Additive Benefit of Radiomics Over Size Alone in the Distinction Between Benign Lesions and Luminal A Cancers on a Large Clinical Breast MRI Dataset

      Rationale and Objectives

      The objective of this study was to demonstrate improvement in distinguishing between benign lesions and luminal A breast cancers in a large clinical breast magnetic resonance imaging database by using quantitative radiomics over maximum linear size alone.

      Materials and Methods

      In this retrospective study, 264 benign lesions and 390 luminal A breast cancers were automatically segmented from dynamic contrast-enhanced breast magnetic resonance images. Thirty-eight radiomic features were extracted. Tenfold cross validation was performed to assess the ability to distinguish between lesions and cancers using maximum linear size alone and lesion signatures obtained with stepwise feature selection and a linear discriminant analysis classifier including and excluding size features. Area under the receiver operating characteristic curve (AUC) was used as the figure of merit.


      For maximum linear size alone, AUC and 95% confidence interval was 0.684 (0.642, 0.724) compared to 0.728 (0.687, 0.766) (P = 0.005) and 0.729 (0.689, 0.767) (P = 0.005) for lesion signature feature selection protocols including and excluding size features, respectively. The features of irregularity and entropy were chosen in all folds when size features were included and excluded. AUC for the radiomic signature using feature selection from all features was statistically equivalent to using feature selection from all features excluding size features, within an equivalence margin of 2%.


      Inclusion of multiple radiomic features, automatically extracted from magnetic resonance images, in a lesion signature significantly improved the ability to distinguish between benign lesions and luminal A breast cancers, compared to using maximum linear size alone. The radiomic features of irregularity and entropy appear to play an important but not a solitary role within the context of feature selection and computer-aided diagnosis.

