Related Experiment Video
Updated: May 19, 2026
Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
Published on: August 30, 2013
[Impact of breast density on computer-aided detection (CAD) of breast cancer]
Kai-yan Yang1, Xiao-juan Liu, Ren-you Zhai
1Department of Radiology, Capital University of Medical Sciences, Beijing, China.
Objective:
To evaluate the impact of breast density on computer-aided detection (CAD) for breast cancer and the CAD false-positive rate of normal controls.
Methods:
Two hundred and seventy-one histologically proven breast malignant lesions (from Feb. 2008 to Dec. 2009) and 238 randomly selected normal cases were classified by mammographic density according to the American College of Radiology breast imaging reporting and data system (BI-RADS). Mammograms of BI-RADS 1 or BI-RADS 2 density were categorized as non-dense breasts, and those of BI-RADS 3 or BI-RADS 4 density were categorized as dense breasts. Full-field digital mammography (GEMS Senographe) were performed in all patients and controls with craniocaudal (CC) and mediolateral oblique (MLO) views. Then the image data were transferred to review workstation (SenoAdvantage), and the lesions were marked by Second Look Digital CAD system (version 7.2, iCAD). The differences of sensitivity and false-positive rate between dense and non-dense breasts were compared.
Results:
Overall, the sensitivity of CAD in detection of cancers was 84.1% (228/271), there was a statistically significant difference in CAD of cancers in dense versus non-dense breasts (P = 0.015). The sensitivity of CAD in detection of mass cancers was 76.5% (186/243), in detection of calcification cancers was 79.1% (125/158), there was no statistically significant difference in CAD performance for the detection of mass cancers versus calcification cancers (P = 0.547). There was a significant difference in the CAD performance for the detection of mass cancer cases in non-dense versus dense breasts (P = 0.001), but no significant difference in the CAD for the detection of calcification cancers in non-dense versus dense breasts (P = 0.216). In the controls, the distribution of mass false-positive marks did not differ significantly between non-dense and dense breast tissue cases (P = 0.207), but the distribution of calcification false-positive marks differed significantly between non-dense and dense breast tissue cases (P = 0.001). There was a statistically significant difference of false-positive marks in non-dense versus dense breasts (P = 0.043).
Conclusions:
The sensitivity of CAD in the detection of breast cancers is impacted by breast density. There is a statistically significant difference in the CAD performance for the detection of cancer cases in non-dense versus dense breasts. The false-positive rate of CAD is lower in dense versus non-dense breasts. It appears difficult for CAD in the early detection of breast cancer in the absence of microcalcifications, particularly in dense breasts.
