Review ArticleReview Article
Open Access

The utilization of artificial intelligence applications to improve breast cancer detection and prognosis

Walaa M. Alsharif
Saudi Medical Journal February 2023, 44 (2) 119-127; DOI: https://doi.org/10.15537/smj.2023.44.2.20220611

References

  1. 1.↵
    1. Bray F,
    2. Ferlay J,
    3. Soerjomataram I,
    4. Siegel RL,
    5. Torre LA,
    6. 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.
    OpenUrlCrossRefPubMed
  2. 2.
    1. Lehman CD,
    2. Arao RF,
    3. Sprague BL,
    4. Lee JM,
    5. Buist DS,
    6. Kerlikowske K, et al.
    National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology 2017; 283: 49.
    OpenUrlCrossRefPubMed
  3. 3.↵
    1. Black E,
    2. Richmond R.
    Improving early detection of breast cancer in sub-Saharan Africa: why mammography may not be the way forward. Global Health 2019; 15: 1–11.
    OpenUrlPubMed
  4. 4.↵
    1. Lee CH,
    2. Dershaw DD,
    3. Kopans D,
    4. Evans P,
    5. Monsees B,
    6. Monticciolo D, et al.
    Breast cancer screening with imaging: recommendations from the Society of Breast Imaging and the ACR on the use of mammography, breast MRI, breast ultrasound, and other technologies for the detection of clinically occult breast cancer. J Am Coll Radiol 2010; 7: 18–27.
    OpenUrlCrossRefPubMed
  5. 5.
    1. Oeffinger KC,
    2. Fontham ET,
    3. Etzioni R,
    4. Herzig A,
    5. Michaelson JS,
    6. Shih Y-CT, et al.
    Breast cancer screening for women at average risk: 2015 guideline update from the American Cancer Society. Jama 2015; 314: 1599–1614.
    OpenUrlCrossRefPubMed
  6. 6.↵
    1. Siu AL,
    2. Force UPST.
    Screening for breast cancer: US Preventive Services Task Force recommendation statement. Ann Intern Med 2016; 164: 279–296.
    OpenUrlCrossRefPubMed
  7. 7.↵
    1. Houssami N,
    2. Hunter K.
    The epidemiology, radiology and biological characteristics of interval breast cancers in population mammography screening. NPJ Breast Cancer 2017; 3: 1–13.
    OpenUrl
  8. 8.↵
    1. Vourtsis A,
    2. Berg WA.
    Breast density implications and supplemental screening. Eur Radiol 2019; 29: 1762–1777.
    OpenUrlCrossRef
  9. 9.↵
    1. Nelson HD,
    2. O’meara ES,
    3. Kerlikowske K,
    4. Balch S,
    5. Miglioretti D.
    Factors associated with rates of false-positive and false-negative results from digital mammography screening: an analysis of registry data. Ann Intern Med 2016; 164: 226–235.
    OpenUrlCrossRefPubMed
  10. 10.↵
    1. Tosteson AN,
    2. Fryback DG,
    3. Hammond CS,
    4. Hanna LG,
    5. Grove MR,
    6. Brown M, et al.
    Consequences of false-positive screening mammograms. JAMA internal medicine 2014; 174: 954–961.
    OpenUrl
  11. 11.↵
    1. Alcusky M,
    2. Philpotts L,
    3. Bonafede M,
    4. Clarke J,
    5. Skoufalos A.
    The patient burden of screening mammography recall. J Womens Health (Larchmt) 2014; 23: S11–S19.
    OpenUrl
  12. 12.↵
    1. Jalalian A,
    2. Mashohor S,
    3. Mahmud R,
    4. Karasfi B,
    5. Saripan MIB,
    6. Ramli ARB.
    Foundation and methodologies in computer-aided diagnosis systems for breast cancer detection. EXCLI J 2017; 16: 113.
    OpenUrl
  13. 13.↵
    1. Bargalló X,
    2. Santamaría G,
    3. Del Amo M,
    4. Arguis P,
    5. Ríos J,
    6. Grau J, et al.
    Single reading with computer-aided detection performed by selected radiologists in a breast cancer screening program. Eur J Radiol 2014; 83: 2019–2023.
