Independent Artificial Intelligence in Breast Cancer Detection via Digital Mammography: Where is the Future of Breast Radiology Heading?
Main Article Content
Keywords
Breast Neoplasms, Mammography, Mass Screening, Artificial Intelligence
Abstract
Breast cancer is the most prevalent and deadly malignancy among women worldwide. Screening mammography stands out as the most effective tool for early detection of malignant lesions due to its high sensitivity, cost-effectiveness, and relative accessibility. However, its performance heavily relies on the operator’s expertise, both in image acquisition and interpretation. In resource-limited settings where complementary techniques such as ultrasonography or magnetic resonance imaging are unavailable, mammography may be the only accessible modality. Artificial intelligence (AI) emerges as a transformative technology, capable of identifying patterns from algorithms trained on extensive image datasets, with significant potential to revolutionize breast radiology. Recent studies highlight that AI can optimize radiologists' workloads, enhance diagnostic accuracy, and reduce false negatives in breast cancer detection. This review examines the most up-to-date evidence on the use of independent artificial intelligence in digital mammography screening, emphasizing its clinical implications and its future in diagnostic imaging.
References
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19. Lei YM, Yin M, Yu MH, Yu J, Zeng SE, Lv WZ, et al. Artificial Intelligence in Medical Imaging of the Breast. Front Oncol. 2021;11:600557. doi: 10.3389/fonc.2021.600557.
20. Mahant SS, Varma AR. Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine. Cureus. 2022;14(9):e28945. doi: 10.7759/cureus.28945.
21. Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, Dietrich CF. Artificial intelligence in breast ultrasound. World J Radiol. 2019;11(2):19-26. doi: 10.4329/wjr.v11.i2.19.
22. Fujioka T, Mori M, Kubota K, Oyama J, Yamaga E, Yashima Y, et al. The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review. Diagnostics (Basel). 2020;10(12):1055. doi: 10.3390/diagnostics10121055.
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24. Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun. 2021;12:5645. doi: 10.1038/s41467-021-26023-2.
25. Shoshan Y, Bakalo R, Gilboa-Solomon F, Ratner V, Barkan E, Ozery-Flato M, et al. Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology. 2022;303(1):69-77. doi: 10.1148/radiol.211105.
26. Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res. 2022;24:14. doi: 10.1186/s13058-022-01509-z.
27. Scaranelo AM. Standalone AI in Breast Cancer Screening: Where We Are and What Is to Be Achieved. Radiology. 2023;307(5):e230935. doi: 10.1148/radiol.230935.
28. Romero-Martín S, Elías-Cabot E, Raya-Povedano JL, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M. Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation. Radiology. 2022;302(3):535-42. doi: 10.1148/radiol.211590.
29. Kim H, Choi JS, Kim K, Ko ES, Ko EY, Han BK. Effect of artificial intelligence-based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study. Eur Radiol. 2023; doi: 10.1007/s00330-023-09692-z.
30. 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. doi: 10.2214/AJR.21.26506.
31. van Winkel SL, Rodríguez-Ruiz A, Appelman L, Gubern-Mérida A, Karssemeijer N, Teuwen J, et al. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol. 2021;31(11):8682-91. doi: 10.1007/s00330-021-07992-w.
32. Dang LA, Chazard E, Poncelet E, Serb T, Rusu A, Pauwels X, et al. Impact of artificial intelligence in breast cancer screening with mammography. Breast Cancer. 2022;29(6):967-77. doi: 10.1007/s12282-022-01375-9.
33. Lauritzen AD, Rodríguez-Ruiz A, von Euler-Chelpin MC, Lynge E, Vejborg I, Nielsen M, et al. An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload. Radiology. 2022;304(1):41-9. doi: 10.1148/radiol.210948.
34. Dembrower K, Wåhlin E, Liu Y, Salim M, Smith K, 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 Digit Health. 2020;2(9):e468-e74. doi: 10.1016/S2589-7500(20)30185-0.
