• Liu Z, Wang S, Dong D, Wei J, Fang C, Zhou X, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics. 2019;9:1303–22.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, et al. Radiomics: from qualitative to quantitative imaging. Br J Radiol. 2020;93:20190948.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Conti A, Duggento A, Indovina I, Guerrisi M, Toschi N. Radiomics in breast cancer classification and prediction. Semin Cancer Biol. 2020;72:238–50.

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • Lee SH, Park H, Ko ES. Radiomics in breast imaging from techniques to clinical applications: a review. Korean J Radiol. 2020;21:779–92.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RGPM, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48:441–6.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, et al. Radiomics: the process and the challenges. Magn Reson Imaging. 2012;30:1234–48.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2015;278:563–77.

    PubMed 
    Article 

    Google Scholar
     

  • Valdora F, Houssami N, Rossi F, Calabrese M, Tagliafico AS. Rapid review: radiomics and breast cancer. Breast Cancer Res Tr. 2018;169:217–29.

    Article 

    Google Scholar
     

  • Dromain C, Thibault F, Muller S, Rimareix F, Delaloge S, Tardivon A, et al. Dual-energy contrast-enhanced digital mammography: initial clinical results. Eur Radiol. 2011;21:565–74.

    PubMed 
    Article 

    Google Scholar
     

  • Ghaderi KF, Phillips J, Perry H, Lotfi P, Mehta TS. Contrast-enhanced mammography: current applications and future directions. Radiographics. 2019;39:1907–20.

    PubMed 
    Article 

    Google Scholar
     

  • Lalji UC, Jeukens CRLPN, Houben I, Nelemans PJ, van Engen RE, van Wylick E, et al. Evaluation of low-energy contrast-enhanced spectral mammography images by comparing them to full-field digital mammography using EUREF image quality criteria. Eur Radiol. 2015;25:2813–20.

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Francescone MA, Jochelson MS, Dershaw DD, Sung JS, Hughes MC, Zheng J, et al. Low energy mammogram obtained in contrast-enhanced digital mammography (CEDM) is comparable to routine full-field digital mammography (FFDM). Eur J Radiol. 2014;83:1350–5.

    PubMed 
    Article 

    Google Scholar
     

  • Fanizzi A, Losurdo L, Basile TMA, Bellotti R, Bottigli U, Delogu P, et al. Fully automated support system for diagnosis of breast Cancer in contrast-enhanced spectral mammography images. J Clin Med. 2019;8:891.

    PubMed Central 
    Article 

    Google Scholar
     

  • Danala G, Patel B, Aghaei F, Heidari M, Li J, Wu T, et al. Classification of breast masses using a computer-aided diagnosis scheme of contrast enhanced digital mammograms. Ann Biomed Eng. 2018;46:1419–31.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Patel BK, Ranjbar S, Wu T, Pockaj BA, Li J, Zhang N, et al. Computer-aided diagnosis of contrast-enhanced spectral mammography: a feasibility study. Eur J Radiol. 2018;98:207–13.

    PubMed 
    Article 

    Google Scholar
     

  • Fusco R, Vallone P, Filice S, Granata V, Petrosino T, Rubulotta MR, et al. Radiomic features analysis by digital breast tomosynthesis and contrast-enhanced dual-energy mammography to detect malignant breast lesions. Biomed Signal Process Control. 2019;53:101568.

    Article 

    Google Scholar
     

  • Losurdo L, Fanizzi A, Basile TMA, Bellotti R, Bottigli U, Dentamaro R, et al. Radiomics analysis on contrast-enhanced spectral mammography images for breast Cancer diagnosis: a pilot study. Entropy. 2019;21:1110.

    PubMed Central 
    Article 

    Google Scholar
     

  • Lin F, Wang Z, Zhang K, Yang P, Ma H, Shi Y, et al. Contrast-enhanced spectral mammography-based Radiomics Nomogram for identifying benign and malignant breast lesions of Sub-1 cm. Front Oncol. 2020;10:573630.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Limkin EJ, Sun R, Dercle L, Zacharaki EI, Robert C, Reuzé S, et al. Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology. Ann Oncol. 2017;28:1191–206.

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Verma V, Simone CB 2nd, Krishnan S, Lin SH, Yang J, Hahn SM. The rise of Radiomics and implications for oncologic management. J Natl Cancer Inst. 2017;109.

  • D’Orsi CJSE, Mendelson EB, Morris EA. ACR BI-RADS® atlas: breast imaging reporting and data system. Reston: American College of Radiology; 2013.


    Google Scholar
     

  • D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA. ACR BI-RADS Atlas. Breast Imaging Reporting and Data System 2013.

  • Bhimani C, Li L, Liao L, Roth RG, Tinney E, Germaine P. Contrast-enhanced spectral mammography: modality-specific artifacts and other factors which may interfere with image quality. Acad Radiol. 2017;24:89–94.

    PubMed 
    Article 

    Google Scholar
     

  • Nori J, Gill MK, Vignoli C, Bicchierai G, De Benedetto D, Di Naro F, et al. Artefacts in contrast enhanced digital mammography: how can they affect diagnostic image quality and confuse clinical diagnosis? Insights Imaging. 2020;11:16.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Yuan G-X, Ho C-H, Lin C-J. An improved glmnet for l1-regularized logistic regression. J Machine Learn Res. 2012;13:1999–2030.


    Google Scholar
     

  • Breiman L. Random forests. Mach Learn. 2001;45:5–32.

