Breast Cancer Detection with Standalone AI versus Radiologist Interpretation of Unilateral Surveillance Mammography after Mastectomy

  • Deputy Editor: Linda Moy
  • Scientific Editor: Shannyn Wolfe
Published Online:https://doi.org/10.1148/radiol.242955

Standalone artificial intelligence (AI) could detect contralateral breast cancers in patients treated with unilateral mastectomy, showing higher cancer detection rates and sensitivity than radiologists without AI assistance.

Background

Limited data are available regarding the accuracy of artificial intelligence (AI) algorithms trained on bilateral mammograms for second breast cancer surveillance in patients with a personal history of breast cancer treated with unilateral mastectomy.

Purpose

To compare the performance of standalone AI for second breast cancer surveillance on unilateral mammograms with that of radiologists reading mammograms without AI assistance.

Materials and Methods

In this retrospective institutional database study, patients who were diagnosed with breast cancer between January 2001 and December 2018 and underwent postmastectomy surveillance mammography from January 2011 to March 2023 were included. Radiologists’ mammogram interpretations without AI assistance were collected from these records and compared with AI interpretations of the same mammograms. The reference standards were histologic examination and 1-year follow-up data. The cancer detection rate per 1000 screening examinations, sensitivity, and specificity of standalone AI and the radiologists’ interpretations without AI were compared using the McNemar test.

Results

Among the 4184 asymptomatic female patients (mean age, 52 years), 111 (2.7%) had contralateral second breast cancer. The cancer detection rate (17.4 per 1000 examinations [73 of 4184]; 95% CI: 13.7, 21.9) and sensitivity (65.8% [73 of 111]; 95% CI: 56.2, 74.5) were greater for standalone AI than for radiologists (14.6 per 1000 examinations [61 of 4184]; 95% CI: 11.2, 18.7; P = .01; 55.0% [61 of 111]; 95% CI: 45.2, 64.4; P = .01). The specificity was lower for standalone AI than for radiologists (91.5% [3725 of 4073]; 95% CI: 90.6, 92.3 vs 98.1% [3996 of 4073]; 95% CI: 97.6, 98.5; P < .001). AI detected 16 of 50 (32%) cancers missed by radiologists; however, 34 of 111 (30.6%) breast cancers were missed by both radiologists and AI.

Conclusion

Standalone AI for surveillance mammography showed higher sensitivity with lower specificity for contralateral breast cancer detection in patients treated with unilateral mastectomy than radiologists without AI assistance.

© RSNA, 2025

Supplemental material is available for this article.

See also the editorial by Philpotts in this issue.

