Lawson MB, Herschorn SD, Sprague BL, Buist DS, Lee SJ, Newell MS, Lourenco AP, Lee JM. Imaging surveillance options for individuals with a personal history of breast cancer: AJR expert panel narrative review. AJR Am J Roentgenol 2022;219(6):854-868. https://doi.org/10.2214/AJR.22.27635
Article PubMed PubMed Central Google Scholar
Lee JM, Abraham L, Lam DL, Buist DS, Kerlikowske K, Miglioretti DL, Houssami N, Lehman CD, Henderson LM, Hubbard RA. Cumulative risk distribution for interval invasive second breast cancers after negative surveillance mammography. J Clin Oncol 2018;36(20):2070. https://doi.org/10.1200/JCO.2017.76.8267
Article PubMed PubMed Central Google Scholar
Lee JM, Ichikawa LE, Wernli KJ, Bowles E, Specht JM, Kerlikowske K, Migiloretti DL, Lowry KP, Tosteson ANA. Digital mammography and breast tomosynthesis performance in women with a personal history of breast cancer, 2007-2016. Radiology 2021;300(2):290-300. https://doi.org/10.1148/radiol.2021204581
Ha SM, Lee JM, Kim SO, Moon WK, Chang JM. Semiannual breast US or MRI screening in patients with a personal history of breast cancer. Radiology 2023;307(5):e221660. https://doi.org/10.1148/radiol.221660
Yoon JH, Kim EK. Deep learning-based artificial intelligence for mammography. Korean J Radiol. 2021;22(8):1225-1239. https://doi.org/10.3348/kjr.2020.1210
Article PubMed PubMed Central Google Scholar
Yoon JH, Kim EK, Kim GR, Han K, Moon HJ. Mammographic surveillance after breast-conserving therapy: impact of digital breast tomosynthesis and artificial intelligence–based computer-aided detection. AJR Am J Roentgenol 2022;218(1):42-51. https://doi.org/10.2214/AJR.21.26506
Allen B, Dreyer K, Stibolt Jr R, Agarwal S, Coombs L, Treml C, Elkholy M, Brink L, Wald C. Evaluation and real-world performance monitoring of artificial intelligence models in clinical practice: try it, buy it, check it. J Am Coll Radiol 2021;18(11):1489-1496. https://doi.org/10.1016/j.jacr.2021.08.022
Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, Minihan A, Jemal A, Siegel RL. Breast Cancer Statistics, 2022. CA A Cancer J Clin 2022;72(6):524-541. https://doi.org/10.3322/caac.21754
Amin MB, Edge SB, Greene FL, Compton CC, Gershenwald JE, Brookland RK, Meyer L, Gress DM, Byrd DR, Winchester DP. The eight edition AJCC cancer staging manual: continuing to build a bridge from a population-based to a more “personalized” approach to cancer staging. CA Cancer J Clin 2017;67(2):93–99. https://doi.org/10.3322/caac.21388
Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, Lee EH, Kim EK. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health 2020;2(3):e138-e148. https://doi.org/10.1016/S2589-7500(20)30003-0
Kim EK, Kim HE, Han K, Kang BJ, Sohn YM, Woo OH, Lee CW. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study. Sci Rep. 2018 ;8(1):2762. https://doi.org/10.1038/s41598-018-21215-1
Article CAS PubMed PubMed Central Google Scholar
Lee SE, Hong H, Kim EK. Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography. Korean J Radiol 2024;25(4):343-350. https://doi.org/10.3348/kjr.2023.0907
Article PubMed PubMed Central Google Scholar
Yoon JH, Strand F, Baltzer PAT, Conant EF, Gilbert FJ, Lehman CD, Morris EA, Mullen LA, Nishikawa RM, Sharma N, Vejborg I, Moy L, Mann RM. Standalone AI for Breast Cancer Detection at Screening Digital Mammography and Digital Breast Tomosynthesis: A Systematic Review and Meta-Analysis. Radiology 2023;307(5):e222639. https://doi.org/10.1148/radiol.222639.
Kim D, Hwang JE, Cho Y, Cho HW, Lee W, Lee JH, Oh IY, Baek S, Lee E, Kim J. A Retrospective Clinical Evaluation of an Artificial Intelligence Screening Method for Early Detection of STEMI in the Emergency Department. J Korean Med Sci 2022;37(10):e81. https://doi.org/10.3346/jkms.2022.37.e81
Article PubMed PubMed Central Google Scholar
Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Broeders M, Gennaro G, Clauser P, Helbich TH, Chevalier M, Tan T, Mertelmeier T, Wallis MG, Andersson I, Zackrisson S, Mann RM, Sechopoulos I. Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. J Natl Cancer Inst 2019;111(9):916-922. https://doi.org/10.1093/jnci/djy222
Article PubMed PubMed Central Google Scholar
Lee JH, Kim KH, Lee EH, Ahn JS, Ryu JK, Park YM, Shin GW, Kim YJ, Choi HY. Improving the performance of radiologists using artificial intelligence-based detection support software for mammography: a multi-reader study. Korean J Radiol. 2022;23(5):505-516. https://doi.org/10.3348/kjr.2021.0476
Article PubMed PubMed Central Google Scholar
Hupse R, Karssemeijer N. Use of normal tissue context in computer-aided detection of masses in mammograms. IEEE Trans Med Imaging 2009;28(12):2033-2041. https://doi.org/10.1109/TMI.2009.2028611
Wu N, Huang Z, Shen Y, Park J, Phang J, Makino T, Kim SG, Cho K, Heacock L, Moy L, Geras KJ. Reducing false-positive biopsies using deep neural networks that utilize both local and global image context of screening mammograms. J Digit Imaging 2021;34(6):1414-1423. https://doi.org/10.1007/s10278-021-00530-6
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