      Key Words

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        • Siegel R.
        • Miller K.D.
        • Jemal A.
        Cancer Statistics, 2017.
        CA Cancer J Clin. 2017; 67: 7-30
        • Giger M.L.
        • Chan H.-P.
        • Boone J.
        Anniversary paper: history and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM.
        Med Phys. 2008; 35: 5799-5820
        • Aerts H.J.
        • Velazquez E.R.
        • Leijenaar R.T.
        • et al.
        Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
        Nat Commun. 2014; 5: 4006
        • Gillies R.J.
        • Kinahan P.E.
        • Hricak H.
        Radiomics: images are more than pictures, they are data.
        Radiology. 2016; 278: 563-577
        • Rahbar H.
        • McDonald E.S.
        • Lee J.M.
        • et al.
        How can advanced imaging be used to mitigate potential breast cancer overdiagnosis?.
        Acad Radiol. 2016; 23: 1-6
        • Giger M.L.
        Computer-aided detection/computer-aided diagnosis.
        in: Wolbarst A.B. Mossman K.L. Hendee W.R. Advances in medical physics: 2008. Medical Physics Publishing, Madison, WI2008: 143-168
        • Chen W.
        • Giger M.L.
        • Bick U.
        • et al.
        Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI.
        Med Phys. 2006; 33: 2878-2887
        • Bickelhaupt S.
        • Paech D.
        • Kickingereder P.
        • et al.
        Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography.
        J Magn Reson Imaging. 2017; 46: 604-616
        • Burnside E.S.
        • Drukker K.
        • Li H.
        • et al.
        Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to predict breast cancer pathologic stage.
        Cancer. 2016; 122: 748-757
        • Mazurowski M.A.
        • Zhang J.
        • Grimm L.J.
        • et al.
        Radiogenomic analysis of breast cancer: luminal b molecular subtype is associated with enhancement dynamics at MR imaging.
        Radiology. 2014; 273: 365-372
        • Blaschke E.
        • Abe H.
        MRI phenotype of breast cancer: kinetic assessment for molecular subtypes.
        J Magn Reson Imaging. 2015; 42: 920-924
        • Grimm L.J.
        • Zhang J.
        • Mazurowski M.A.
        Computational approach to radiogenomics of breast cancer: luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms.
        J Magn Reson Imaging. 2015; 42: 902-907
        • Bhooshan N.
        • Giger M.L.
        • Jansen S.A.
        • et al.
        Cancerous breast lesions on dynamic contrast-enhanced MR images.
        Breast Imaging. 2010; 254: 680-690
        • Wang J.
        • Kato F.
        • Oyama-Manabe N.
        • et al.
        Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: a pilot radiomics study.
        PLoS ONE. 2015; 10 (e0143308)
        • Li H.
        • Zhu Y.
        • Burnside E.S.
        • et al.
        Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set.
        Breast Cancer. 2016; 2: 16012
        • Desantis C.E.
        • Fedewa S.A.
        • Sauer A.G.
        • et al.
        Breast cancer statistics, 2015: convergence of incidence rates between black and white women.
        CA Cancer J Clin. 2016; 66: 31-42
        • Tamaki K.
        • Ishida T.
        • Miyashita M.
        • et al.
        Correlation between mammographic findings and corresponding histopathology: potential predictors for biological characteristics of breast diseases.
        Cancer Sci. 2011; 102: 2179-2185
        • Cho N.
        Molecular subtypes and imaging phenotypes of breast cancer.
        Ultrasonography. 2016; 35: 281-288
        • Chen W.
        • Giger M.L.
        • Bick U.
        A fuzzy C-means (FCM)-based approach for computerized segmentation of breast lesions in dynamic contrast-enhanced MR images.
        Acad Radiol. 2006; 13: 63-72
        • Gilhuijs K.G.
        • Giger M.L.
        • Bick U.
        Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging.
        Med Phys. 1998; 25: 1647-1654
        • Chen W.
        • Giger M.L.
        • Lan L.
        • et al.
        Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics.
        Med Phys. 2004; 31: 1076-1082
        • Chen W.
        • Giger M.L.
        • Li H.
        • et al.
        Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images.
        Magn Reson Med. 2007; 58: 562-571
        • Antropova N.
        • Huynh B.Q.
        • Giger M.L.
        A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets.
        Med Phys. 2017; 44: 5162-5171
        • Horsch K.
        • Pesce L.L.
        • Giger M.L.
        • et al.
        A scaling transformation for classifier output based on likelihood ratio: applications to a CAD workstation for diagnosis of breast cancer.
        Med Phys. 2012; 39: 2787-2804
        • Metz C.E.
        Basic principles of ROC analysis.
        Semin Nucl Med. 1978; 8: 283-298
        • Metz C.E.
        • Herman B.A.
        • Shen J.H.
        Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data.
        Stat Med. 1998; 17: 1033-1053
        • Metz C.E.
        ROCkit 0.9b [Internet].
        (Available at:) (Accessed December 15, 2017)
        • Holm S.
        A simple sequentially rejective multiple test procedure.
        Scand J Stat. 1979; 6: 65-70
        • Ahn S.
        • Park S.H.
        • Lee K.H.
        How to demonstrate similarity by using noninferiority and equivalence statistical testing in radiology research.
        Radiology. 2013; 267: 328-338
      1. D'Orsi C. Sickles E. Mendelson E. ACR BI-RADS® Atlas, Breast Imaging Reporting and Data System. American College of Radiology, Reston, VA2013
        • Navarro Vilar L.
        • Alandete Germán S.P.
        • Medina García R.
        • et al.
        MR imaging findings in molecular subtypes of breast cancer according to BIRADS system.
        Breast J. 2017; 23: 421-428
        • Whitney H.
        • Drukker K.
        • Edwards A.
        • et al.
        Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers.
        in: Proceedings of the 14th International Workshop on Breast Imaging. 2018 (p. in press)
        • Whitney H.
        • Drukker K.
        • Edwards A.
        • et al.
        Robustness of radiomic breast features of benign lesions and luminal A cancers across MR magnet strengths.
        in: Mori K. Petrick N. Medical Imaging 2018: Computer-Aided Diagnosis. SPIE, 2018