    OpenUrl
  14. 14.↵
    1. Henriksen EL,
    2. Carlsen JF,
    3. Vejborg IM,
    4. Nielsen MB,
    5. Lauridsen CA.
    The efficacy of using computer-aided detection (CAD) for detection of breast cancer in mammography screening: a systematic review. Acta Radiologica 2019; 60: 13–18.
    OpenUrl
  15. 15.↵
    1. Masud R,
    2. Al-Rei M,
    3. Lokker C.
    Computer-aided detection for breast cancer screening in clinical settings: scoping review. JMIR Med Inform 2019; 7: e12660.
    OpenUrl
  16. 16.↵
    1. Katzen J,
    2. Dodelzon K.
    A review of computer aided detection in mammography. Clinical imaging 2018; 52: 305–309.
    OpenUrlPubMed
  17. 17.↵
    1. Lehman CD,
    2. Wellman RD,
    3. Buist DS,
    4. Kerlikowske K,
    5. Tosteson AN,
    6. Miglioretti DL, et al.
    Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 2015; 175: 1828–1837.
    OpenUrl
  18. 18.↵
    1. Le E,
    2. Wang Y,
    3. Huang Y,
    4. Hickman S,
    5. Gilbert F.
    Artificial intelligence in breast imaging. Clin Radiol 2019; 74: 357–366.
    OpenUrlPubMed
  19. 19.↵
    1. Liu H,
    2. Lang B.
    Machine learning and deep learning methods for intrusion detection systems: A survey. Appl Sci 2019; 9: 4396.
    OpenUrl
  20. 20.↵
    1. Choi J-H,
    2. Kang BJ,
    3. Baek JE,
    4. Lee HS,
    5. Kim SH.
    Application of computer-aided diagnosis in breast ultrasound interpretation: improvements in diagnostic performance according to reader experience. Ultrasonography 2018; 37: 217–225.
    OpenUrlPubMed
  21. 21.
    1. Lee J,
    2. Kim S,
    3. Kang BJ,
    4. Kim SH,
    5. Park GE.
    Evaluation of the effect of computer aided diagnosis system on breast ultrasound for inexperienced radiologists in describing and determining breast lesions. Med Ultrason 2019; 21: 239–245.
    OpenUrlCrossRef
  22. 22.
    1. Di Segni M,
    2. de Soccio V,
    3. Cantisani V,
    4. Bonito G,
    5. Rubini A,
    6. Di Segni G, et al.
    Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound 2018; 21: 105–118.
    OpenUrl
  23. 23.
    1. Bartolotta TV,
    2. Orlando A,
    3. Cantisani V,
    4. Matranga D,
    5. Ienzi R,
    6. Cirino A, et al.
    Focal breast lesion characterization according to the BI-RADS US lexicon: role of a computer-aided decision-making support. Radiol Med 2018; 123: 498–506.
    OpenUrlPubMed
  24. 24.↵
    1. Kim K,
    2. Song MK,
    3. Kim E-K,
    4. Yoon JH.
    Clinical application of S-Detect to breast masses on ultrasonography: a study evaluating the diagnostic performance and agreement with a dedicated breast radiologist. Ultrasonography 2017; 36: 3.
    OpenUrlPubMed
  25. 25.↵
    1. Choi JS,
    2. Han B-K,
    3. Ko ES,
    4. Bae JM,
    5. Ko EY,
    6. Song SH, et al.
    Effect of a deep learning framework-based computer-aided diagnosis system on the diagnostic performance of radiologists in differentiating between malignant and benign masses on breast ultrasonography. Korean J Radiol 2019; 20: 749–758.
    OpenUrl
  26. 26.↵
    1. Jiang L,
    2. Wu Z,
    3. Xu X,
    4. Zhan Y,
    5. Jin X,
    6. Wang L, et al.
    Opportunities and challenges of artificial intelligence in the medical field: Current application, emerging problems, and problem-solving strategies. J Int Med Res 2021; 49: 03000605211000157.
    OpenUrl
  27. 27.↵
    1. Ou WC,
    2. Polat D,
    3. Dogan BE.
    Deep learning in breast radiology: current progress and future directions. Eur Radiol 2021; 31: 4872–4885.
    OpenUrl
  28. 28.↵
    1. Chan HP,
    2. Hadjiiski LM,
    3. Samala RK.
    Computer‐aided diagnosis in the era of deep learning. Med Phys 2020; 47: e218–e27.
