16%)

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Image Dataset

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Parenchymal Texture Analysis

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Mammographic Density Estimation

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Data Analysis

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Results

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Table 2

Summary of Texture Feature Characteristics in Cancer-Free and Cancer-Affected Women

Premenopausal Postmenopausal Mean SD CV ICC Mean SD CV ICC Cancer-free population Coarseness 4.139E-4 1.688E-4 0.40 0.68 4.498E-4 2.898E-4 0.43 0.66 Contrast 3.528E2 107.05 0.31 0.87 3.443E2 89.459 0.26 0.77 Gray level correlation 9.655E-1 3.450E-2 0.03 0.39 9.658E-1 3.470E-2 0.03 0.71 Energy 8.697E-5 3.054E-5 0.36 0.42 7.874E-5 3.327E-5 0.42 0.67 Homogeneity 1.379E-1 1.370E-2 0.07 0.90 1.384E-1 1.280E-2 0.07 0.91 Fractal dimension* 2.556 0.251 0.10 0.71 2.669 0.163 0.06 0.65 Skewness* −1.882E-1 0.527 −2.79 0.52 −1.30E-2 0.559 −55.00 0.27 MD (%)** 43.72 15.36 0.34 0.80 34.57 15.37 0.42 0.66 Cancer-affected population Coarseness* 4.705E-4 1.617E-4 0.34 0.44 4.085E-4 1.754E-4 0.44 0.63 Contrast 3.266E2 1.609E1 0.49 0.09 3.444E2 1.006E1 0.29 0.68 Gray level correlation* 9.751E-1 2.000E-2 0.02 0.35 9.574E-1 4.200E-2 0.043 0.80 Energy 8.217E-5 3.370E-5 0.41 0.63 9.955E-5 5.575E-5 0.59 0.73 Homogeneity 1.440E-1 1.340E-2 0.07 0.56 1.396E-1 1.220E-2 0.09 0.81 Fractal dimension* 2.658 0.210 0.08 0.59 2.591 0.176 0.07 0.62 Skewness −0.855E-1 1.166 −13.00 0.07 0.0934 0.492 5.56 0.31 MD (%)** 44.61 14.96 0.33 0.76 33.36 14.09 0.46 0.73

CV, coefficient of variation; ICC: intra-class correlation; MD, mammographic density; SD, standard deviation.

\* P < .05, ** P < .001. P values are from t -test comparing differences in feature means between pre- and postmenopausal women.

Table 3

Interfeature Spearman Correlation Coefficients in Cancer-Free and Cancer-Affected Women

Coarseness Contrast Gray-Level Correlation Energy Homogeneity Fractal Dimension Skewness MD Cancer-free population Coarseness 1.00 Contrast −0.44** 1.00 Gray-level correlation 0.91** −0.29** 1.00 Energy −0.73** −0.22* −0.72** 1.00 Homogeneity 0.47** −0.99** 0.32** 0.20* 1.00 Fractal dimension 0.61** −0.41** 0.52** −0.30** 0.41** 1.00 Skewness 0.01 −0.12 −0.15 0.11 0.15 0.12 1.00 MD (%) 0.09 −0.15 0.16* 0.01 0.13 −0.09 −0.34** 1.00 Cancer-affected population Coarseness 1.00 Contrast −0.35** 1.00 Gray level correlation 0.95** −0.31** 1.00 Energy −0.77** −0.20* −0.76** 1.00 Homogeneity 0.34** −0.93** 0.29** 0.24* 1.00 Fractal dimension 0.58** −0.24* 0.54** −0.42** 0.20* 1.00 Skewness −0.20* 0.01 −0.35** 0.29** 0.05 −0.02 1.00 MD (%) 0.13 −0.18* 0.23* −0.05 0.17* 0.09 −0.45** 1.00

MD, mammographic density.

\* P < 0.05, P < .001.

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Table 4

Univariate Logistic Regression and ROC Curve Performance in Distinguishing between Pre- and Postmenopausal Women for Each of the Texture Features Alone and after Adjusting for Density and Hormonal Factors

Texture Feature Texture Feature + Density Texture Feature + Density + Hormonal Factors Regression Coefficient AUC Regression Coefficient AUC Regression Coefficient AUC Cancer-free population Coarseness 1200 0.55 1600 0.70** 1700 0.71** Contrast −0.001 0.50 −0.002 0.71** −0.002 0.72** Gray level Correlation 1.80 0.51 4.70 0.70** 4.10 0.70** Energy −5700 0.56 −5200 0.70** −5400 0.70** Homogeneity 5.20 0.50 13 0.70** 13.10 0.71** Fractal Dimension 2.30* 0.62* 2.20* 0.71** 2.10* 0.73** Skewness 0.62* 0.58* 0.24 0.70** 0.27 0.71** MD (%) — −0.05** 0.70** −0.05** 0.71** Cancer-affected population Coarseness −2500* 0.64* −2600* 0.75** −2600* 0.77** Contrast 0.0010 0.57 0.0004 0.73** 0.0008 0.76** Gray level Correlation −24.00* 0.66** −23.00* 0.76** −24.00* 0.78** Energy 7700 0.59 1100* 0.74** 8800 0.77** Homogeneity −25.00 0.57 −18.00 0.74** −29.00 0.77** Fractal dimension −1.90* 0.61* −2.40* 0.75** −2.60* 0.78** Skewness 0.33 0.67 0.05 0.74** 0.03 0.76** MD (%) — −0.05** 0.72** −0.06** 0.76**

AUC, area under the curve; MD, mammographic density; ROC, receiver operating characteristic.

