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Ha SM, Jang MJ, Youn I, Yoen H, Ji H, Lee SH, Yi A, Chang JM. Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US. Radiology 2024; 312:e233391. [PMID: 39041940 DOI: 10.1148/radiol.233391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Abstract
Background Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography remains unclear. Purpose To compare the performance of mammography alone, mammography with AI, and mammography plus supplemental US for screening women with dense breasts, and to investigate the characteristics of the detected cancers. Materials and Methods A retrospective database search identified consecutive asymptomatic women (≥40 years of age) with dense breasts who underwent mammography plus supplemental whole-breast handheld US from January 2017 to December 2018 at a primary health care center. Sequential reading for mammography alone and mammography with the aid of an AI system was conducted by five breast radiologists, and their recall decisions were recorded. Results of the combined mammography and US examinations were collected from the database. A dedicated breast radiologist reviewed marks for mammography alone or with AI to confirm lesion identification. The reference standard was histologic examination and 1-year follow-up data. The cancer detection rate (CDR) per 1000 screening examinations, sensitivity, specificity, and abnormal interpretation rate (AIR) of mammography alone, mammography with AI, and mammography plus US were compared. Results Among 5707 asymptomatic women (mean age, 52.4 years ± 7.9 [SD]), 33 (0.6%) had cancer (median lesion size, 0.7 cm). Mammography with AI had a higher specificity (95.3% [95% CI: 94.7, 95.8], P = .003) and lower AIR (5.0% [95% CI: 4.5, 5.6], P = .004) than mammography alone (94.3% [95% CI: 93.6, 94.8] and 6.0% [95% CI: 5.4, 6.7], respectively). Mammography plus US had a higher CDR (5.6 vs 3.5 per 1000 examinations, P = .002) and sensitivity (97.0% vs 60.6%, P = .002) but lower specificity (77.6% vs 95.3%, P < .001) and higher AIR (22.9% vs 5.0%, P < .001) than mammography with AI. Supplemental US alone helped detect 12 cancers, mostly stage 0 and I (92%, 11 of 12). Conclusion Although AI improved the specificity of mammography interpretation, mammography plus supplemental US helped detect more node-negative early breast cancers that were undetected using mammography with AI. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Whitman and Destounis in this issue.
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Affiliation(s)
- Su Min Ha
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
| | - Myoung-Jin Jang
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
| | - Inyoung Youn
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
| | - Heera Yoen
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
| | - Hye Ji
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
| | - Su Hyun Lee
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
| | - Ann Yi
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
| | - Jung Min Chang
- From the Department of Radiology (S.M.H., H.Y., H.J., S.H.L., J.M.C.) and Medical Research Collaborating Center (M.J.J.), Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul 03080, Republic of Korea; Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea (S.M.H., S.H.L., J.M.C.); Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Republic of Korea (S.M.H.); Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea (I.Y.); and Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Republic of Korea (A.Y.)
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Yoen H, Jang MJ, Yi A, Moon WK, Chang JM. Artificial Intelligence for Breast Cancer Detection on Mammography: Factors Related to Cancer Detection. Acad Radiol 2024; 31:2239-2247. [PMID: 38216413 DOI: 10.1016/j.acra.2023.12.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 12/01/2023] [Accepted: 12/01/2023] [Indexed: 01/14/2024]
Abstract
RATIONALE AND OBJECTIVES Little is known about the factors affecting the Artificial Intelligence (AI) software performance on mammography for breast cancer detection. This study was to identify factors associated with abnormality scores assigned by the AI software. MATERIALS AND METHODS A retrospective database search was conducted to identify consecutive asymptomatic women who underwent breast cancer surgery between April 2016 and December 2019. A commercially available AI software (Lunit INSIGHT, MMG, Ver. 1.1.4.0) was used for preoperative mammography to assign individual abnormality scores to the lesions and score of 10 or higher was considered as positive detection by AI software. Radiologists without knowledge of the AI results retrospectively assessed the mammographic density and classified mammographic findings into positive and negative finding. General linear model (GLM) analysis was used to identify the clinical, pathological, and mammographic findings related to the abnormality scores, obtaining coefficient β values that represent the mean difference per unit or comparison with the reference value. Additionally, the reasons for non-detection by the AI software were investigated. RESULTS Among the 1001 index cancers (830 invasive cancers and 171 ductal carcinoma in situs) in 1001 patients, 717 (72%) were correctly detected by AI, while the remaining 284 (28%) were not detected. Multivariable GLM analysis showed that abnormal mammography findings (β = 77.0 for mass, β = 73.1 for calcification only, β = 49.4 for architectural distortion, and β = 47.6 for asymmetry compared to negative; all Ps < 0.001), invasive tumor size (β = 4.3 per 1 cm, P < 0.001), and human epidermal growth receptor type 2 (HER2) positivity (β = 9.2 compared to hormone receptor positive, HER2 negative, P = 0.004) were associated with higher mean abnormality score. AI failed to detect small asymmetries in extremely dense breasts, subcentimeter-sized or isodense lesions, and faint amorphous calcifications. CONCLUSION Cancers with positive abnormal mammographic findings on retrospective review, large invasive size, HER2 positivity had high AI abnormality scores. Understanding the patterns of AI software performance is crucial for effectively integrating AI into clinical practice.
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Affiliation(s)
- Heera Yoen
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Myoung-Jin Jang
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Ann Yi
- Department of Radiology, Seoul National University Hospital Healthcare System Gangnam Center, Seoul, Korea
| | - Woo Kyung Moon
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea
| | - Jung Min Chang
- Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, Republic of Korea.
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