35. Reyes-Monasterio A, Lozada-Martinez ID, Cabrera-Vargas LF, Narvaez-Rojas AR. Breast cancer care in Latin America: The ghost burden of a pandemic outbreak. Int J Surg. 2022;104:106784. doi: 10.1016/j.ijsu.2022.106784.
36. Reyes A, Torregrosa L, Lozada-Martinez ID, Cabrera-Vargas LF, Nunez-Ordonez N, Martínez Ibata TF. Breast cancer mortality research in Latin America: A gap needed to be filled. Am J Surg. 2023;225(5):937-8. doi: 10.1016/j.amjsurg.2023.01.010.
37. Reyes Monasterio A, Reyes T, Acevedo Parrales JD, Mantilla-Sylvain F, Cabrera LF, Lozada ID, et al. Social media to overcome technological barriers in surgical training: Four Latin American experiences. Am J Surg. 2023;S0002-9610(23)00206-4. doi: 10.1016/j.amjsurg.2023.05.020.
38. Yeh AC, Li H, Zhu Y, Zhang J, Khramtsova G, Drukker K, et al. Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging. 2019;19(1):48. doi: 10.1186/s40644-019-0233-5.
39. Gallivanone F, Bertoli G, Porro D. Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions. Methods Protoc. 2022;5(5):78. doi: 10.3390/mps5050078.
40. Liu Q, Hu P. Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer. Biomark Res. 2023;11:9. doi: 10.1186/s40364-023-00455-y.
2. Arnold M, Morgan E, Rumgay H, Mafra A, Singh D, Laversanne M, et al. Current and future burden of breast cancer: Global statistics for 2020 and 2040. Breast. 2022;66:15-23. doi: 10.1016/j.breast.2022.08.010.
3. Allahqoli L, Mazidimoradi A, Momenimovahed Z, Rahmani A, Hakimi S, Tiznobaik A, et al. The Global Incidence, Mortality, and Burden of Breast Cancer in 2019: Correlation With Smoking, Drinking, and Drug Use. Front Oncol. 2022;12:921015. doi: 10.3389/fonc.2022.921015.
4. The Cancer Atlas. Breast Cancer [Internet]. [Consultado 11 Jul 2023]. Disponible en: https://canceratlas.cancer.org/the-burden/breast-cancer/
5. Ranganathan K, Singh P, Raghavendran K, Wilkins EG, Hamill JB, Aliu O, et al. The Global Macroeconomic Burden of Breast Cancer: Implications for Oncologic Surgery. Ann Surg. 2021;274(6):1067-72. doi: 10.1097/SLA.0000000000003662.
6. National Cancer Institute. Mammograms [Internet]. [Consultado 11 Jul 2023]. Disponible en: https://www.cancer.gov/types/breast/mammograms-fact-sheet
7. Gøtzsche PC. Mammography screening is harmful and should be abandoned. J R Soc Med. 2015;108(9):341-5. doi: 10.1177/0141076815602452.
8. Budh DP, Sapra A. Breast Cancer Screening. [Updated 2022 Oct 22]. En: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-. Disponible en: https://www.ncbi.nlm.nih.gov/books/NBK556050/
9. Dileep G, Gianchandani Gyani SG. Artificial Intelligence in Breast Cancer Screening and Diagnosis. Cureus. 2022;14(10):e30318. doi: 10.7759/cureus.30318.
10. Marinovich ML, Wylie E, Lotter W, Lund H, Waddell A, Madeley C, et al. Artificial intelligence (AI) for breast cancer screening: BreastScreen population-based cohort study of cancer detection. EBioMedicine. 2023;90:104498. doi: 10.1016/j.ebiom.2023.104498.
11. Yoon JH, Strand F, Baltzer PAT, Conant EF, Gilbert FJ, Lehman CD, et al. 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. doi: 10.1148/radiol.222639.
12. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat Rev Cancer. 2018;18(8):500-10. doi: 10.1038/s41568-018-0016-5.