\* P < .05, ** P < .001. Asterisk notation on AUC reflects overall P value of regression model.

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Table 5

Logistic Regression Results for Each Partial Regression Coefficient before and after the Addition of Density and Hormonal Variables to the Multivariable Texture Feature Models

Texture Only Texture + Density Texture + Density + Hormonal Factors Regression Coefficient_P_ Value Regression Coefficient_P_ Value Regression Coefficient_P_ Value Cancer-free population Model constant 20 .15 20 .19 23 .13 Coarseness 920 .65 1400 .50 1600 .48 Contrast −0.01 .28 −0.01 .17 −0.01 .14 Gray-level correlation −15 .18 −8.35 .51 −11 .40 Energy −5700 .73 4200 .98 −2100 .91 Homogeneity −76 .27 −98 .18 −100 .17 Fractal dimension 3.30 <.01 2.68 .04 2.60 .04 Skewness 0.48 .15 0.18 .60 0.19 .59 MD (%) −0.05 <.01 −0.05 <.01 Age at menarche 0.01 .92 Estrogen therapy 0.90 .49 Contraceptive use 0.49 .71 Ethnicity 0.02 .49 Parity/age at first birth 0.45 .104 Model P value .017 <.001 <.001 Cancer-affected population Model constant 39 .01 37 .02 44 .01 Coarseness 3200 .15 2500 .27 330 .17 Contrast −0.01 <.01 −0.01 .04 −0.01 .04 Gray-level correlation −24 .12 −24 .15 −29 .11 Energy 10,000 .12 10,000 .17 9400 .21 Homogeneity −95 <.01 −65 .04 −78 .03 Fractal dimension −0.62 .61 −1.01 .43 −1.20 .38 Skewness 0.99 .02 0.56 .19 0.56 .22 MD (%) −0.04 <.01 −0.03 <.01 Age at menarche 0.04 .79 Estrogen therapy −1.00 .48 Contraceptive use −1.60 .178 Ethnicity 0.07 .433 Parity/age at first birth −0.46 .142 Model P value <.001 <.001 <.001

MD, mammographic density.

P values are shown for the Wald test for each partial regression coefficient and for overall model significance.

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Discussion

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Conclusion

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Acknowledgments

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Appendix

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skewness=w3w322where s

wk=∑gmaxi=0ni(i−i¯)k/N,N=∑gmaxi=0ni,i¯=∑gmaxi=0(ini/N) w

∑

max

(

−

)

∑

max

∑

max

(

)

and n__i represents the number of times that gray level value i takes place in the image region, g max is the maximum gray-level value, and N is the total number of image pixels.

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coarseness=(∑gmaxi=0piv(i))−1,wherev(i)={∑∣∣i−L¯¯¯i∣∣fori∈{ni}ifni≠0istheNGTDM0otherwise c

(

∑

max

(

)

−

where

(

)

{

∑

−

∈

{

}

≠

the

NGTDM

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L¯¯¯i=1S−1∑tk=−t∑tl=−tj(x+k,y+l), L

−

∑

−

∑

−

(

)

where j(x,y) is the pixel located at (x,y) with gray level value i , (k,l)≠(0,0) and S = (2 d + 1) 2 , with d specifying the neighborhood size around the pixel located at (x,y) .

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contrast=∑gi∑gj|i−j|2C(i,j), c

∑

−

(

)

energy=∑gi∑gjC(i,j), e

∑

(

)

correlation=∑i∑j(ij)p(i,j)−μxμyσxσy,homogeneity=∑gmaxi=0∑gmaxj=0C(i,j)1+|i−j| c

∑

(

)

(

)

−

∑

(

)

−

where g is the total number of gray levels, μ and σ are the mean and standard deviation of the partial probability density function p , and C is the normalized cooccurrence matrix .

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F(u,v)=∑M−1m=0∑N−1n=0I(m,n)e−j(2πM)ume−j(2πN)vn,u=0,1,…,M−1v=0,1,…,N−1 F

(

)

∑

−

∑

−

(

)

−

(

)

−

(

)

…

−

…

−

where I is the 2D image region of size ( M , N ), and u and v are the spatial frequencies in the x and y directions. The power spectral density P was estimated from F ( u , v ) as:

P(u,v)=|F(u,v)|2 P

(

)

(

)

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FD=3DT+2−β2=8−β2 F

−

where DT is the topological dimension, and is equal to 2 for a 2D image.

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Mammographic Parenchymal Patterns as an Imaging Marker of Endogenous Hormonal Exposure

Rationale and Objectives

Materials and Methods

Results

Conclusion

Materials and methods

Study Population

Image Dataset

Parenchymal Texture Analysis

Mammographic Density Estimation

Data Analysis

Results

Discussion

Conclusion

Acknowledgments

Appendix

References

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