    OpenUrl
  29. 29.↵
    1. Kohli A,
    2. Jha S.
    Why CAD failed in mammography. J Am Coll Radiol 2018; 15: 535–537.
    OpenUrl
  30. 30.↵
    1. Le MT,
    2. Mothersill CE,
    3. Seymour CB,
    4. McNeill FE.
    Is the false-positive rate in mammography in North America too high? Br J Radiol 2016; 89: 20160045.
    OpenUrlCrossRefPubMed
  31. 31.↵
    1. Chen X-W,
    2. Lin X.
    Big data deep learning: challenges and perspectives. IEEE Access 2014; 2: 514–525.
    OpenUrl
  32. 32.↵
    1. Agnes SA,
    2. Anitha J,
    3. Pandian S,
    4. Peter JD.
    Classification of mammogram images using multiscale all convolutional neural network (MA-CNN). J Med Syst 2020; 44: 1–9.
    OpenUrlCrossRefPubMed
  33. 33.↵
    1. LeCun Y,
    2. Bengio Y,
    3. Hinton G.
    Deep learning. Nature 2015; 521: 436–444.
    OpenUrlCrossRefPubMed
  34. 34.↵
    1. Rawat W,
    2. Wang Z.
    Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput 2017; 29: 2352–449.
    OpenUrlCrossRefPubMed
  35. 35.↵
    1. Balkenende L,
    2. Teuwen J,
    3. Mann RM
    , editors. Application of deep learning in breast cancer imaging. Semin Nucl Med 2022; 52: 584–596.
    OpenUrl
  36. 36.↵
    1. Welch HG,
    2. Prorok PC,
    3. O’Malley AJ,
    4. Kramer BS.
    Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness. New N Engl J Med 2016; 375: 1438–1447.
    OpenUrl
  37. 37.↵
    1. Alsheh Ali M,
    2. Eriksson M,
    3. Czene K,
    4. Hall P,
    5. Humphreys K.
    Detection of potential microcalcification clusters using multivendor for‐presentation digital mammograms for short‐term breast cancer risk estimation. Med Phys 2019; 46: 1938–1946.
    OpenUrl
  38. 38.↵
    1. Burt JR,
    2. Torosdagli N,
    3. Khosravan N,
    4. RaviPrakash H,
    5. Mortazi A,
    6. Tissavirasingham F, et al.
    Deep learning beyond cats and dogs: recent advances in diagnosing breast cancer with deep neural networks. Br J Radiol 2018; 91: 20170545.
    OpenUrlPubMed
  39. 39.↵
    1. Rodriguez Ruiz A,
    2. Krupinski E,
    3. Mordang J-J,
    4. Schilling K,
    5. Heywang-Kobrunner S,
    6. Sechopoulos I, et al.
    Detection of breast cancer with mammography: effect of an artificial intelligence support system 2019; 290(2):305–14
  40. 40.↵
    1. Pacilè S,
    2. Lopez J,
    3. Chone P,
    4. Bertinotti T,
    5. Grouin JM,
    6. Fillard P.
    Improving breast cancer detection accuracy of mammography with the concurrent use of an artificial intelligence tool. Radiol Artif Intell 2020; 2: e190208.
    OpenUrl
  41. 41.↵
    1. Watanabe AT,
    2. Lim V,
    3. Vu HX,
    4. Chim R,
    5. Weise E,
    6. Liu J, et al.
    Improved cancer detection using artificial intelligence: a retrospective evaluation of missed cancers on mammography. J Digit Imaging 2019; 32: 625–637.
    OpenUrlCrossRefPubMed
  42. 42.↵
    1. Dembrower K,
    2. Wåhlin E,
    3. Liu Y,
    4. Salim M,
    5. Smith K,
    6. Lindholm P, et al.
    Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study. Lancet Digital Health 2020; 2: e468–e474.
    OpenUrl
  43. 43.↵
    1. Kim H-E,
    2. Kim HH,
    3. Han B-K,
    4. Kim KH,
    5. Han K,
    6. Nam H, et al.
    Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digital Health 2020; 2: e138–e148.
    OpenUrl
  44. 44.↵
    1. Dahlblom V,
    2. Andersson I,
    3. Lång K,
    4. Tingberg A,
    5. Zackrisson S,
    6. Dustler M.
    Artificial intelligence detection of missed cancers at digital mammography that were detected at digital breast tomosynthesis. Radiol Artif Intell 2021; 3: e200299.