13. Kelly BS, Judge C, Bollard SM, Clifford SM, Healy GM, Aziz A, et al. Radiology artificial intelligence: a systematic review and evaluation of methods (RAISE). Eur Radiol. 2022;32(11):7998-8007. doi: 10.1007/s00330-022-08784-6.
14. Pérez Del Barrio A, Menéndez Fernández-Miranda P, Sanz Bellón P, Lloret Iglesias L, Rodríguez González D. Artificial Intelligence in Radiology: an introduction to the most important concepts. Radiologia (Engl Ed). 2022;64(3):228-36. doi: 10.1016/j.rxeng.2022.03.005.
15. Moawad AW, Fuentes DT, ElBanan MG, Shalaby AS, Guccione J, Kamel S, et al. Artificial Intelligence in Diagnostic Radiology: Where Do We Stand, Challenges, and Opportunities. J Comput Assist Tomogr. 2022;46(1):78-90. doi: 10.1097/RCT.0000000000001247.
16. van Leeuwen KG, de Rooij M, Schalekamp S, van Ginneken B, Rutten MJCM. How does artificial intelligence in radiology improve efficiency and health outcomes? Pediatr Radiol. 2022;52(11):2087-93. doi: 10.1007/s00247-021-05114-8.
17. Gore JC. Artificial intelligence in medical imaging. Magn Reson Imaging. 2020;68:A1-4. doi: 10.1016/j.mri.2019.12.006.
18. Lobig F, Subramanian D, Blankenburg M, Sharma A, Variyar A, Butler O. To pay or not to pay for artificial intelligence applications in radiology. NPJ Digit Med. 2023;6(1):117. doi: 10.1038/s41746-023-00861-4.
19. Lei YM, Yin M, Yu MH, Yu J, Zeng SE, Lv WZ, et al. Artificial Intelligence in Medical Imaging of the Breast. Front Oncol. 2021;11:600557. doi: 10.3389/fonc.2021.600557.
20. Mahant SS, Varma AR. Artificial Intelligence in Breast Ultrasound: The Emerging Future of Modern Medicine. Cureus. 2022;14(9):e28945. doi: 10.7759/cureus.28945.
21. Wu GG, Zhou LQ, Xu JW, Wang JY, Wei Q, Deng YB, Cui XW, Dietrich CF. Artificial intelligence in breast ultrasound. World J Radiol. 2019;11(2):19-26. doi: 10.4329/wjr.v11.i2.19.
22. Fujioka T, Mori M, Kubota K, Oyama J, Yamaga E, Yashima Y, et al. The Utility of Deep Learning in Breast Ultrasonic Imaging: A Review. Diagnostics (Basel). 2020;10(12):1055. doi: 10.3390/diagnostics10121055.
23. Freeman K, Geppert J, Stinton C, Todkill D, Johnson S, Clarke A, et al. Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy. BMJ. 2021;374:n1872. doi: 10.1136/bmj.n1872.
24. Shen Y, Shamout FE, Oliver JR, Witowski J, Kannan K, Park J, et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun. 2021;12:5645. doi: 10.1038/s41467-021-26023-2.
25. Shoshan Y, Bakalo R, Gilboa-Solomon F, Ratner V, Barkan E, Ozery-Flato M, et al. Artificial Intelligence for Reducing Workload in Breast Cancer Screening with Digital Breast Tomosynthesis. Radiology. 2022;303(1):69-77. doi: 10.1148/radiol.211105.
26. Gastounioti A, Desai S, Ahluwalia VS, Conant EF, Kontos D. Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review. Breast Cancer Res. 2022;24:14. doi: 10.1186/s13058-022-01509-z.
27. Scaranelo AM. Standalone AI in Breast Cancer Screening: Where We Are and What Is to Be Achieved. Radiology. 2023;307(5):e230935. doi: 10.1148/radiol.230935.
