Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: Effect of ROI size and location

Abstract

The long-term goal of our research is to develop computerized radiographic markers for assessing breast density and parenchymal patterns that may be used together with clinical measures for determining the risk of breast cancer and assessing the response to preventive treatment. In our earlier studies, we found that women at high risk tended to have dense breasts with mammographic patterns that were coarse and low in contrast. With our method, computerized texture analysis is performed on a region of interest (ROI) within the mammographic image. In our current study, we investigate the effect of ROI size and ROI location on the computerized texture features obtained from 90 subjects (30 BRCA1/BRCA2 gene-mutation carriers and 60 age-matched women deemed to be at low risk for breast cancer). Mammograms were digitized at 0.1 mm pixel size and various ROI sizes were extracted from different breast regions in the craniocaudal (CC) view. Seventeen features, which characterize the density and texture of the parenchymal patterns, were extracted from the ROIs on these digitized mammograms. Stepwise feature selection and linear discriminant analysis were applied to identify features that differentiate between the low-risk women and the BRCA1/BRCA2 gene-mutation carriers. ROC analysis was used to assess the performance of the features in the task of distinguishing between these two groups. Our results show that there was a statistically significant decrease in the performance of the computerized texture features, as the ROI location was varied from the central region behind the nipple. However, we failed to show a statistically significant decrease in the performance of the computerized texture features with decreasing ROI size for the range studied.

Power Spectral Analysis of Mammographic Parenchymal Patterns for Breast Cancer Risk Assessment

“…The laser film digitizer was routinely calibrated to assure that film optical density in the range of 0.0 to 3.5 is linearly translated into digital pixel values. ROIs, 256 by 256 pixels in size, were manually selected from the central breast region behind the nipple, 23 as they usually include the most dense part of the breast. These ROIs were used for subsequent power law spectral analyses.…”

“…Mammographic parenchymal patterns have been studied extensively [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] to demonstrate the relationship between breast density and the risk of developing breast cancer. Wolfe 10 described the mammographic parenchymal patterns with four categories (N1, P1, P2, and DY) based on the radiographic appearance of prominent ducts and dysplasia.…”

“…Visual estimation of the percentage of fibroglandular tissue on the mammogram as breast density has also been used to characterize the breast parenchymal patterns. 12,13 Quantitative breast density estimation and mammographic pattern characterization based on computerized texture analyses [14][15][16][17][18][19][20][21][22][23][24][25] have been also investigated using digitized mammograms. Numerous studies 13,[18][19][20] have shown that increased mammographic breast density yields as high as a five-to sixfold increase in risk of developing breast cancer.…”

“…Our results also suggested that there was a statistically significant decrease in the performance of the computerized texture features in the task of distinguishing between high-risk and low-risk women groups, as the ROI location was varied from the central region behind the nipple. 23 Power law spectrum of the form P f ð Þ ¼ B f has recently been shown by Burgess et al to be related to the background parenchymal pattern of breast structure on mammography. 26, 27 Thus, in our current study, we investigate such power law spectral analysis on the digitized mammograms of two groups of women-BRCA1/BRCA2 gene mutation carriers and low-risk women.…”

A Complementary Method for the Detection of Osteoblastic Metastases on Digitized Radiographs

“…6,7 ROIs of 100 Â 25 pixels were selected to characterize the healthy bone. It was the minimum area that included only one kind of tissue (cortical, trabecular, or flat bone).…”