    OpenUrl
  45. 45.↵
    1. Lång K,
    2. Hofvind S,
    3. Rodríguez-Ruiz A,
    4. Andersson I.
    Can artificial intelligence reduce the interval cancer rate in mammography screening? Eur Radiol 2021; 31: 5940–5947.
    OpenUrl
  46. 46.↵
    1. McKinney SM,
    2. Sieniek M,
    3. Godbole V,
    4. Godwin J,
    5. Antropova N,
    6. Ashrafian H, et al.
    International evaluation of an AI system for breast cancer screening. Nature 2020; 577: 89–94.
    OpenUrlCrossRefPubMed
  47. 47.↵
    1. Mayo RC,
    2. Kent D,
    3. Sen LC,
    4. Kapoor M,
    5. Leung JW,
    6. Watanabe AT.
    Reduction of false-positive markings on mammograms: a retrospective comparison study using an artificial intelligence-based CAD. Journal of digital imaging 2019; 32: 618–624.
    OpenUrl
  48. 48.↵
    1. Schaffter T,
    2. Buist DS,
    3. Lee CI,
    4. Nikulin Y,
    5. Ribli D,
    6. Guan Y, et al.
    Evaluation combiend artificial intelligence and radiologist assessment to interpret screening mammograms. JAMA Netw Open 2020; 3: e20065-e.
    OpenUrl
  49. 49.↵
    1. Salim M,
    2. Wåhlin E,
    3. Dembrower K,
    4. Azavedo E,
    5. Foukakis T,
    6. Liu Y, et al.
    External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA oncology 2020; 6: 1581–1588.
    OpenUrl
  50. 50.↵
    1. Hmida M,
    2. Hamrouni K,
    3. Solaiman B,
    4. Boussetta S.
    Mammographic mass segmentation using fuzzy contours. Comput Methods Programs Biomed 2018; 164: 131–142.
    OpenUrl
  51. 51.↵
    1. Sapate SG,
    2. Mahajan A,
    3. Talbar SN,
    4. Sable N,
    5. Desai S,
    6. Thakur M.
    Radiomics based detection and characterization of suspicious lesions on full field digital mammograms. Comput Methods Programs Biomed 2018; 163: 1–20.
    OpenUrl
  52. 52.↵
    1. Akkus Z,
    2. Cai J,
    3. Boonrod A,
    4. Zeinoddini A,
    5. Weston AD,
    6. Philbrick KA, et al.
    A survey of deep-learning applications in ultrasound: Artificial intelligence–powered ultrasound for improving clinical workflow. J Am Coll Radiol 2019; 16: 1318–1328.
    OpenUrlCrossRefPubMed
  53. 53.↵
    1. Feig S.
    Cost-effectiveness of mammography, MRI, and ultrasonography for breast cancer screening. Radiol Clin North Am 2010; 48: 879–891.
    OpenUrlCrossRefPubMed
  54. 54.↵
    1. Huang Q,
    2. Luo Y,
    3. Zhang Q.
    Breast ultrasound image segmentation: a survey. Int J Comput Assist Radiol Surg 2017; 12: 493–507.
    OpenUrl
  55. 55.↵
    1. Huang Q-H,
    2. Lee S-Y,
    3. Liu L-Z,
    4. Lu M-H,
    5. Jin L-W,
    6. Li A-H.
    A robust graph-based segmentation method for breast tumors in ultrasound images. Ultrasonics 2012; 52: 266–275.
    OpenUrlCrossRefPubMed
  56. 56.↵
    1. Crystal P,
    2. Strano SD,
    3. Shcharynski S,
    4. Koretz MJ.
    Using sonography to screen women with mammographically dense breasts. AJR Am J Roentgenol 2003; 181: 177–182.
    OpenUrlCrossRefPubMedWeb of Science
  57. 57.↵
    1. Lazarus E,
    2. Mainiero MB,
    3. Schepps B,
    4. Koelliker SL,
    5. Livingston LS.
    BI-RADS lexicon for US and mammography: interobserver variability and positive predictive value. Radiology 2006; 239: 385–391.
    OpenUrlCrossRefPubMedWeb of Science
  58. 58.↵
    1. Gao Y,
    2. Geras KJ,
    3. Lewin AA,
    4. Moy L.
    New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence. AJR Am J Roentgenol 2019; 212: 300.
