Article

Mammographic Pattern Analysis: An Emerging Risk Assessment Tool

Academic Radiology (Impact Factor: 2.08). 06/2007; 14(5):511-2. DOI: 10.1016/j.acra.2007.03.003
Source: PubMed
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    • "Substantial advances have been made with computer aided mammography in breast cancer research and treatment . Mammographic risk can be assessed in a clinical environment based on subjective appraisal of mammograms using protocols such as BI-RADS (American College of Radiology's Breast Imaging Reporting and Data System) [5], which can lead to inter-and intraobserver variability [6]. Within computer aided mammography, the idea of developing a fully automatic and repeatable breast tissue segmentation using computer vision and machine learning techniques is to facilitate cancer risk classification. "
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    ABSTRACT: Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models.
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    ABSTRACT: Studies have demonstrated a relationship between mammographic parenchymal texture and breast cancer risk. Although promising, texture analysis in mammograms is limited by tissue superposition. Digital breast tomosynthesis (DBT) is a novel tomographic x-ray breast imaging modality that alleviates the effect of tissue superposition, offering superior parenchymal texture visualization compared to mammography. The aim of this study was to investigate the potential advantages of DBT parenchymal texture analysis for breast cancer risk estimation. DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with cancer risk, were computed from the retroareolar breast region. The relative performances of the DBT and DM texture features were compared in correlating with two measures of breast cancer risk: (1) the Gail and Claus risk estimates and (2) mammographic breast density. Linear regression was performed to model the association between texture features and increasing levels of risk. No significant correlation was detected between parenchymal texture and the Gail and Claus risk estimates. Significant correlations were observed between texture features and breast density. Overall, the DBT texture features demonstrated stronger correlations with breast percent density than DM features (P < or = .05). When dividing the study population into groups of increasing breast percent density, the DBT texture features appeared to be more discriminative, having regression lines with overall lower P values, steeper slopes, and higher R(2) estimates. Although preliminary, the results of this study suggest that DBT parenchymal texture analysis could provide more accurate characterization of breast density patterns, which could ultimately improve breast cancer risk estimation.
    Academic radiology 03/2009; 16(3):283-98. DOI:10.1016/j.acra.2008.08.014 · 2.08 Impact Factor
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    ABSTRACT: Strong evidence shows that characteristic patterns of breast tissues as seen on mammography, referred to as mammographic parenchymal patterns, provide crucial information about breast cancer risk. Quantitative evaluation of the characteristic mixture of breast tissues can be used as for mammographic risk assessment as well as for quantification of change of the relative proportion of different breast tissue patterns. This paper investigates mammographic segmentation based on spatial moments and prior information of mammographic building blocks (i.e. nodular, linear, homogenous, and radiolucent) as described by Taba¿r's tissue models to describe parenchymal patterns. The algorithm extracted texture features from a set of sub-sampled mammographic patches. Taba¿r's mammographic building blocks were modelled as statistical distribution of clustered filter responses based on spatial moments. Evaluation was based on the Mammographic Image Analysis Society (MIAS) database. The experimental results indicated that the developed methodology is capable of modelling complex mammographic images and can deal with intraclass variation and noise aspects. The results show realistic segmentation on tissue specific regions with respect to breast anatomy and Taba¿r's tissue models. In addition, the segmentation results were used for mammographic risk based classification of the entire MIAS database resulting in ~70% correct low/high risk classification.
    Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference on; 12/2009
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