Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment. Issue 7 (16th June 2015)

• Record Type: Journal Article
• Authors: Zheng, Yuanjie; Keller, Brad M.; Ray, Shonket; Wang, Yan; Conant, Emily F.; Gee, James C.; Kontos, Despina
• Is Part Of: Medical physics. Volume 42:Issue 7(2015)
• Page Start: 4149
• Page End: 4160
• Publication Date: 2015-06-16
• Subjects: cancer -- feature extraction -- image texture -- mammography -- medical image processing -- regression analysis; Mammography; Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Analysis of texture; digital mammography -- parenchymal texture -- breast density -- cancer risk; Cancer -- Digital mammography -- Pipelines -- Image analysis -- Fractals -- Medical image segmentation -- Tissues -- Granular pattern formation; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4921996
• Languages: English
• ISSNs: 0094-2405

Abstract

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 to standard demographic and reproductive risk factors.