Original Investigation| Volume 23, ISSUE 1, P62-69, January 2016

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Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy

Published:October 27, 2015DOI:

      Rationale and Objectives

      The discovery of germline genetic variants associated with breast cancer has engendered interest in risk stratification for improved, targeted detection and diagnosis. However, there has yet to be a comparison of the predictive ability of these genetic variants with mammography abnormality descriptors.

      Materials and Methods

      Our institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study utilized a personalized medicine registry in which participants consented to provide a DNA sample and to participate in longitudinal follow-up. In our retrospective, age-matched, case-controlled study of 373 cases and 395 controls who underwent breast biopsy, we collected risk factors selected a priori based on the literature, including demographic variables based on the Gail model, common germline genetic variants, and diagnostic mammography findings according to Breast Imaging Reporting and Data System (BI-RADS). We developed predictive models using logistic regression to determine the predictive ability of (1) demographic variables, (2) 10 selected genetic variants, or (3) mammography BI-RADS features. We evaluated each model in turn by calculating a risk score for each patient using 10-fold cross-validation, used this risk estimate to construct Receiver Operator Characteristic Curve (ROC) curves, and compared the area under the ROC curve (AUC) of each using the DeLong method.


      The performance of the regression model using demographic risk factors was not statistically different from the model using genetic variants (P = 0.9). The model using mammography features (AUC = 0.689) was superior to both the demographic model (AUC = .598; P < 0.001) and the genetic model (AUC = .601; P < 0.001).


      BI-RADS features exceeded the ability of demographic and 10 selected germline genetic variants to predict breast cancer in women recommended for biopsy.

