Literature DB >> 21771961

Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment.

Despina Kontos1, Lynda C Ikejimba, Predrag R Bakic, Andrea B Troxel, Emily F Conant, Andrew D A Maidment.   

Abstract

PURPOSE: To correlate the parenchymal texture features at digital breast tomosynthesis (DBT) and digital mammography with breast percent density (PD), an established breast cancer risk factor, in a screening population of women.
MATERIALS AND METHODS: This HIPAA-compliant study was approved by the institutional review board. Bilateral DBT images and digital mammograms from 71 women (mean age, 54 years; age range, 34-75 years) with negative or benign findings at screening mammography were retrospectively collected from a separate institutional review board-approved DBT screening trial (performed from July 2007 to March 2008) in which all women had given written informed consent. Parenchymal texture features of skewness, coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the retroareolar region. Principal component analysis (PCA) was applied to obtain orthogonal texture components. Mammographic PD was estimated with software. Correlation analysis and multiple linear regression with generalized estimating equations were performed to determine the association between texture features and breast PD. Regression was adjusted for age to determine the independent association of texture to breast PD when age was also considered as a predictor variable.
RESULTS: Texture feature correlations to breast PD were stronger with DBT than with digital mammography. Statistically significant correlations (P < .001) were observed for contrast (r = 0.48), energy (r = -0.47), and homogeneity (r = -0.56) at DBT and for contrast (r = 0.26), energy (r = -0.26), and homogeneity (r = -0.33) at digital mammography. Multiple linear regression analysis of PCA texture components as predictors of PD also demonstrated significantly stronger associations with DBT. The association was strongest when age was also considered as a predictor of PD (R² = 0.41 for DBT and 0.28 for digital mammography; P < .001).
CONCLUSION: Parenchymal texture features are more strongly correlated to breast PD in DBT than in digital mammography. The authors' long-term hypothesis is that parenchymal texture analysis with DBT will result in quantitative imaging biomarkers that can improve the estimation of breast cancer risk. © RSNA, 2011.

Entities:  

Mesh:

Year:  2011        PMID: 21771961      PMCID: PMC3176420          DOI: 10.1148/radiol.11100966

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  65 in total

1.  Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment.

Authors:  Hui Li; Maryellen L Giger; Olufunmilayo I Olopade; Li Lan
Journal:  Acad Radiol       Date:  2007-05       Impact factor: 3.173

2.  Scatter radiation in digital tomosynthesis of the breast.

Authors:  Ioannis Sechopoulos; Sankararaman Suryanarayanan; Srinivasan Vedantham; Carl J D'Orsi; Andrew Karellas
Journal:  Med Phys       Date:  2007-02       Impact factor: 4.071

3.  Experimental validation of a three-dimensional linear system model for breast tomosynthesis.

Authors:  Bo Zhao; Jun Zhou; Yue-Houng Hu; Thomas Mertelmeier; Jasmina Ludwig; Wei Zhao
Journal:  Med Phys       Date:  2009-01       Impact factor: 4.071

4.  Evidence-based breast cancer prevention: the importance of individual risk.

Authors:  Karla Kerlikowske
Journal:  Ann Intern Med       Date:  2009-11-17       Impact factor: 25.391

5.  Quantitative assessment of percent breast density: analog versus digital acquisition.

Authors:  Jennifer A Harvey
Journal:  Technol Cancer Res Treat       Date:  2004-12

6.  Computerized texture analysis of mammographic parenchymal patterns of digitized mammograms.

Authors:  Hui Li; Maryellen L Giger; Olufunmilayo I Olopade; Anna Margolis; Li Lan; Michael R Chinander
Journal:  Acad Radiol       Date:  2005-07       Impact factor: 3.173

7.  Importance of point-by-point back projection correction for isocentric motion in digital breast tomosynthesis: relevance to morphology of structures such as microcalcifications.

Authors:  Ying Chen; Joseph Y Lo; James T Dobbins
Journal:  Med Phys       Date:  2007-10       Impact factor: 4.071

8.  Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.

