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

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; <i>P</i> = .01; 55.0% [61 of 111]; 95% CI: 45.2, 64.4; <i>P</i> = .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; <i>P</i> < .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 <i>Supplemental material is available for this article.</i> See also the editorial by Philpotts in this issue.

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Publication Details

Journal
Radiology
Published
2025-04-01
DOI
https://doi.org/10.1148/radiol.242955
Citations
1
Primary Topic
AI in cancer detection
Type
article
Field-Weighted Citation Impact
2.48

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article

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

Jung Min Chang, Janie M. Lee, Su Min Ha, Hong Kyu Kim et al.
1 citations
Radiology
AI in cancer detection
2.48
article

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

Jung Min Chang, Janie M. Lee, Su Min Ha, Hong Kyu Kim, Myoung‐jin Jang
article en
1 citations

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; <i>P</i> = .01; 55.0% [61 of 111]; 95% CI: 45.2, 64.4; <i>P</i> = .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; <i>P</i> < .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 <i>Supplemental material is available for this article.</i> See also the editorial by Philpotts in this issue.

RadiologyVol. 315(1)
Seoul National University (KR), University of Washington (US), Seoul National University Hospital (KR)
Seoul National University Hospital
Good health and well-being
Openalex Percentile: Top 11%
AI in cancer detection
2.48
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