      Key Words

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        • Gail M.H.
        • Brinton L.A.
        • Byar D.P.
        • et al.
        Projecting individualized probabilities of developing breast cancer for white females who are being examined annually.
        J Natl Cancer Inst. 1989; 81: 1879-1886
        • Barlow W.E.
        • White E.
        • Ballard-Barbash R.
        • et al.
        Prospective breast cancer risk prediction model for women undergoing screening mammography.
        J Natl Cancer Inst. 2006; 98: 1204-1214
        • Pashayan N.
        • Duffy S.W.
        • Chowdhury S.
        • et al.
        Polygenic susceptibility to prostate and breast cancer: implications for personalised screening.
        Br J Cancer. 2011; 104: 1656-1663
        • Baker J.A.
        • Kornguth P.J.
        • Lo J.Y.
        • et al.
        Breast cancer: prediction with artificial neural network based on BI-RADS® standardized lexicon.
        Radiology. 1995; 196: 817-822
        • Liberman L.
        • Abramson A.F.
        • Squires F.B.
        • et al.
        The breast imaging reporting and data system: positive predictive value of mammographic features and final assessment categories.
        AJR Am J Roentgenol. 1998; 171: 35-40
        • Burnside E.S.
        • Rubin D.L.
        • Fine J.P.
        • et al.
        Bayesian network to predict breast cancer risk of mammographic microcalcifications and reduce number of benign biopsy results: initial experience.
        Radiology. 2006; 240: 666-673
        • Chhatwal J.
        • Alagoz O.
        • Lindstrom M.J.
        • et al.
        A logistic regression model based on the national mammography database format to aid breast cancer diagnosis.
        AJR Am J Roentgenol. 2009; 192: 1117-1127
        • Timmers J.M.
        • Verbeek A.L.
        • IntHout J.
        • et al.
        Breast cancer risk prediction model: a nomogram based on common mammographic screening findings.
        Eur Radiol. 2013; 23: 2413-2419
        • Linger R.J.
        • Kruk P.A.
        BRCA1 16 years later: risk-associated BRCA1 mutations and their functional implications.
        FEBS J. 2010; 277: 3086-3096
        • Pharoah P.D.
        • Antoniou A.C.
        • Easton D.F.
        • et al.
        Polygenes, risk prediction, and targeted prevention of breast cancer.
        N Engl J Med. 2008; 358: 2796-2803
        • Wacholder S.
        • Hartge P.
        • Prentice R.
        • et al.
        Performance of common genetic variants in breast-cancer risk models.
        N Engl J Med. 2010; 362: 986-993
        • Gail M.H.
        Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk.
        J Natl Cancer Inst. 2008; 100: 1037-1041
        • Gail M.H.
        Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model.
        J Natl Cancer Inst. 2009; 101: 959-963
        • Devilee P.
        • Rookus M.A.
        A tiny step closer to personalized risk prediction for breast cancer.
        N Engl J Med. 2010; 362: 1043-1045
        • Tamimi R.M.
        • Cox D.
        • Kraft P.
        • et al.
        Breast cancer susceptibility loci and mammographic density.
        Breast Cancer Res. 2008; 10: R66
        • Darabi H.
        • Czene K.
        • Zhao W.
        • et al.
        Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement.
        Breast Cancer Res. 2012; 14: R25
        • Armstrong K.
        • Handorf E.A.
        • Chen J.
        • et al.
        Breast cancer risk prediction and mammography biopsy decisions: a model-based study.
        Am J Prev Med. 2013; 44: 15-22
        • McCarty C.A.
        • Wilke R.A.
        • Giampietro P.F.
        • et al.
        Marshfield Clinic Personalized Medicine Research Project (PMRP): design, methods and recruitment for a large population-based biobank.
        Personalized Med. 2005; 2: 49-79
        • Wu Y.
        • Alagoz O.
        • Ayvaci M.U.
        • et al.
        A comprehensive methodology for determining the most informative mammographic features.
        J Digit Imaging. 2013; 26: 941-947
        • Kobayashi S.
        • Sugiura H.
        • Ando Y.
        • et al.
        Reproductive history and breast cancer risk.
        Breast Cancer. 2012; 19: 302-308
        • McCarty C.A.
        • Chisholm R.L.
        • Chute C.G.
        • et al.
        The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies.
        BMC Med Genomics. 2011; 4: 13
        • Easton D.F.
        • Pooley K.A.
        • Dunning A.M.
        • et al.
        Genome-wide association study identifies novel breast cancer susceptibility loci.
        Nature. 2007; 447: 1087-1093
        • Hunter D.J.
        • Kraft P.
        • Jacobs K.B.
        • et al.
        A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer.
        Nat Genet. 2007; 39: 870-874
        • American College of Radiology
        Breast Imaging Reporting And Data System (BI-RADS®). 4th ed. American College of Radiology, Reston VA2003
        • American College of Radiology
        Breast Imaging Reporting And Data System (BI-RADS). 3rd ed. American College of Radiology, Reston VA1998
        • Houssam N.
        • Ryan W.
        • Elizabeth B.
        • et al.
        Information Extraction for Clinical Data Mining: A Mammography Case Study.
        (In: Proceedings of the 2009 IEEE International Conference on Data Mining Workshops: IEEE Computer Society)2009
        • Percha B.
        • Nassif H.
        • Lipson J.
        • et al.
        Automatic classification of mammography reports by BI-RADS breast tissue composition class.
        J Am Med Inform Assoc. 2012; 19: 913-916
        • Swets J.A.
        Measuring the accuracy of diagnostic systems.
        Science. 1988; 240: 1285-1293
        • Burnside E.S.
        • Sickles E.A.
        • Bassett L.W.
        • et al.
        The ACR BI-RADS experience: learning from history.
        J Am Coll Radiol. 2009; 6: 851-860
        • Rubin D.B.
        Multiple imputation for nonresponse in surveys.
        John Wiley & Sons, New York1987
        • van Buuren S.
        Flexible imputation of missing data.
        CRC/Chapman & Hall Interdisciplinary Statistics Series. Chapman and Hall/CRC, Boca Raton, FL2012
        • van Buuren S.
        • Oudshoorn C.G.M.
        Multivariate Imputation by Chained Equations MICE V1.0 User's manual.
        (Leiden, The Netherlands: TNO Prevention and Health; June)2000 (Report No.: 90-6743-677-1)
        • van Buuren S.
        • Groothuis-Oudshoorn K.
        mice: Multivariate Imputation by Chained Equations in R.
        J Stat Softw. 2011; 45: 1-67
        • Van Buuren S.
        • Oudshoorn C.G.M.
        Multivariate imputation by chained equations: MICE V1.0 user's manual.
        TNO Prevention and Health, Leiden2000
        • DeLong E.R.
        • DeLong D.M.
        • Clarke-Pearson D.L.
        Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.
        Biometrics. 1988; 44: 837-845
        • R Core Team
        R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria2013 ([3.0.1])
        • Tice J.A.
        • Cummings S.R.
        • Smith-Bindman R.
        • et al.
        Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model.
        Ann Intern Med. 2008; 148: 337-347
        • Berg W.A.
        • Arnoldus C.L.
        • Teferra E.
        • et al.
        Biopsy of amorphous breast calcifications: pathologic outcome and yield at stereotactic biopsy.
        Radiology. 2001; 221: 495-503
        • Burnside E.S.
        • Ochsner J.E.
        • Fowler K.J.
        • et al.
        Use of microcalcification descriptors in BI-RADS 4th edition to stratify risk of malignancy.
        Radiology. 2007; 242: 388-395
        • Michailidou K.
        • Hall P.
        • Gonzalez-Neira A.
        • et al.
        Large-scale genotyping identifies 41 new loci associated with breast cancer risk.
        Nat Genet. 2013; 45 (361e1-2): 353-361