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

Journal: Radiology
PMID: 40197097

Abstract

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; = .01; 55.0% [61 of 111]; 95% CI: 45.2, 64.4; = .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; < .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 See also the editorial by Philpotts in this issue.

Authors

  • Su Min Ha
  • Janie M Lee
    Department of Radiology, Seattle Cancer Care Alliance, University of Washington, 825 Eastlake Avenue East, Suite G2-600, Seattle, WA, 98109, USA.
  • Myoung-Jin Jang
    From the Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, 101 Daehak-ro, Chongno-gu, Seoul 03080, Korea (S.L., J.M.G., S.H.Y.); Division of Pulmonary and Critical Medicine, Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea (J.J.Y., N.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea (Y.J.L.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul, Korea (J.K.L.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, National Medical Center, Seoul, Korea (J.Y.L.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Incheon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Incheon, Korea (J.S.K.); Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea (Y.A.K.); Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea (D.J.); Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Korea (M.J.J.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea (J.M.G.); and Department of Radiology, UMass Memorial Medical Center, Worcester, Mass (S.H.Y.).
  • Hong-Kyu Kim
    Department of Surgery, Seoul National University College of Medicine and Seoul National University Hospital, Seoul, Republic of Korea.
  • Jung Min Chang
    Department of Radiology, Seoul National University Hospital, Seoul 110-744, South Korea.

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