Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment

Abstract

Purpose: Mammographic percent density (PD%) is known to be a strong risk factor for breast cancer. Recent studies also suggest that parenchymal texture features, which are more granular descriptors of the parenchymal pattern, can provide additional information about breast cancer risk. To date, most studies have measured mammographic texture within selected regions of interest (ROIs) in the breast, which cannot adequately capture the complexity of the parenchymal pattern throughout the whole breast. To better characterize patterns of the parenchymal tissue, the authors have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. Methods: Digital mammograms from 106 cases with 318 age-matched controls were retrospectively analyzed. The lattice-based approach is based on a regular grid virtually overlaid on each mammographic image. Texture features are computed from the intersection (i.e., lattice) points of the grid lines within the breast, using a local window centered at each lattice point. Using this strategy, a range of statistical (gray-level histogram, co-occurrence, and run-length) and structural (edge-enhancing, local binary pattern, and fractal dimension) features are extracted. To cover the entire breast, the size of the local window for feature extraction is set equal to the lattice grid spacing and optimized experimentally by evaluating different windows sizes. The association between their lattice-based texture features and breast cancer was evaluated using logistic regression with leave-one-out cross validation and further compared to that of breast PD% and commonly used single-ROI texture features extracted from the retroareolar or the central breast region. Classification performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC). DeLong's test was used to compare the different ROCs in terms of AUC performance. Results: The average univariate performance of the lattice-based features is higher when extracted from smaller than larger window sizes. While not every individual texture feature is superior to breast PD% (AUC: 0.59, STD: 0.03), their combination in multivariate analysis has significantly better performance (AUC: 0.85, STD: 0.02, p < 0.001). The lattice-based texture features also outperform the single-ROI texture features when extracted from the retroareolar or the central breast region (AUC: 0.60-0.74, STD: 0.03). Adding breast PD% does not make a significant performance improvement to the lattice-based texture features or the single-ROI features (p > 0.05). Conclusions: The proposed lattice-based strategy for mammographic texture analysis enables to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. Larger studies are warranted to validate these findings and also compare...

Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations

“…The particular approach of breast sampling has been shown to maximize the robustness of texture features across different systems, as well as their association with breast cancer risk. 15,32 In a second step, a feature vector, comprising of the mean of the corresponding texture values over all lattice points within the breast for all descriptors, characterizes the overall breast parenchymal texture. Following this two-step approach, we estimated a total of 28 texture descriptors, including structural, 34,35 In Fig.…”

“…An automated image analysis pipeline, 14,15 based on a novel lattice-based strategy, was used to extract a wide range of parenchymal texture features from the entire breast region. With this method, texture feature values are calculated within nonoverlapping 6.3 × 6.3 mm 2 local square regions surrounding each lattice point of a grid virtually overlaid on the entire segmented breast region, thereby generating a corresponding texture map for each texture descriptor.…”

“…12,13 Growing evidence also suggests that localized measures of the parenchymal tissue patterns may reflect changes in modifiable risk factors for breast cancer (i.e., factors, such as hormonal exposure, and BMI, which can be controlled or treated) and have a complementary role to breast density. 14,15 Digital mammography acquisition produces two types of images, both of which can be used for breast density and texture analysis. [16][17][18][19][20][21][22][23][24][25] The raw (i.e., "FOR PROCESSING") images are proportional to the x-ray attenuation of the breast tissue, while the processed (i.e., "FOR PRESENTATION" or "FOR CLINICAL DISPLAY") images are generated after vendor-specific postprocessing is applied to the raw images in an attempt to increase lesion conspicuity before clinical evaluation and interpretation.…”

The use of fractal analysis in medicine: A literature review

“…11 Zheng et al have developed a fully automated software pipeline based on a novel lattice-based strategy to extract a range of parenchymal texture features from the entire breast region. 12 They conclude that a lattice-based strategy for mammographic texture analysis makes it possible to characterize the parenchymal pattern over the entire breast. As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment.…”

“…As such, these features provide richer information compared to currently used descriptors and may ultimately improve breast cancer risk assessment. 12 Heymans et al examined the differences of micro vessels in malignant melanoma using the fractal method.…”