References

  • 1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71(3):209–249.
  • 2. Houssami N, Ciatto S. Mammographic surveillance in women with a personal history of breast cancer: how accurate? How effective? Breast 2010;19(6):439–445.
  • 3. Lawson MB, Herschorn SD, Sprague BL, et al. 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.
  • 4. Lee CI, Abraham L, Miglioretti DL, et al. National Performance Benchmarks for Screening Digital Breast Tomosynthesis: Update from the Breast Cancer Surveillance Consortium. Radiology 2023;307(4):e222499.
  • 5. Lee JM, Ichikawa LE, Wernli KJ, et al. Digital Mammography and Breast Tomosynthesis Performance in Women with a Personal History of Breast Cancer, 2007-2016. Radiology 2021;300(2):290–300.
  • 6. Lamb LR, Mohallem Fonseca M, Verma R, Seely JM. Missed Breast Cancer: Effects of Subconscious Bias and Lesion Characteristics. RadioGraphics 2020;40(4):941–960.
  • 7. Kim HE, Kim HH, Han BK, et al. 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.
  • 8. Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists. J Natl Cancer Inst 2019;111(9):916–922.
  • 9. Pacilè S, Lopez J, Chone P, Bertinotti T, Grouin JM, Fillard P. Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool. Radiol Artif Intell 2020;2(6):e190208.
  • 10. Rodriguez-Ruiz A, Lång K, Gubern-Merida A, et al. Can we reduce the workload of mammographic screening by automatic identification of normal exams with artificial intelligence? A feasibility study. Eur Radiol 2019;29(9):4825–4832.
  • 11. Dembrower K, Wåhlin E, Liu Y, 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–e474.
  • 12. Lauritzen AD, Rodríguez-Ruiz A, von Euler-Chelpin MC, et al. An Artificial Intelligence-based Mammography Screening Protocol for Breast Cancer: Outcome and Radiologist Workload. Radiology 2022;304(1):41–49.
  • 13. Wanders AJT, Mees W, Bun PAM, et al. Interval Cancer Detection Using a Neural Network and Breast Density in Women with Negative Screening Mammograms. Radiology 2022;303(2):269–275.
  • 14. Larsen M, Aglen CF, Lee CI, et al. Artificial Intelligence Evaluation of 122 969 Mammography Examinations from a Population-based Screening Program. Radiology 2022;303(3):502–511.
  • 15. Park GE, Kang BJ, Kim SH, Lee J. Retrospective Review of Missed Cancer Detection and Its Mammography Findings with Artificial-Intelligence-Based, Computer-Aided Diagnosis. Diagnostics (Basel) 2022;12(2):387.
  • 16. Yoon JH, Strand F, Baltzer PAT, 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.
  • 17. Lång K, Josefsson V, Larsson AM, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol 2023;24(8):936–944.
  • 18. 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.
  • 19. American Joint Committee on Cancer. AJCC cancer staging manual. 8th ed. Springer, 2017.
  • 20. 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.
  • 21. D’Orsi CJ, Bassett LW, Berg WA, et al. Mammography. In: Breast Imaging reporting and data system (BIRADS). 4th ed. American College of Radiology, 2003.
  • 22. D’Orsi CJ, Sickles EA, Mendelson EB, Morris EA. ACR BI-RADS Atlas: Breast Imaging Reporting and Data System. 5th ed. American College of Radiology, 2013.
  • 23. Harris E. FDA Updates breast density reporting standards, other mammogram rules. JAMA 2023;329(14):1142–1143.
  • 24. Kim EK, Kim HE, Han K, et al. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study. Sci Rep 2018;8(1):2762.
  • 25. Cho N, Han W, Han BK, et al. Breast Cancer Screening With Mammography Plus Ultrasonography or Magnetic Resonance Imaging in Women 50 Years or Younger at Diagnosis and Treated With Breast Conservation Therapy. JAMA Oncol 2017;3(11):1495–1502.
  • 26. Kim SY, Cho N, Kim SY, et al. Supplemental Breast US Screening in Women with a Personal History of Breast Cancer: A Matched Cohort Study. Radiology 2020;295(1):54–63.
  • 27. Berg WA, Berg JM, Bandos AI, et al. Addition of Contrast-enhanced Mammography to Tomosynthesis for Breast Cancer Detection in Women with a Personal History of Breast Cancer: Prospective TOCEM Trial Interim Analysis. Radiology 2024;311(1):e231991.
  • 28. Lamb LR, Lehman CD, Gastounioti A, Conant EF, Bahl M. Artificial Intelligence (AI) for Screening Mammography, From the AJR Special Series on AI Applications. AJR Am J Roentgenol 2022;219(3):369–380.
  • 29. Lehman CD, Yala A, Schuster T, et al. Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation. Radiology 2019;290(1):52–58.
  • 30. Lowry KP, Zuiderveld CC. Artificial Intelligence for Breast Cancer Risk Assessment. Radiol Clin North Am 2024;62(4):619–625.
  • 31. Yala A, Lehman C, Schuster T, Portnoi T, Barzilay R. A Deep Learning Mammography-based Model for Improved Breast Cancer Risk Prediction. Radiology 2019;292(1):60–66.
  • 32. Liu Y, Sorkhei M, Dembrower K, Azizpour H, Strand F, Smith K. Use of an AI Score Combining Cancer Signs, Masking, and Risk to Select Patients for Supplemental Breast Cancer Screening. Radiology 2024;311(1):e232535.
  • 33. Hickman SE, Woitek R, Le EPV, et al. Machine Learning for Workflow Applications in Screening Mammography: Systematic Review and Meta-Analysis. Radiology 2022;302(1):88–104.
  • 34. Ha SM, Jang MJ, Youn I, et al. Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US. Radiology 2024;312(1):e233391.

Article History

Received: Oct 2 2024
Revision requested: Nov 11 2024
Revision received: Jan 14 2025
Accepted: Jan 16 2025
Published online: Apr 08 2025