    Article 

    Google Scholar
     

  • Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Maalouf M. Logistic regression in data analysis: an overview. Int J Data Analysis Techniques Strategies. 2011;3:281–99.

    Article 

    Google Scholar
     

  • Chen C, Liaw A, Breiman L. Using random forest to learn imbalanced data. Berkeley: University of California; 2004;110:24.

  • Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Machine Learn Res. 2011;12:2825–30.


    Google Scholar
     

  • Fluss R, Faraggi D, Reiser B. Estimation of the Youden index and its associated cutoff point. Biometrical J. 2005;47:458–72.

    Article 

    Google Scholar
     

  • Chambers J. Software for data analysis: programming with R: Springer Science & Business Media; 2008.

  • Rudnicki W, Heinze S, Niemiec J, Kojs Z, Sas-Korczynska B, Hendrick E, et al. Correlation between quantitative assessment of contrast enhancement in contrast-enhanced spectral mammography (CESM) and histopathology-preliminary results. Eur Radiol. 2019;29:6220–6.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Deng CY, Juan YH, Cheung YC, Lin YC, Lo YF, Lin G, et al. Quantitative analysis of enhanced malignant and benign lesions on contrast-enhanced spectral mammography. Br J Radiol. 2018;91:20170605.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Lv Y, Chi X, Sun B, Lin S, Xing D. Diagnostic value of quantitative gray-scale analysis of contrast-enhanced spectral mammography for benign and malignant breast lesions. J Comput Assist Tomogr. 2020;44:405–12.

    PubMed 
    Article 

    Google Scholar
     

  • Dromain C, Vietti-Violi N, Meuwly JY. Angiomammography: a review of current evidences. Diagn. Interv. Imaging. 2019;100:593–605.

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Luczynska E, Niemiec J, Heinze S, Adamczyk A, Ambicka A, Marcyniuk P, et al. Intensity and pattern of enhancement on CESM: prognostic significance and its relation to expression of Podoplanin in tumor Stroma – a preliminary report. Anticancer Res. 2018;38:1085–95.

    CAS 
    PubMed 

    Google Scholar
     

  • Yagil Y, Shalmon A, Rundstein A, Servadio Y, Halshtok O, Gotlieb M, et al. Challenges in contrast-enhanced spectral mammography interpretation: artefacts lexicon. Clin Radiol. 2016;71:450–7.

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Lancaster RB, Gulla S, De Los SJ, Umphrey HR. Contrast-enhanced spectral mammography in breast imaging. Semin Roentgenol. 2018;53:294–300.

    PubMed 
    Article 

    Google Scholar
     

  • James JJ, Tennant SL. Contrast-enhanced spectral mammography (CESM). Clin Radiol. 2018;73:715–23.

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Savaridas SL, Taylor DB, Gunawardana D, Phillips M. Could parenchymal enhancement on contrast-enhanced spectral mammography (CESM) represent a new breast cancer risk factor? Correlation with known radiology risk factors. Clin Radiol. 2017;72:1085.e1081–1085.e1089.

    Article 

    Google Scholar
     

  • Sogani J, Morris EA, Kaplan JB, D’Alessio D, Goldman D, Moskowitz CS, et al. Comparison of background parenchymal enhancement at contrast-enhanced spectral mammography and breast MR imaging. Radiology. 2016;282:63–73.

    PubMed 
    Article 

    Google Scholar
     

  • Zamora K, Allen E, Hermecz B. Contrast mammography in clinical practice: current uses and potential diagnostic dilemmas. Clin Imaging. 2021;71:126–35.

    PubMed 
    Article 

    Google Scholar
     

  • Gluskin J, Click M, Fleischman R, Dromain C, Morris EA, Jochelson MS. Contamination artifact that mimics in-situ carcinoma on contrast-enhanced digital mammography. Eur J Radiol. 2017;95:147–54.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Neppalli S, Kessell MA, Madeley CR, Hill ML, Vlaskovsky PS, Taylor DB. Artifacts in contrast-enhanced mammography: are there differences between vendors? Clin Imaging. 2021;80:123–30.

    PubMed 
    Article 

    Google Scholar
     

  • Knogler T, Homolka P, Hörnig M, Leithner R, Langs G, Waitzbauer M, et al. Contrast-enhanced dual energy mammography with a novel anode/filter combination and artifact reduction: a feasibility study. Eur Radiol. 2016;26:1575–81.

    PubMed 
    Article 

    Google Scholar
     

  • Sistermanns M, Kowall B, Hörnig M, Beiderwellen K, Uhlenbrock D. Motion artifact reduction in contrast-enhanced dual-energy mammography – a multireader study about the effect of nonrigid registration as motion correction on image quality. Rofo. 2021;193:1183–8.

    PubMed 
    Article 

    Google Scholar
     

  • Lu Y, Peng B, Lau BA, Hu Y-H, Scaduto DA, Zhao W, et al. A scatter correction method for contrast-enhanced dual-energy digital breast tomosynthesis. Phys Med Biol. 2015;60:6323–54.

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Sensakovic WF, Carnahan MB, Czaplicki CD, Fahrenholtz S, Panda A, Zhou Y, et al. Contrast-enhanced mammography: how does it work? Radiographics. 2021;41:829–39.

    PubMed 
    Article 

    Google Scholar
     

  • Wang S, Sun Y, Li R, Mao N, Li Q, Jiang T, et al. Diagnostic performance of perilesional radiomics analysis of contrast-enhanced mammography for the differentiation of benign and malignant breast lesions. Eur Radiol. 2022;32:639–49.

    PubMed 
    Article 

    Google Scholar
     

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