Texture Features from Mammographic Images and Risk of Breast Cancer

Manduca, Armando; Carston, Michael J.; Heine, John J.; Scott, Christopher G.; Pankratz, V. Shane; Brandt, Kathy R.; Sellers, Thomas A.; Vachon, Celine M.; Cerhan, James R.

doi:10.1158/1055-9965.epi-08-0631

Cited by 128 publications

(156 citation statements)

References 34 publications

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“…[3][4][5] Starting with the pioneering work of Wolfe,6,7 studies have long suggested a relationship between mammographic parenchymal patterns and breast cancer risk. [8][9][10][11] These parenchymal patterns are formed by the distribution of fatty, fibroglandular, and stromal breast tissues. An increasingly supported hypothesis is that inherent biological factors, such as endogenous hormonal exposure and genetic predisposition that are associated with the risk of breast cancer, are expressed in a woman's parenchymal tissue and subsequently manifested in her mammographic parenchymal patterns.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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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...

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Section: Introductionmentioning

confidence: 99%

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

et al. 2015

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“…Manduca et al performed texture analysis on a case-control study on screen-film mammograms. The texture features showed a similar predicting power for breast cancer risk assessment (area under curve(AUC)00.58-0.60) as that obtained with just breast percent density (AUC00.58) [14]. Wei et al also performed a case-control study with computer-extracted texture features on screen-film mammograms; the AUC of 0.74 was obtained by using combined texture features as the decision variable for differentiating case subjects from control subjects in their study [15].…”

Section: Introductionmentioning

confidence: 67%

Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

Giger

et al. 2012

J Digit Imaging

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The purpose of this study was to demonstrate the robustness of our prior computerized texture analysis method for breast cancer risk assessment, which was developed initially on a limited dataset of screen-film mammograms. This current study investigated the robustness by (1) evaluating on a large clinical dataset, (2) using full-field digital mammograms (FFDM) as opposed to screen-film mammography, and (3) incorporating analyses over two types of high-risk patient sets, as well as patients at low risk for breast cancer. The evaluation included the analyses on the parenchymal patterns of women at high risk of developing of breast cancer, including both BRCA1/2 gene mutation carriers and unilateral cancer patients, and of women at low risk of developing breast cancer. A total of 456 cases, including 53 women with BRCA1/2 gene mutations, 75 women with unilateral cancer, and 328 low-risk women, were retrospectively collected under an institutional review board approved protocol. Regions-ofinterest (ROIs), were manually selected from the central breast region immediately behind the nipple. These ROIs were subsequently used in computerized feature extraction to characterize the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk subjects. In a round robin evaluation on the FFDM dataset with Bayesian artificial neural network analysis, AUC values of 0.82 (95% confidence interval [0.75, 0.88]) and 0.73 (95% confidence interval [0.67, 0.78]) were obtained between BRCA1/2 gene mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results from computerized texture analysis on digital mammograms demonstrated that high-risk and low-risk women have different mammographic parenchymal patterns. On this large clinical dataset, we validated our methods for quantitative analyses of mammographic patterns on FFDM, statistically demonstrating again that women at high risk tend to have dense breasts with coarse and lowcontrast texture patterns.

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“…Also, improvements in contrast normalization should be addressed to avoid underestimating MD in low contrast images. Finally, other parameters could be explored that take into account not only the relative density but also the shape and the distribution of FGT in the breast [20,22] or texture features from the mammogram [21].…”

Section: Discussionmentioning

confidence: 99%

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

Llobet

Pollán

Antón

et al. 2014

Computer Methods and Programs in Biomedicine

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Semi-automated and fully-automated mammographic density measurement and breast cancer risk prediction better tissue classification and density measurement on a continuous scale. The fully-automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully-automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms.The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC=0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC=0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully-automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessment present a good correlation.Both methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.

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Texture Features from Mammographic Images and Risk of Breast Cancer

Cited by 128 publications

References 34 publications

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

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

Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

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