    OpenUrl
  59. 59.↵
    1. Fujioka T,
    2. Mori M,
    3. Kubota K,
    4. Oyama J,
    5. Yamaga E,
    6. Yashima Y, et al.
    The utility of deep learning in breast ultrasonic imaging: a review. Diagnostics (Basel) 2020; 10: 1055.
    OpenUrl
  60. 60.↵
    1. Al-Dhabyani W,
    2. Gomaa M,
    3. Khaled H,
    4. Aly F.
    Deep learning approaches for data augmentation and classification of breast masses using ultrasound images. Int J Adv Comput Sci Appl 2019; 10: 1–11.
    OpenUrl
  61. 61.↵
    1. Shen Y,
    2. Shamout FE,
    3. Oliver JR,
    4. Witowski J,
    5. Kannan K,
    6. Park J, et al.
    Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nature communications 2021; 12: 1–13.
    OpenUrlCrossRef
  62. 62.↵
    1. Becker AS,
    2. Mueller M,
    3. Stoffel E,
    4. Marcon M,
    5. Ghafoor S,
    6. Boss A.
    Classification of breast cancer in ultrasound imaging using a generic deep learning analysis software: a pilot study. Br J Radiol 2018; 91: 20170576.
    OpenUrlPubMed
  63. 63.
    1. Zhang Q,
    2. Xiao Y,
    3. Dai W,
    4. Suo J,
    5. Wang C,
    6. Shi J, et al.
    Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 2016; 72: 150–157.
    OpenUrl
  64. 64.↵
    1. Han S,
    2. Kang H-K,
    3. Jeong J-Y,
    4. Park M-H,
    5. Kim W,
    6. Bang W-C, et al.
    A deep learning framework for supporting the classification of breast lesions in ultrasound images. Phys Med Biol 2017; 62: 7714.
    OpenUrl
  65. 65.↵
    1. Fujioka T,
    2. Katsuta L,
    3. Kubota K,
    4. Mori M,
    5. Kikuchi Y,
    6. Kato A, et al.
    Classification of breast masses on ultrasound shear wave elastography using convolutional neural networks. Ultrason Imaging 2020; 42: 213–220.
    OpenUrl
  66. 66.↵
    1. Mango VL,
    2. Sun M,
    3. Wynn RT,
    4. Ha R.
    Should we ignore, follow, or biopsy? Impact of artificial intelligence decision support on breast ultrasound lesion assessment. AJR Am J Roentgenol 2020; 214: 1445.
    OpenUrlCrossRef
  67. 67.↵
    1. Di Segni M,
    2. de Soccio V,
    3. Cantisani V,
    4. Bonito G,
    5. Rubini A,
    6. Si Segni G, et al.
    Automated classification of focal breast lesions according to S-detect: validation and role as a clinical and teaching tool. J Ultrasound 2018; 21: 105–108.
    OpenUrl
  68. 68.↵
    1. Bitencourt A,
    2. Naranjo ID,
    3. Gullo RL,
    4. Saccarelli CR,
    5. Pinker K.
    AI-enhanced breast imaging: Where are we and where are we heading? Eur J Radiol 2021; 142: 109882.
    OpenUrlCrossRef
  69. 69.↵
    1. Jiang Y,
    2. Edwards AV,
    3. Newstead GM.
    Artificial intelligence applied to breast MRI for improved diagnosis. Radiology 2021; 298: 38–46.
    OpenUrl
  70. 70.↵
    1. Bluemke DA,
    2. Gatsonis CA,
    3. Chen MH,
    4. DeAngelis GA,
    5. DeBruhl N,
    6. Harms S, et al.
    Magnetic resonance imaging of the breast prior to biopsy. JAMA 2004; 292: 2735–2742.
    OpenUrlCrossRefPubMedWeb of Science
  71. 71.↵
    1. Vreemann S,
    2. Gubern-Merida A,
    3. Lardenoije S,
    4. Bult P,
    5. Karssemeijer N,
    6. Pinker K, et al.
    The frequency of missed breast cancers in women participating in a high-risk MRI screening program. Breast Cancer Res Treat 2018; 169: 323–331.
