Computerized Analysis of Mammographic Parenchymal Patterns on a Large Clinical Dataset of Full-Field Digital Mammograms: Robustness Study with Two High-Risk Datasets

Abstract

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-ofinterest (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 lowcontrast texture patterns.

Comparative analysis of image-based phenotypes of mammographic density and parenchymal patterns in distinguishing betweenBRCA1/2cases, unilateral cancer cases, and controls

“…1, breast PD over the entire mammographic image, and coarseness and contrast within each ROI, as well as other texture features, were calculated to characterize the mammographic parenchymal patterns. These computer-extracted texture features were used to assess the image local composition (density-related measures), image contrast, image homogeneity, and image coarseness of the breast parenchyma, as previously described, [16][17][18][19][20][21]26 and served as image-based phenotypes.…”

“…[9][10][11][12][13] Investigators have also studied breast parenchymal patterns as characterized by computerized texture analysis on digitized screen-film mammograms and full-field digital mammograms (FFDMs). [14][15][16][17][18][19][20][21] Results indicate that women at high risk of developing breast cancer tended to have mammographic parenchymal patterns that were coarse and low in contrast. [16][17][18][19][20][21] The purpose of this current study was to investigate the additional value of parenchymal pattern characteristics to breast percent density (PD) in characterizing and distinguishing between women at high risk for breast cancer and low-risk controls.…”

“…[14][15][16][17][18][19][20][21] Results indicate that women at high risk of developing breast cancer tended to have mammographic parenchymal patterns that were coarse and low in contrast. [16][17][18][19][20][21] The purpose of this current study was to investigate the additional value of parenchymal pattern characteristics to breast percent density (PD) in characterizing and distinguishing between women at high risk for breast cancer and low-risk controls. The image-based parenchymal phenotypes of coarseness and contrast, as well as other texture features, were calculated from digital radiographic texture analysis (RTA) applied to FFDMs.…”

Texture analysis of diffusion weighted imaging for the evaluation of glioma heterogeneity based on different regions of interest

“…It has shown promise in the field of oncology diagnosis (11,12), quantifying tumor heterogeneity (13), separating tumor tissue from surrounding tissue (14,15), tumor grading and classification (16)(17)(18), and (19,20). Until now, some reports have been published regarding tumor heterogeneity in intracranial tumors using CT and MRI texture analysis.…”

Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts

“…1,2 A considerable number of studies aiming to quantify breast cancer risk based on mammographic images has been published. [3][4][5][6] While women with "dense breasts" are at higher risk of developing breast cancer, conventional x-ray mammography performance is considerably lower for these women. Although the overall sensitivity of mammography is 70%-90%, the sensitivity can range from 30% to 98% depending on whether the breast density is extremely dense or mostly fatty replaced.…”