Skip to main content
construction release_alert
Scholars@Duke will be undergoing maintenance April 11-15. Some features may be unavailable during this time.
cancel
Journal cover image

Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.

Publication ,  Journal Article
Li, H; Giger, ML; Lan, L; Bancroft Brown, J; MacMahon, A; Mussman, M; Olopade, OI; Sennett, C
Published in: J Digit Imaging
October 2012

The purpose of this study was to demonstrate the robustness of our prior computerized texture analysis method for breast cancer risk assessment, which was developed initially on a limited dataset of screen-film mammograms. This current study investigated the robustness by (1) evaluating on a large clinical dataset, (2) using full-field digital mammograms (FFDM) as opposed to screen-film mammography, and (3) incorporating analyses over two types of high-risk patient sets, as well as patients at low risk for breast cancer. The evaluation included the analyses on the parenchymal patterns of women at high risk of developing of breast cancer, including both BRCA1/2 gene mutation carriers and unilateral cancer patients, and of women at low risk of developing breast cancer. A total of 456 cases, including 53 women with BRCA1/2 gene mutations, 75 women with unilateral cancer, and 328 low-risk women, were retrospectively collected under an institutional review board approved protocol. Regions-of-interest (ROIs), were manually selected from the central breast region immediately behind the nipple. These ROIs were subsequently used in computerized feature extraction to characterize the mammographic parenchymal patterns in the images. Receiver operating characteristic analysis was used to assess the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk subjects. In a round robin evaluation on the FFDM dataset with Bayesian artificial neural network analysis, AUC values of 0.82 (95% confidence interval [0.75, 0.88]) and 0.73 (95% confidence interval [0.67, 0.78]) were obtained between BRCA1/2 gene mutation carriers and low-risk women, and between unilateral cancer and low-risk women, respectively. These results from computerized texture analysis on digital mammograms demonstrated that high-risk and low-risk women have different mammographic parenchymal patterns. On this large clinical dataset, we validated our methods for quantitative analyses of mammographic patterns on FFDM, statistically demonstrating again that women at high risk tend to have dense breasts with coarse and low-contrast texture patterns.

Duke Scholars

Published In

J Digit Imaging

DOI

EISSN

1618-727X

Publication Date

October 2012

Volume

25

Issue

5

Start / End Page

591 / 598

Location

United States

Related Subject Headings

  • Young Adult
  • Risk Management
  • Risk Assessment
  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Mammography
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Li, H., Giger, M. L., Lan, L., Bancroft Brown, J., MacMahon, A., Mussman, M., … Sennett, C. (2012). Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets. J Digit Imaging, 25(5), 591–598. https://doi.org/10.1007/s10278-012-9452-z
Li, Hui, Maryellen L. Giger, Li Lan, Jeremy Bancroft Brown, Aoife MacMahon, Mary Mussman, Olufunmilayo I. Olopade, and Charlene Sennett. “Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.J Digit Imaging 25, no. 5 (October 2012): 591–98. https://doi.org/10.1007/s10278-012-9452-z.
Li H, Giger ML, Lan L, Bancroft Brown J, MacMahon A, Mussman M, et al. Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets. J Digit Imaging. 2012 Oct;25(5):591–8.
Li, Hui, et al. “Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets.J Digit Imaging, vol. 25, no. 5, Oct. 2012, pp. 591–98. Pubmed, doi:10.1007/s10278-012-9452-z.
Li H, Giger ML, Lan L, Bancroft Brown J, MacMahon A, Mussman M, Olopade OI, Sennett C. Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets. J Digit Imaging. 2012 Oct;25(5):591–598.
Journal cover image

Published In

J Digit Imaging

DOI

EISSN

1618-727X

Publication Date

October 2012

Volume

25

Issue

5

Start / End Page

591 / 598

Location

United States

Related Subject Headings

  • Young Adult
  • Risk Management
  • Risk Assessment
  • Retrospective Studies
  • Radiographic Image Interpretation, Computer-Assisted
  • Radiographic Image Enhancement
  • ROC Curve
  • Nuclear Medicine & Medical Imaging
  • Middle Aged
  • Mammography