Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment

Abstract

BackgroundThe assessment of a woman’s risk for developing breast cancer has become increasingly important for establishing personalized screening recommendations and forming preventive strategies. Studies have consistently shown a strong relationship between breast cancer risk and mammographic parenchymal patterns, typically assessed by percent mammographic density. This paper will review the advancing role of mammographic texture analysis as a potential novel approach to characterize the breast parenchymal tissue to augment conventional density assessment in breast cancer risk estimation.Main textThe analysis of mammographic texture provides refined, localized descriptors of parenchymal tissue complexity. Currently, there is growing evidence in support of textural features having the potential to augment the typically dichotomized descriptors (dense or not dense) of area or volumetric measures of breast density in breast cancer risk assessment. Therefore, a substantial research effort has been devoted to automate mammographic texture analysis, with the aim of ultimately incorporating such quantitative measures into breast cancer risk assessment models. In this paper, we review current and emerging approaches in this field, summarizing key methodological details and related studies using novel computerized approaches. We also discuss research challenges for advancing the role of parenchymal texture analysis in breast cancer risk stratification and accelerating its clinical translation.ConclusionsThe objective is to provide a comprehensive reference for researchers in the field of parenchymal pattern analysis in breast cancer risk assessment, while indicating key directions for future research.

Deep learning modeling using normal mammograms for predicting breast cancer risk

“…34 Manual feature engineering is about crafting well-defined features (such as shape, contour, texture) to directly describe domain knowledge, such as imaging appearance characteristics. 35,36 Researchers have crafted hundreds of computer features 9,35,37 and tested them in many applications in a trial-and-error manner to identify those that work best for specific tasks. 38 However, it is not always straightforward to translate qualitative and tactic radiological domain knowledge to exact mathematic descriptors to support manual feature engineering.…”

“…[5][6][7][8] In addition to breast density, studies have shown that the mammographic imaging texture features of breast tissue are also related to breast cancer risk. 9,10 Texture descriptors such as energy, contrast, correlation, etc. compute the local properties at each pixel and derive a set of statistics information from the local properties' distribution.…”

Beyond BI-RADS Density: A Call for Quantification in the Breast Imaging Clinic

“…In addition to generating reproducible, quantitative metrics, these methods allow estimation of a continuous score, rather than a categorical assessment, which may allow for more granular density-based risk stratification for women. subjective assignment of BI-RADS density categories by providing reproducible estimates of both the risk of masking a cancer as well as the risk of developing breast cancer-two important factors in determining personalized breast cancer screening algorithms (35).…”

Technical challenges in generalizing calibration techniques for breast density measurements

“…Women with an extensive degree of breast density have a two to sixfold higher risk of breast cancer compared to women with little density . Although there are several established methods to analyze mammograms for risk purposes, to date there is not a recognized standard for breast density determination clinically . To address this situation, our ongoing breast density work includes developing calibrated metrics based on imaging reference phantoms.…”