28. Romero-Martín S, Elías-Cabot E, Raya-Povedano JL, Gubern-Mérida A, Rodríguez-Ruiz A, Álvarez-Benito M. Stand-Alone Use of Artificial Intelligence for Digital Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation. Radiology. 2022;302(3):535-42. doi: 10.1148/radiol.211590.
29. Kim H, Choi JS, Kim K, Ko ES, Ko EY, Han BK. Effect of artificial intelligence-based computer-aided diagnosis on the screening outcomes of digital mammography: a matched cohort study. Eur Radiol. 2023; doi: 10.1007/s00330-023-09692-z.
30. 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. doi: 10.2214/AJR.21.26506.
31. van Winkel SL, Rodríguez-Ruiz A, Appelman L, Gubern-Mérida A, Karssemeijer N, Teuwen J, et al. Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study. Eur Radiol. 2021;31(11):8682-91. doi: 10.1007/s00330-021-07992-w.
32. Dang LA, Chazard E, Poncelet E, Serb T, Rusu A, Pauwels X, et al. Impact of artificial intelligence in breast cancer screening with mammography. Breast Cancer. 2022;29(6):967-77. doi: 10.1007/s12282-022-01375-9.
33. Lauritzen AD, Rodríguez-Ruiz A, von Euler-Chelpin MC, Lynge E, Vejborg I, Nielsen M, et al. An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload. Radiology. 2022;304(1):41-9. doi: 10.1148/radiol.210948.
34. Dembrower K, Wåhlin E, Liu Y, Salim M, Smith K, 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 Digit Health. 2020;2(9):e468-e74. doi: 10.1016/S2589-7500(20)30185-0.
35. Reyes-Monasterio A, Lozada-Martinez ID, Cabrera-Vargas LF, Narvaez-Rojas AR. Breast cancer care in Latin America: The ghost burden of a pandemic outbreak. Int J Surg. 2022;104:106784. doi: 10.1016/j.ijsu.2022.106784.
36. Reyes A, Torregrosa L, Lozada-Martinez ID, Cabrera-Vargas LF, Nunez-Ordonez N, Martínez Ibata TF. Breast cancer mortality research in Latin America: A gap needed to be filled. Am J Surg. 2023;225(5):937-8. doi: 10.1016/j.amjsurg.2023.01.010.
37. Reyes Monasterio A, Reyes T, Acevedo Parrales JD, Mantilla-Sylvain F, Cabrera LF, Lozada ID, et al. Social media to overcome technological barriers in surgical training: Four Latin American experiences. Am J Surg. 2023;S0002-9610(23)00206-4. doi: 10.1016/j.amjsurg.2023.05.020.
38. Yeh AC, Li H, Zhu Y, Zhang J, Khramtsova G, Drukker K, et al. Radiogenomics of breast cancer using dynamic contrast enhanced MRI and gene expression profiling. Cancer Imaging. 2019;19(1):48. doi: 10.1186/s40644-019-0233-5.
39. Gallivanone F, Bertoli G, Porro D. Radiogenomics, Breast Cancer Diagnosis and Characterization: Current Status and Future Directions. Methods Protoc. 2022;5(5):78. doi: 10.3390/mps5050078.
40. Liu Q, Hu P. Radiogenomic association of deep MR imaging features with genomic profiles and clinical characteristics in breast cancer. Biomark Res. 2023;11:9. doi: 10.1186/s40364-023-00455-y.
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Dec 29, 2024
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Perez Rodriguez, A. F., Medina Sánchez, D. A., Arias Audor, G. A. A. A., Castrillon Moscote, Y. G., Báez Blanco, L. E., Díaz Mesa , A. M., Manjarres Guevara , A. E., & Ortega Sierra, M. G. O. S. (2024). Independent Artificial Intelligence in Breast Cancer Detection via Digital Mammography: Where is the Future of Breast Radiology Heading?. Hispano-American Journal of Health Sciences, 10(3), 160–167. https://doi.org/10.56239/rhcs.2024.103.815
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