TY - JOUR
T1 - Beyond breast density
T2 - A review on the advancing role of parenchymal texture analysis in breast cancer risk assessment
AU - Gastounioti, Aimilia
AU - Conant, Emily F.
AU - Kontos, Despina
N1 - Funding Information:
The authors wish to acknowledge support by the National Cancer Institute at the National Institutes of Health via a research grant award (5R01CA161749-04), the Population-based Research Optimizing Screening through Personalized Regimens (PROSPR) Network (U54CA163313), and a Resource-Related Research Project–Cooperative Agreement (1U24CA189523).
Publisher Copyright:
© 2016 The Author(s).
PY - 2016/9/20
Y1 - 2016/9/20
N2 - 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.
AB - 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.
KW - Breast cancer risk
KW - Digital mammography
KW - Parenchymal texture analysis
KW - Quantitative breast imaging
UR - http://www.scopus.com/inward/record.url?scp=84988504403&partnerID=8YFLogxK
U2 - 10.1186/s13058-016-0755-8
DO - 10.1186/s13058-016-0755-8
M3 - Review article
C2 - 27645219
AN - SCOPUS:84988504403
SN - 1465-5411
VL - 18
JO - Breast Cancer Research
JF - Breast Cancer Research
IS - 1
M1 - 91
ER -