Breast Cancer Risk and Mammographic Density Assessed with Semiautomated and Fully Automated Methods and BI-RADS.
Purpose To compare three metrics of breast density on full-field digital mammographic (FFDM) images as predictors of future breast cancer risk. Materials and Methods This institutional review board-approved study included 125 women with invasive breast cancer and 274 age- and race-matched control subjects who underwent screening FFDM during 2004-2013 and provided informed consent. The percentage of density and dense area were assessed semiautomatically with software (Cumulus 4.0; University of Toronto, Toronto, Canada), and volumetric percentage of density and dense volume were assessed automatically with software (Volpara; Volpara Solutions, Wellington, New Zealand). Clinical Breast Imaging Reporting and Data System (BI-RADS) classifications of breast density were extracted from mammography reports. Odds ratios and 95% confidence intervals (CIs) were estimated by using conditional logistic regression stratified according to age and race and adjusted for body mass index, parity, and menopausal status, and the area under the receiver operating characteristic curve (AUC) was computed. Results The adjusted odds ratios and 95% CIs for each standard deviation increment of the percentage of density, dense area, volumetric percentage of density, and dense volume were 1.61 (95% CI: 1.19, 2.19), 1.49 (95% CI: 1.15, 1.92), 1.54 (95% CI: 1.12, 2.10), and 1.41 (95% CI: 1.11, 1.80), respectively. Odds ratios for women with extremely dense breasts compared with those with scattered areas of fibroglandular density were 2.06 (95% CI: 0.85, 4.97) and 2.05 (95% CI: 0.90, 4.64) for BI-RADS and Volpara density classifications, respectively. Clinical BI-RADS was more accurate (AUC, 0.68; 95% CI: 0.63, 0.74) than Volpara (AUC, 0.64; 95% CI: 0.58, 0.70) and continuous measures of percentage of density (AUC, 0.66; 95% CI: 0.60, 0.72), dense area (AUC, 0.66; 95% CI: 0.60, 0.72), volumetric percentage of density (AUC, 0.64; 95% CI: 0.58, 0.70), and density volume (AUC, 0.65; 95% CI: 0.59, 0.71), although the AUC differences were not statistically significant. Conclusion Mammographic density on FFDM images was positively associated with breast cancer risk by using the computer assisted methods and BI-RADS. BI-RADS classification was as accurate as computer-assisted methods for discrimination of patients from control subjects.©RSNA, 2016.
From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Abra M
AM
Jeffers
From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Weiva
W
Sieh
From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Jafi A
JA
Lipson
From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Joseph H
JH
Rothstein
From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Valerie
V
McGuire
From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Alice S
AS
Whittemore
From the Departments of Management Science and Engineering (A.M.J.) and Medicine (Biomedical Informatics Research) (D.L.R.), Stanford University, Stanford, Calif; and Departments of Health Research and Policy (W.S., J.H.R., V.M., A.S.W.) and Radiology (J.A.L., D.L.R.), Stanford University School of Medicine, 1201 Welch Rd, Office P285, Stanford, CA 94305.
Daniel L
DL
Rubin
D000328:Adult; D000368:Aged; D015992:Body Mass Index; D000071060:Breast Density; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D016022:Case-Control Studies; D055088:Early Detection of Cancer / Q000379:methods; D005260:Female; D006801:Humans; D008327:Mammography; D008875:Middle Aged; D009361:Neoplasm Invasiveness / Q000000981:diagnostic imaging; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D018570:Risk Assessment; D012984:Software