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  • br Burnside ES Drukker K Li H et


    9. Burnside ES, Drukker K, Li H, et al. Using computer-extracted image phenotypes from tumors on breast magnetic resonance imaging to pre-dict breast cancer pathologic stage. Cancer 2016; 122:748–757.
    10. Mazurowski MA, Zhang J, Grimm LJ, et al. Radiogenomic analysis of breast cancer: luminal b molecular subtype is associated with enhance-ment dynamics at MR imaging. Radiology 2014; 273:365–372.
    11. Blaschke E, Abe H. MRI Thonzonium Bromide of breast cancer: kinetic assessment for molecular subtypes. J Magn Reson Imaging 2015; 42:920–924. 12. Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radioge-nomics 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. 13. Bhooshan N, Giger ML, Jansen SA, et al. Cancerous breast lesions on dynamic contrast-enhanced MR images. Breast Imaging 2010; 254:680– 690.
    14. Wang J, Kato F, Oyama-Manabe N, et al. Identifying triple-negative breast cancer using background parenchymal enhancement heteroge-neity on dynamic contrast-enhanced MRI: a pilot radiomics study. PLoS ONE 2015; 10. e0143308.
    15. Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the predic-tion of molecular classifications of breast cancer subtypes in the TCGA/ TCIA data set. Breast Cancer 2016; 2:16012.
    17. 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.
    19. Chen W, Giger ML, 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.
    20. Gilhuijs KG, Giger ML, Bick U. Computerized analysis of breast lesions in three dimensions using dynamic magnetic-resonance imaging. Med Phys 1998; 25:1647–1654.
    22. Chen W, Giger ML, Li H, et al. Volumetric texture analysis of breast lesions on contrast-enhanced magnetic resonance images. Magn Reson Med 2007; 58:562–571.
    23. Antropova N, Huynh BQ, Giger ML. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med Phys 2017; 44:5162–5171.
    24. Horsch K, Pesce LL, Giger ML, et al. A scaling transformation for classi-fier output based on likelihood ratio: applications to a CAD workstation for diagnosis of breast cancer. Med Phys 2012; 39:2787–2804.
    26. Metz CE, Herman BA, Shen JH. Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distrib-uted data. Stat Med 1998; 17:1033–1053.
    29. Ahn S, Park SH, Lee KH. How to demonstrate similarity by using nonin-feriority and equivalence statistical testing in radiology research. Radiol-ogy 2013; 267:328–338.
    30. D'Orsi C, Sickles E, Mendelson E, eds. ACR BI-RADS Atlas, Breast Imaging Reporting and Data System, Reston, VA: American College of Radiology; 2013. 31. Navarro Vilar L, Alandete German SP, Medina García R, et al. MR imag-ing findings in molecular subtypes of breast cancer according to BIRADS system. Breast J 2017; 23:421–428.
    32. Whitney H, Drukker K, Edwards A, et al. Effect of biopsy on the MRI radiomics classification of benign lesions and luminal A cancers. In: In: 
    Proceedings of the 14th International Thonzonium Bromide Workshop on Breast Imaging; 2018. p. in press.
    33. 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, eds. Medical Imaging 2018: Computer-Aided Diagnosis, SPIE; 2018.
    Academic Radiology, Vol 26, No 2, February 2019 RADIOMICS IN DIAGNOSING LUMINAL A CANCERS
    Image Feature Description
    Volume (mm3) Volume of lesion Effective greatest dimension (mm) Greatest dimension of a sphere with the same volume as the lesion Surface area (mm2) Lesion surface area Maximum linear size (mm) Maximum distance between any two voxels in the lesion Shape
    Sphericity Similarity of the lesion shape to a sphere Irregularity Deviation of the lesion surface from the surface of a sphere Surface area-to-volume ratio (1/mm) Ratio of surface area to volume Morphology
    Margin sharpness Mean of the image gradient at the lesion margin Variance of margin sharpness Variance of the image gradient at the lesion margin Variance of radial gradient histogram Degree to which the enhancement structure extends in a radial
    pattern originating from the center of the lesion Enhancement texture
    Angular second moment (energy)