Radiomic Phenotypes of Mammographic Parenchymal Complexity: Toward Augmenting Breast Density in Breast Cancer Risk Assessment

Abstract

Purpose To identify phenotypes of mammographic parenchymal complexity by using radiomic features and to evaluate their associations with breast density and other breast cancer risk factors. Materials and Methods Computerized image analysis was used to quantify breast density and extract parenchymal texture features in a cross-sectional sample of women screened with digital mammography from September 1, 2012, to February 28, 2013 (n = 2029; age range, 35-75 years; mean age, 55.9 years). Unsupervised clustering was applied to identify and reproduce phenotypes of parenchymal complexity in separate training (n = 1339) and test sets (n = 690). Differences across phenotypes by age, body mass index, breast density, and estimated breast cancer risk were assessed by using Fisher exact, χ², and Kruskal-Wallis tests. Conditional logistic regression was used to evaluate preliminary associations between the detected phenotypes and breast cancer in an independent case-control sample (76 women diagnosed with breast cancer and 158 control participants) matched on age. Results Unsupervised clustering in the screening sample identified four phenotypes with increasing parenchymal complexity that were reproducible between training and test sets (P = .001). Breast density was not strongly correlated with phenotype category (R² = 0.24 for linear trend). The low- to intermediate-complexity phenotype (prevalence, 390 of 2029 [19%]) had the lowest proportion of dense breasts (eight of 390 [2.1%]), whereas similar proportions were observed across other phenotypes (from 140 of 291 [48.1%] in the high-complexity phenotype to 275 of 511 [53.8%] in the low-complexity phenotype). In the independent case-control sample, phenotypes showed a significant association with breast cancer (P = .001), resulting in higher discriminatory capacity when added to a model with breast density and body mass index (area under the curve, 0.84 vs 0.80; P = .03 for comparison). Conclusion Radiomic phenotypes capture mammographic parenchymal complexity beyond conventional breast density measures and established breast cancer risk factors. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Pinker in this issue.

Figure 1:

Flowchart shows inclusion and exclusion criteria for cross-sectional screening sample analyzed in study for parenchymal complexity phenotype identification.

Figure 2a:

Heat map generated by unsupervised hierarchical clustering of extracted radiomic features, applied to separate (a) training and (b) test sets of screening sample for phenotype identification, shows cluster membership (top color bar) with complexity-based color scheme (ie, light green = low parenchymal complexity, dark green = low to intermediate parenchymal complexity, dark red = intermediate to high parenchymal complexity, bright red = high parenchymal complexity). Each column in heat map represents a woman and each row represents a specific radiomic feature, with standardized feature values ordered according to parenchymal complexity (ie, low complexity = −3 standard deviations depicted by green, high complexity = 3 standard deviations depicted by red in color bar on top left). Dendrogram at top represents grouping of women in distinct phenotypes, whereas dendrogram on left represents groupings of extracted features with similar information. Corresponding color maps represent distributions of demographics, risk factor data, and screening outcomes for each phenotype (top) and categories of extracted features (left).

Figure 2b:

Heat map generated by unsupervised hierarchical clustering of extracted radiomic features, applied to separate (a) training and (b) test sets of screening sample for phenotype identification, shows cluster membership (top color bar) with complexity-based color scheme (ie, light green = low parenchymal complexity, dark green = low to intermediate parenchymal complexity, dark red = intermediate to high parenchymal complexity, bright red = high parenchymal complexity). Each column in heat map represents a woman and each row represents a specific radiomic feature, with standardized feature values ordered according to parenchymal complexity (ie, low complexity = −3 standard deviations depicted by green, high complexity = 3 standard deviations depicted by red in color bar on top left). Dendrogram at top represents grouping of women in distinct phenotypes, whereas dendrogram on left represents groupings of extracted features with similar information. Corresponding color maps represent distributions of demographics, risk factor data, and screening outcomes for each phenotype (top) and categories of extracted features (left).

Figure 3:

Phenotype-specific violin plots show distributions from left to right of age (in years), body mass index (BMI) (in kg/m²), and percentage of density across different parenchymal complexity phenotypes. Plots are arranged in order of increasing parenchymal complexity (from light green to bright red), and solid bars at top indicate significant pairwise differences in corresponding distributions of age, BMI, and percentage of density for different phenotypes. LIBRA = Laboratory for Individualized Breast Radiodensity Assessment.

Figure 4a:

Images show examples of negative digital screening mammograms of women with (a) high density and high complexity (percent density [PD], 42%; complexity score [CS], 0.6), (b) high density and low complexity (PD, 43%; CS, −0.6), (c) low density and high complexity (PD, 14%; CS, 0.7), and (d) low density and low complexity (PD, 8%; CS, −0.7), demonstrating differences in breast density versus breast complexity. Corresponding age and body mass index for these women were (a) 54 years and 19.5 kg/m², (b) 46 years and 28.7 kg/m², (c) 57 years and 17.9 kg/m², and (d) 52 years and 48.6 kg/m², respectively.

Figure 4b:

Images show examples of negative digital screening mammograms of women with (a) high density and high complexity (percent density [PD], 42%; complexity score [CS], 0.6), (b) high density and low complexity (PD, 43%; CS, −0.6), (c) low density and high complexity (PD, 14%; CS, 0.7), and (d) low density and low complexity (PD, 8%; CS, −0.7), demonstrating differences in breast density versus breast complexity. Corresponding age and body mass index for these women were (a) 54 years and 19.5 kg/m², (b) 46 years and 28.7 kg/m², (c) 57 years and 17.9 kg/m², and (d) 52 years and 48.6 kg/m², respectively.

Figure 4c:

Images show examples of negative digital screening mammograms of women with (a) high density and high complexity (percent density [PD], 42%; complexity score [CS], 0.6), (b) high density and low complexity (PD, 43%; CS, −0.6), (c) low density and high complexity (PD, 14%; CS, 0.7), and (d) low density and low complexity (PD, 8%; CS, −0.7), demonstrating differences in breast density versus breast complexity. Corresponding age and body mass index for these women were (a) 54 years and 19.5 kg/m², (b) 46 years and 28.7 kg/m², (c) 57 years and 17.9 kg/m², and (d) 52 years and 48.6 kg/m², respectively.

Figure 4d:

Images show examples of negative digital screening mammograms of women with (a) high density and high complexity (percent density [PD], 42%; complexity score [CS], 0.6), (b) high density and low complexity (PD, 43%; CS, −0.6), (c) low density and high complexity (PD, 14%; CS, 0.7), and (d) low density and low complexity (PD, 8%; CS, −0.7), demonstrating differences in breast density versus breast complexity. Corresponding age and body mass index for these women were (a) 54 years and 19.5 kg/m², (b) 46 years and 28.7 kg/m², (c) 57 years and 17.9 kg/m², and (d) 52 years and 48.6 kg/m², respectively.