    OpenUrlCrossRef
  72. 72.↵
    1. Yamaguchi K,
    2. Schacht D,
    3. Newstead GM,
    4. Bradbury AR,
    5. Verp MS,
    6. Olopade OI, et al.
    Breast cancer detected on an incident (second or subsequent) round of screening MRI: MRI features of false-negative cases. AJR Am J Roentgenol 2013; 201: 1155–1163.
    OpenUrl
  73. 73.↵
    1. Kuhl CK,
    2. Schrading S,
    3. Bieling HB,
    4. Wardelmann E,
    5. Leutner CC,
    6. Koenig R, et al.
    MRI for diagnosis of pure ductal carcinoma in situ: a prospective observational study. The Lancet 2007;370(9586):485–92.
    OpenUrl
  74. 74.
    1. Schnall MD,
    2. Blume J,
    3. Bluemke DA,
    4. DeAngelis GA,
    5. DeBruhl N,
    6. Harms S, et al.
    Diagnostic architectural and dynamic features at breast MR imaging: multicenter study. Radiology 2006; 238: 42–53.
    OpenUrlCrossRefPubMedWeb of Science
  75. 75.↵
    1. Sheth D,
    2. Giger ML.
    Artificial intelligence in the interpretation of breast cancer on MRI. J Magn Reson Imaging 2020; 51: 1310–1324.
    OpenUrl
  76. 76.↵
    1. Meyer-Bäse A,
    2. Morra L,
    3. Meyer-Bäse U,
    4. Pinker K.
    Current status and future perspectives of artificial intelligence in magnetic resonance breast imaging. Contrast Media Mol Imaging 2020; 2020.
  77. 77.↵
    1. Dalmış MU,
    2. Vreemann S,
    3. Kooi T,
    4. Mann RM,
    5. Karssemeijer N,
    6. Gubern-Mérida A.
    Fully automated detection of breast cancer in screening MRI using convolutional neural networks. J Med Imaging (Bellingham) 2018; 5: 014502.
    OpenUrl
  78. 78.↵
    1. Antropova NO,
    2. Abe H,
    3. Giger ML.
    Use of clinical MRI maximum intensity projections for improved breast lesion classification with deep convolutional neural networks. Journal of Medical Imaging 2018; 5: 014503.
    OpenUrl
  79. 79.
    1. Reig B,
    2. Heacock L,
    3. Geras KJ,
    4. Moy L.
    Machine learning in breast MRI. J Magn Reson Imaging 2020; 52: 998–1018.
    OpenUrl
  80. 80.↵
    1. Herent P,
    2. Schmauch B,
    3. Jehanno P,
    4. Dehaene O,
    5. Saillard C,
    6. Balleyguier C, et al.
    Detection and characterization of MRI breast lesions using deep learning. Diagn Interv Imaging 2019; 100: 219–225.
    OpenUrl
  81. 81.↵
    1. Adachi M,
    2. Fujioka T,
    3. Mori M,
    4. Kubota K,
    5. Kikuchi Y,
    6. Xiaotong W, et al.
    Detection and diagnosis of breast cancer using artificial intelligence based assessment of maximum intensity projection dynamic contrast-enhanced magnetic resonance images. Diagnostics (Basel) 2020; 10: 330.
    OpenUrl
  82. 82.↵
    1. Zhang Y,
    2. Chen J-H,
    3. Chang K-T,
    4. Park VY,
    5. Kim MJ,
    6. Chan S, et al.
    Automatic breast and fibroglandular tissue segmentation in breast MRI using deep learning by a fully-convolutional residual neural network U-net. Acad Radiol 2019; 26: 1526–1535.
    OpenUrl
  83. 83.
    1. Zhou J,
    2. Luo LY,
    3. Dou Q,
    4. Chen H,
    5. Chen C,
    6. Li GJ, et al.
    Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. J Magn Reson Imaging 2019; 50: 1144–1151.
    OpenUrl
  84. 84.↵
    1. Zhou J,
    2. Zhang Y,
    3. Chang KT,
    4. Lee KE,
    5. Wang O,
    6. Li J, et al.
    Diagnosis of benign and malignant breast lesions on DCE‐MRI by using radiomics and deep learning with consideration of peritumor tissue. J Magn Reson Imaging 2020; 51: 798–809.
    OpenUrl
  85. 85.↵
    1. Truhn D,
    2. Schrading S,
    3. Haarburger C,
    4. Schneider H,
    5. Merhof D,
    6. Kuhl C.
    Radiomic versus convolutional neural networks analysis for classification of contrast-enhancing lesions at multiparametric breast MRI. Radiology 2019; 290: 290–297.
