Performance of Computer-aided Detection in False-Negative Screening Mammograms of Breast Cancers.
10.3348/jkrs.2004.51.4.465
- Author:
Boo Kyung HAN
1
;
Ji Young KIM
;
Jung Hee SHIN
;
Yeon Hyeon CHOE
Author Information
1. Department of Radiology, Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine. bkhan@smc.samsung.co.kr
- Publication Type:Original Article
- Keywords:
Computers, diagnostic aid;
Breast neoplasms;
Diagnostic radiology, observer performance
- MeSH:
Breast Neoplasms;
Breast*;
Diagnosis;
Humans;
Mass Screening*;
Retrospective Studies
- From:Journal of the Korean Radiological Society
2004;51(4):465-472
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: To analyze retrospectively the abnormalities visible on the false-negative screening mammograms of patients with breast cancer and to determine the performance of computer-aided detection (CAD) in the detection of cancers. MATERIALS AND METHODS: Of 108 consecutive cases of breast cancer diagnosed over a period of 6 years, of which previous screening mammograms were available, 32 retrospectively visible abnormalities (at which locations cancer later developed) were found in the previous mammograms, and which were originally reported as negative. These 32 patients ranged in age from 38 to 72 years (mean 52 years). We analyzed their previous mammographic findings, and assessed the ability of CAD to mark cancers in previous mammograms, according to the clinical presentation, the type of abnormalities and the mammographic parenchymal density. RESULTS: In these 32 previous mammograms of breast cancers (20 asymptomatic, 12 symptomatic), the retrospectively visible abnormalities were identified as densities in 22, calcifications in 8, and densities with calcifications in 2. CAD marked abnormalities in 20 (63%) of the 32 cancers with false-negative screening mammograms; 14 (70%) of the 20 subsequent screening-detected cancers, 5 (50%) of the 10 interval cancers, and 1 (50%) of the 2 cancers palpable after the screening interval. CAD marked 12 (50%) of the 24 densities and 9 (90%) of the 10 calcifications. CAD marked abnormalities in 7 (50%) of the 14 predominantly fatty breasts, and 13 (72%) of the 18 dense breasts. CONCLUSION: CAD-assisted diagnosis could potentially decrease the number of false-negative mammograms caused by the failure to recognize the cancer in the screening program, although its usefulness in the prevention of interval cancers appears to be limited.
