Beyond breast density: a review on the advancing role of parenchymal texture analysis in breast cancer risk assessment. Issue 1 (December 2016)

• Record Type: Journal Article
• Authors: Gastounioti, Aimilia; Conant, Emily; Kontos, Despina
• Is Part Of: Breast cancer research. Volume 18:Issue 1(2016)
• Page Start: 1
• Page End: 12
• Publication Date: 2016-12
• Subjects: Breast cancer risk -- Parenchymal texture analysis -- Quantitative breast imaging -- Digital mammography; Breast -- Cancer -- Periodicals; 616.99449
• DOI: 10.1186/s13058-016-0755-8
• Languages: English
• ISSNs: 1465-542X

Abstract

Abstract Background The 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 text The 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. Conclusions The 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.