Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation. A Preliminary Study
(2009) In Academic Radiology 16(3). p.283-298- Abstract
Rationale and Objectives: 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. Materials and Methods: DBT and digital mammographic (DM) images of 39 women were analyzed. Texture features, shown in previous studies with mammograms to correlate with... (More)
Rationale and Objectives: 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. Materials and Methods: 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. Results: 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 ≤ .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 R2 estimates. Conclusion: 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.
(Less)
- author
- Kontos, Despina ; Bakic, Predrag R. LU ; Carton, Ann Katherine ; Troxel, Andrea B. ; Conant, Emily F. and Maidment, Andrew D.A.
- publishing date
- 2009-03
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- breast cancer risk estimation, Digital breast tomosynthesis, digital mammography, parenchymal texture analysis
- in
- Academic Radiology
- volume
- 16
- issue
- 3
- pages
- 16 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:59449106646
- pmid:19201357
- ISSN
- 1076-6332
- DOI
- 10.1016/j.acra.2008.08.014
- language
- English
- LU publication?
- no
- id
- 1e06d463-7c1d-4eff-9920-51c8de22610a
- date added to LUP
- 2020-11-07 13:19:31
- date last changed
- 2024-05-31 02:32:46
@article{1e06d463-7c1d-4eff-9920-51c8de22610a,
abstract = {{<p>Rationale and Objectives: 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. Materials and Methods: 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. Results: 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 ≤ .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<sup>2</sup> estimates. Conclusion: 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.</p>}},
author = {{Kontos, Despina and Bakic, Predrag R. and Carton, Ann Katherine and Troxel, Andrea B. and Conant, Emily F. and Maidment, Andrew D.A.}},
issn = {{1076-6332}},
keywords = {{breast cancer risk estimation; Digital breast tomosynthesis; digital mammography; parenchymal texture analysis}},
language = {{eng}},
number = {{3}},
pages = {{283--298}},
publisher = {{Elsevier}},
series = {{Academic Radiology}},
title = {{Parenchymal Texture Analysis in Digital Breast Tomosynthesis for Breast Cancer Risk Estimation. A Preliminary Study}},
url = {{http://dx.doi.org/10.1016/j.acra.2008.08.014}},
doi = {{10.1016/j.acra.2008.08.014}},
volume = {{16}},
year = {{2009}},
}