High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer

Li, Jingmei; Szekeres, L.; Eriksson, Louise; Heddson, Boel; Sundbom, Ann; Czene, Kamila; Hall, Per; Humphreys, Keith

doi:10.1186/bcr3238

Cited by 97 publications

(115 citation statements)

References 26 publications

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“…15 It can be evaluated via visual categorical assessment, 14 such as by using the Boyd or ACR-BIRADS scale, or by using computer-aided methods to estimate a continuous measure of breast PD%. [16][17][18][19][20] Given that the distribution of dense tissue within the breast is not uniform, there is increasing interest in also measuring the heterogeneity of the parenchymal pattern ( Fig. 1) in more granular ways than the global assessment of breast PD%.…”

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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“…Mammographic density is an important parameter in breast imaging, with higher density linked to a decrease in sensitivity and specificity owing to the so-called "masking effect" 8,9 jeopardizing the effectiveness of breast screening. In the ideal setting, radiotherapy performed after surgery should not lead to radiologically detectable changes of parenchymal structures, especially in females with high risk of tumour recurrence.…”

Section: Discussionmentioning

confidence: 99%

Does accelerated hypofractionated adjuvant whole-breast radiotherapy increase mammographic density or change mammographic features?

Bagnera

Milanesio

Cota

et al. 2015

BJR

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Objective: To compare mammographic features before and after accelerated hypofractionated adjuvant wholebreast radiotherapy (AWB-RT) and to evaluate possible appearance of modifications. Methods: A retrospective review of 177 females before and after an AWB-RT treatment (follow-up ranging from 5 to 9 years) was performed by four radiologists focused in breast imaging who independently evaluated diffuse mammographic density patterns and reported on possible onset of focal alterations; modifications in density and fibrosis with parenchymal distortion were deemed as indicators of AWB-RT treatment impact in breast imaging. Results: Prevalent mammographic density (D) patterns in the 177 females evaluated were according to the American College of Radiology-Breast Imaging Reporting and Data System (ACR-BIRADS): D1, fibroadipose density (score percentage from 55.9% to 43.5%); and D2, scattered fibroglandular density (from 42.9% to 32.7%). No change in diffuse mammographic density and no significant difference in mammographic breast parenchymal structure were observed. "No change" was reported with score percentage from 87% to 79.6%. Appearance of fibrosis with parenchymal distortion was reported by all radiologists in only two cases (1.1%, p 5 0.3); dystrophic calcification was identified with percentage score from 2.2% to 3.3% (small type) and from 9.6% to 12.9% (coarse type). Conclusion: No statistically significant changes in followup mammographies 5-9 years after AWB-RT were detected, justifying large-scale selection of AWB-RT treatment with no risk of altering radiological breast parameters of common use in tumour recurrence detection. Advances in knowledge: The hypofractionated radiotherapy (AWB-RT treatment) is a new proven, safe and effective modality in post-operative patients with early breast cancer with excellent local control and survival. In our study, the absence of changes in mammographic density patterns and in breast imaging before and after AWB-RT treatment (up to 5-9 years after radiotherapy) justifies large-scale use of AWB-RT treatment without hindrance in tumour recurrence diagnosis.

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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%

“…Manduca et al [21] analyze several textural features of breast tissue and find that they predict breast cancer risk at the same magnitude as MD. Li et al [22] propose a method based on a machine learning approach in which the MD obtained using the semi-automated tool presented in [10] is used as ground truth. A variety of measurements obtained under 15 thresholding methods are then used as features to learn the model.…”

Section: Introductionmentioning

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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High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer

Cited by 97 publications

References 26 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

Does accelerated hypofractionated adjuvant whole-breast radiotherapy increase mammographic density or change mammographic features?

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

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