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The English in this document has been checked by at least two professional editors, both native speakers of English. For a certificate, please see: http://www.textcheck.com/certificate/ILXoRO. The scientific guarantor of this publication is the corresponding author, Eun Hye Lee.
참고문헌
- DeSantis CE, Ma J, Gaudet MM, Newman LA, Miller KD, Goding Sauer A, et al. Breast cancer statistics, 2019. CA Cancer J Clin 2019;69:438-451 https://doi.org/10.3322/caac.21583
- Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018;68:394-424 https://doi.org/10.3322/caac.21492
- Oeffinger KC, Fontham ET, Etzioni R, Herzig A, Michaelson JS, Shih YC, et al. Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. JAMA 2015;314:1599-1614 https://doi.org/10.1001/jama.2015.12783
- Myers ER, Moorman P, Gierisch JM, Havrilesky LJ, Grimm LJ, Ghate S, et al. Benefits and harms of breast cancer screening: a systematic review. JAMA 2015;314:1615-1634 https://doi.org/10.1001/jama.2015.13183
- Marmot MG, Altman DG, Cameron DA, Dewar JA, Thompson SG, Wilcox M. The benefits and harms of breast cancer screening: an independent review. Br J Cancer 2013;108:2205-2240 https://doi.org/10.1038/bjc.2013.177
- Baker JA, Rosen EL, Lo JY, Gimenez EI, Walsh R, Soo MS. Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. AJR Am J Roentgenol 2003;181:1083-1088 https://doi.org/10.2214/ajr.181.4.1811083
- Eberhard JW, Alyassin AM, Kapur A. Computer aided detection (CAD) for 3D digital mammography. Web site. https://patents.google.com/patent/US7218766B2/en. Accessed January 11, 2021
- Warren Burhenne LJ, Wood SA, D'Orsi CJ, Feig SA, Kopans DB, O'Shaughnessy KF, et al. Potential contribution of computer-aided detection to the sensitivity of screening mammography. Radiology 2000;215:554-562 https://doi.org/10.1148/radiology.215.2.r00ma15554
- Ribli D, Horvath A, Unger Z, Pollner P, Csabai I. Detecting and classifying lesions in mammograms with deep learning. Sci Rep 2018;8:4165
- Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health 2020;2:e138-e148 https://doi.org/10.1016/S2589-7500(20)30003-0
- Yoon JH, Kim EK. Deep learning-based artificial intelligence for mammography. Korean J Radiol 2021;22:1225-1239 https://doi.org/10.3348/kjr.2020.1210
- Cole EB, Zhang Z, Marques HS, Edward Hendrick R, Yaffe MJ, Pisano ED. Impact of computer-aided detection systems on radiologist accuracy with digital mammography. AJR Am J Roentgenol 2014;203:909-916 https://doi.org/10.2214/AJR.12.10187
- Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL; Breast Cancer Surveillance Consortium. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 2015;175:1828-1837 https://doi.org/10.1001/jamainternmed.2015.5231
- Fenton JJ, Abraham L, Taplin SH, Geller BM, Carney PA, D'Orsi C, et al. Effectiveness of computer-aided detection in community mammography practice. J Natl Cancer Inst 2015;103:1152-1161 https://doi.org/10.1093/jnci/djr206
- Mahoney MC, Meganathan K. False positive marks on unsuspicious screening mammography with computer-aided detection. J Digit Imaging 2015;24:772-777 https://doi.org/10.1007/s10278-011-9389-7
- Rodriguez-Ruiz A, Krupinski E, Mordang JJ, Schilling K, Heywang-Kobrunner SH, Sechopoulos I, et al. Detection of breast cancer with mammography: effect of an artificial intelligence support system. Radiology 2019;290:305-314 https://doi.org/10.1148/radiol.2018181371
- McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature 2020;577:89-94 https://doi.org/10.1038/s41586-019-1799-6
- Rodriguez-Ruiz A, Lang K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, et al. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst 2019;111:916-922 https://doi.org/10.1093/jnci/djy222
- Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, et al. Evaluation of combined artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 2020;3:e200265
- Park SH, Han K. Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 2018;286:800-809 https://doi.org/10.1148/radiol.2017171920
- Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA 2017;318:517-518 https://doi.org/10.1001/jama.2017.7797
- Obuchowski NA. Sample size tables for receiver operating characteristic studies. AJR Am J Roentgenol 2000;175:603-608 https://doi.org/10.2214/ajr.175.3.1750603
- Sung J, Park S, Lee SM, Bae W, Park B, Jung E, et al. Added value of deep learning-based detection system for multiple major findings on chest radiographs: a randomized crossover study. Radiology 2021;299:450-459 https://doi.org/10.1148/radiol.2021202818
- Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 2011;12:77
- Chen W, Samuelson FW. The average receiver operating characteristic curve in multireader multicase imaging studies. Br J Radiol 2014;87:20140016
- Gallas BD, Hillis SL. Generalized Roe and Metz receiver operating characteristic model: analytic link between simulated decision scores and empirical AUC variances and covariances. J Med Imaging (Bellingham) 2014;1:031006
- Swensson RG. Unified measurement of observer performance in detecting and localizing target objects on images. Med Phys 1996;23:1709-1725 https://doi.org/10.1118/1.597758
- He X, Frey E. ROC, LROC, FROC, AFROC: an alphabet soup. J Am Coll Radiol 2009;6:652-655 https://doi.org/10.1016/j.jacr.2009.06.001
- Hillis SL. A comparison of denominator degrees of freedom methods for multiple observer ROC analysis. Stat Med 2007;26:596-619 https://doi.org/10.1002/sim.2532
- Dorfman DD, Berbaum KS, Metz CE. Receiver operating characteristic rating analysis. Generalization to the population of readers and patients with the jackknife method. Invest Radiol 1992;27:723-731 https://doi.org/10.1097/00004424-199209000-00015
- Chakraborty DP. Analysis of location specific observer performance data: validated extensions of the jackknife free-response (JAFROC) method. Acad Radiol 2006;13:1187-1193 https://doi.org/10.1016/j.acra.2006.06.016
- Chakraborty DP, Berbaum KS. Observer studies involving detection and localization: modeling, analysis, and validation. Med Phys 2004;31:2313-2330 https://doi.org/10.1118/1.1769352
- Stock C, Hielscher T. DTComPair: comparison of binary diagnostic tests in a paired study design. R package version 1.0.3. Web site. http://CRAN.R-project.org/package=DTComPair. Published 2014. Accessed July 16, 2020
- Hojsgaard S, Halekoh U, Yan J. The R package geepack for generalized estimating equations. J Stat Softw 2006;15:1-11
- Kooi T, Litjens G, van Ginneken B, Gubern-Merida A, Sanchez CI, Mann R, et al. Large scale deep learning for computer aided detection of mammographic lesions. Med Image Anal 2017;35:303-312 https://doi.org/10.1016/j.media.2016.07.007
- Conant EF, Toledano AY, Periaswamy S, Fotin SV, Go J, Boatsman JE, et al. Improving accuracy and efficiency with concurrent use of artificial intelligence for digital breast tomosynthesis. Radiol Artif Intell 2019;1:e180096