    OpenUrl
  86. 86.↵
    1. Ayatollahi F,
    2. Shokouhi SB,
    3. Mann RM,
    4. Teuwen J.
    Automatic breast lesion detection in ultrafast DCE‐MRI using deep learning. Med Phys 2021; 48: 5897–5907.
    OpenUrl
  87. 87.↵
    1. Eskreis-Winkler S,
    2. Onishi N,
    3. Pinker K,
    4. Reiner JS,
    5. Kaplan J,
    6. Morris EA, et al.
    Using deep learning to improve nonsystematic viewing of breast cancer on MRI. J Breast Imaging 2021; 3: 201–207.
    OpenUrl
  88. 88.↵
    1. Jing X,
    2. Wielema M,
    3. Cornelissen LJ,
    4. van Gent M,
    5. Iwema WM,
    6. Zheng S, et al.
    Using deep learning to safely exclude lesions with only ultrafast breast MRI to shorten acquisition and reading time. Eur Radiol 2022: 1–10.
  89. 89.↵
    1. Wu Y,
    2. Wu J,
    3. Dou Y,
    4. Rubert N,
    5. Wang Y,
    6. Deng J.
    A deep learning fusion model with evidence-based confidence level analysis for differentiation of malignant and benign breast tumors using dynamic contrast enhanced MRI. Biomed Signal Process Control 2022; 72: 103319.
    OpenUrl
  90. 90.↵
    1. Vicini S,
    2. Bortolotto C,
    3. Rengo M,
    4. Ballerini D,
    5. Bellini D,
    6. Carbone I, et al.
    A narrative review on current imaging applications of artificial intelligence and radiomics in oncology: focus on the three most common cancers. Radiol Med 2022: 1-18: 819–836.
    OpenUrl
  91. 91.↵
    1. Parekh VS,
    2. Jacobs MA.
    Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI. NPJ Breast Cancer 2017; 3: 1–9.
    OpenUrl
  92. 92.↵
    1. Fusco R,
    2. Piccirillo A,
    3. Sansone M,
    4. Granata V,
    5. Runulotta MR,
    6. Patrosino T, et al.
    Radiomics and artificial intellidence analysis with textural metrics extracted by contrast-enhanced mammography in the breast lesions classification. Diagnostics (Basel) 2021; 11: 815.
    OpenUrl
  93. 93.↵
    1. Crivelli P,
    2. Ledda RE,
    3. Parascandolo N,
    4. Fara A,
    5. Soro D,
    6. Conti M.
    A new challenge for radiologists: radiomics in breast cancer. Biomed Res Int 2018; 2018.
  94. 94.↵
    1. Bi WL,
    2. Hosny A,
    3. Schabath MB,
    4. Giger ML,
    5. Birkbak NJ,
    6. Mehrtash A, et al.
    Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 2019; 69: 127–57.
    OpenUrlPubMed
  95. 95.
    1. Gilbert F,
    2. Smye S,
    3. Schönlieb C-B.
    Artificial intelligence in clinical imaging: a health system approach. Clin Radiol 2020; 75: 3–6.
    OpenUrl
  96. 96.↵
    1. Tang A,
    2. Tam R,
    3. Cadrin-Chênevert A,
    4. Guest W,
    5. Chong J,
    6. Barfett J, et al.
    Canadian Association of Radiologists white paper on artificial intelligence in radiology. Can Assoc Radiol J 2018; 69: 120–135.
    OpenUrlCrossRefPubMed
  97. 97.↵
    1. Topol EJ.
    High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine 2019; 25: 44–56.
    OpenUrlCrossRefPubMed
  98. 98.↵
    1. Mendelson EB.
    Artificial intelligence in breast imaging: potentials and limitations. AJR Am J Roentgenol 2019; 212: 293–299.
    OpenUrl
  99. 99.↵
    1. Reiner B.
    Contextualizing causation of uncertainty in medical reporting. J Am Coll Radiol 2018; 15: 325–327.
    OpenUrl
  100. 100.↵
    1. Sabottke CF,
    2. Spieler BM.
    The effect of image resolution on deep learning in radiography. Radiol Artif Intell 2020; 2: e190015.
    OpenUrlPubMed
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