Authors:  Jeffrey A Tice; Steven R Cummings; Rebecca Smith-Bindman; Laura Ichikawa; William E Barlow; Karla Kerlikowske
Journal:  Ann Intern Med       Date:  2008-03-04       Impact factor: 25.391

9.  Texture features from mammographic images and risk of breast cancer.

Authors:  Armando Manduca; Michael J Carston; John J Heine; Christopher G Scott; V Shane Pankratz; Kathy R Brandt; Thomas A Sellers; Celine M Vachon; James R Cerhan
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2009-03-03       Impact factor: 4.254

Review 10.  Mammographic density. Potential mechanisms of breast cancer risk associated with mammographic density: hypotheses based on epidemiological evidence.

Authors:  Lisa J Martin; Norman F Boyd
Journal:  Breast Cancer Res       Date:  2008-01-09       Impact factor: 6.466

View more
  22 in total

1.  Effect of Vitamin D Supplementation on Breast Cancer Biomarkers: CALGB 70806 (Alliance) Study Design and Baseline Data.

Authors:  Ogheneruona Apoe; Sin-Ho Jung; Heshan Liu; Drew K Seisler; Jayne Charlamb; Patricia Zekan; Lili X Wang; Gary W Unzeitig; Judy Garber; James Marshall; Marie Wood
Journal:  Am J Hematol Oncol       Date:  2016-07

Review 2.  Novel Quantitative Imaging for Predicting Response to Therapy: Techniques and Clinical Applications.

Authors:  Kaustav Bera; Vamsidhar Velcheti; Anant Madabhushi
Journal:  Am Soc Clin Oncol Educ Book       Date:  2018-05-23

Review 3.  A review of breast tomosynthesis. Part II. Image reconstruction, processing and analysis, and advanced applications.

Authors:  Ioannis Sechopoulos
Journal:  Med Phys       Date:  2013-01       Impact factor: 4.071

Review 4.  Measurement of breast density with digital breast tomosynthesis--a systematic review.

Authors:  E U Ekpo; M F McEntee
Journal:  Br J Radiol       Date:  2014-08-22       Impact factor: 3.039

5.  Quantitative assessment of microcalcification cluster image quality in digital breast tomosynthesis, 2-dimensional and synthetic mammography.

Authors:  Andreas E Petropoulos; Spyros G Skiadopoulos; Anna N Karahaliou; Gerasimos A T Messaris; Nikolaos S Arikidis; Lena I Costaridou
Journal:  Med Biol Eng Comput       Date:  2019-12-07       Impact factor: 2.602

6.  Association between Breast Parenchymal Complexity and False-Positive Recall From Digital Mammography Versus Breast Tomosynthesis: Preliminary Investigation in the ACRIN PA 4006 Trial.

Authors:  Shonket Ray; Lin Chen; Brad M Keller; Jinbo Chen; Emily F Conant; Despina Kontos
Journal:  Acad Radiol       Date:  2016-05-25       Impact factor: 3.173

7.  Breast Tissue Classification in Digital Tomosynthesis Images Based on Global Gradient Minimization and Texture Features.

Authors:  Xulei Qin; Guolan Lu; Ioannis Sechopoulos; Baowei Fei
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2014-03-21

Review 8.  Imaging-based Biomarkers for Predicting and Evaluating Cancer Immunotherapy Response.

Authors:  Minghao Wu; Yanyan Zhang; Yuwei Zhang; Ying Liu; Mingjie Wu; Zhaoxiang Ye
Journal:  Radiol Imaging Cancer       Date:  2019-11-29

9.  Comparison study of reconstruction algorithms for prototype digital breast tomosynthesis using various breast phantoms.

Authors:  Ye-seul Kim; Hye-suk Park; Haeng-Hwa Lee; Young-Wook Choi; Jae-Gu Choi; Hak Hee Kim; Hee-Joung Kim
Journal:  Radiol Med       Date:  2015-09-18       Impact factor: 3.469

10.  Digital Breast Tomosynthesis guided Near Infrared Spectroscopy: Volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions.

Authors:  Srinivasan Vedantham; Linxi Shi; Kelly E Michaelsen; Venkataramanan Krishnaswamy; Brian W Pogue; Steven P Poplack; Andrew Karellas; Keith D Paulsen
Journal:  Biomed Phys Eng Express       Date:  2015-10-27
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.