Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM): A Prospective Multicenter Study Design in Korea Using AI-Based CADe/x

Yun-Woo Chang, Jin Kyung An, Nami Choi, Kyung Hee Ko, Ki Hwan Kim, Kyunghwa Han, Jung Kyu Ryu, Yun-Woo Chang, Jin Kyung An, Nami Choi, Kyung Hee Ko, Ki Hwan Kim, Kyunghwa Han, Jung Kyu Ryu

Abstract

Purpose: Artificial intelligence (AI)-based computer-aided detection/diagnosis (CADe/x) has helped improve radiologists' performance and provides results equivalent or superior to those of radiologists' alone. This prospective multicenter cohort study aims to generate real-world evidence on the overall benefits and disadvantages of using AI-based CADe/x for breast cancer detection in a population-based breast cancer screening program comprising Korean women aged ≥ 40 years. The purpose of this report is to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of Korean women with average breast cancer risk.

Methods: Approximately 32,714 participants will be enrolled between February 2021 and December 2022 at 5 study sites in Korea. A radiologist specializing in breast imaging will interpret the mammography readings with or without the use of AI-based CADe/x. If recall is required, further diagnostic workup will be conducted to confirm the cancer detected on screening. The findings will be recorded for all participants regardless of their screening status to identify study participants with breast cancer diagnosis within both 1 year and 2 years of screening. The national cancer registry database will be reviewed in 2026 and 2027, and the results of this study are expected to be published in 2027. In addition, the diagnostic accuracy of general radiologists and radiologists specializing in breast imaging from another hospital with or without the use of AI-based CADe/x will be compared considering mammography readings for breast cancer screening.

Discussion: The Artificial Intelligence for Breast Cancer Screening in Mammography (AI-STREAM) study is a prospective multicenter study that aims to compare the diagnostic accuracy of radiologists with and without the use of AI-based CADe/x in mammography readings for breast cancer screening of women with average breast cancer risk. AI-STREAM is currently in the patient enrollment phase.

Trial registration: ClinicalTrials.gov Identifier: NCT05024591.

Keywords: Artificial Intelligence; Breast; Clinical Trial; Digital Mammography; Early Detection of Cancer.

Conflict of interest statement

Kim KH is an employee of Lunit. All other authors declare that they have no competing interests.

© 2022 Korean Breast Cancer Society.

Figures

Figure 1. Overview of the study design.
Figure 1. Overview of the study design.
AI = artificial intelligence.
Figure 2. Overview of the study timeline.
Figure 2. Overview of the study timeline.
Scenarios A and B: Enrolled and diagnosed with cancer at 6 or 12 months; classified as having breast cancer diagnosis within 1 year from screening. Scenario C: Diagnosed with cancer within 24 months of enrollment; classified as having breast cancer diagnosis within 2 years from screening. Scenario D: Not diagnosed with cancer until 24 months after enrollment. The results of Scenarios A–D can be termed positive or negative depending on the cancer diagnosis. Scenario E: Died during the study because of any reason and cannot be included in the study. Scenario F: Lost to follow-up and cannot be included in the study because of no available records.

References

    1. Francies FZ, Hull R, Khanyile R, Dlamini Z. Breast cancer in low-middle income countries: abnormality in splicing and lack of targeted treatment options. Am J Cancer Res. 2020;10:1568–1591.
    1. National Cancer Center. Cancer Statistics. [Accessed September 22nd, 2021]. .
    1. Kaplan HG, Malmgren JA, Atwood MK, Calip GS. Effect of treatment and mammography detection on breast cancer survival over time: 1990-2007. Cancer. 2015;121:2553–2561.
    1. Choi KS, Yoon M, Song SH, Suh M, Park B, Jung KW, et al. Effect of mammography screening on stage at breast cancer diagnosis: results from the Korea National Cancer Screening Program. Sci Rep. 2018;8:8882.
    1. Lee K, Kim H, Lee JH, Jeong H, Shin SA, Han T, et al. Retrospective observation on contribution and limitations of screening for breast cancer with mammography in Korea: detection rate of breast cancer and incidence rate of interval cancer of the breast. BMC Womens Health. 2016;16:72.
    1. Majid AS, de Paredes ES, Doherty RD, Sharma NR, Salvador X. Missed breast carcinoma: pitfalls and pearls. Radiographics. 2003;23:881–895.
    1. Jo HM, Lee EH, Ko K, Kang BJ, Cha JH, Yi A, et al. Prevalence of women with dense breasts in Korea: results from a nationwide cross-sectional study. Cancer Res Treat. 2019;51:1295–1301.
    1. Skaane P, Engedal K, Skjennald A. Interobserver variation in the interpretation of breast imaging. Comparison of mammography, ultrasonography, and both combined in the interpretation of palpable noncalcified breast masses. Acta Radiol. 1997;38:497–502.
    1. Lehman CD, Arao RF, Sprague BL, Lee JM, Buist DS, Kerlikowske K, et al. National performance benchmarks for modern screening digital mammography: update from the Breast Cancer Surveillance Consortium. Radiology. 2017;283:49–58.
    1. Giger ML, Chan HP, Boone J. Anniversary paper: History and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med Phys. 2008;35:5799–5820.
    1. Wu N, Phang J, Park J, Shen Y, Huang Z, Zorin M, et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans Med Imaging. 2020;39:1184–1194.
    1. Kim HE, Kim HH, Han BK, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit Health. 2020;2:e138–e148.
    1. 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:e468–e474.
    1. Sickles EA, D'Orsi CJ, Bassett LW. ACR BI-RADS® Atlas. Reston: American College of Radiology; 2013. ACR BI-RADS® mammography.
    1. Hong S, Song SY, Park B, Suh M, Choi KS, Jung SE, et al. Effect of Digital mammography for breast cancer screening: a comparative study of more than 8 million Korean women. Radiology. 2020;294:247–255.
    1. Hillis SL. A marginal-mean ANOVA approach for analyzing multireader multicase radiological imaging data. Stat Med. 2014;33:330–360.
    1. Salim M, Wåhlin E, Dembrower K, Azavedo E, Foukakis T, Liu Y, et al. External evaluation of 3 commercial artificial intelligence algorithms for independent assessment of screening mammograms. JAMA Oncol. 2020;6:1581–1588.
    1. Taylor-Phillips S, Jenkinson D, Stinton C, Wallis MG, Dunn J, Clarke A. Double reading in breast cancer screening: cohort evaluation in the CO-OPS trial. Radiology. 2018;287:749–757.
    1. Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL, et al. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med. 2015;175:1828–1837.
    1. Lee EH, Kim KW, Kim YJ, Shin DR, Park YM, Lim HS, et al. Performance of screening mammography: a report of the alliance for breast cancer screening in Korea. Korean J Radiol. 2016;17:489–496.
    1. Yoon JH, Kim EK. Deep learning-based artificial intelligence for mammography. Korean J Radiol. 2021;22:1225–1239.
    1. Rodriguez-Ruiz A, Lång K, Gubern-Merida A, Teuwen J, Broeders M, Gennaro G, 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;29:4825–4832.

Source: PubMed

赞助者和合作者

医疗条件

药物干预

3
订阅