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<p>Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-based computer-aided detection (AI-CAD) for screening mammograms in a real-world, single-read setting. A prospective multicenter cohort study is conducted within South Korea’s national breast cancer screening program for women. The primary outcomes are screen-detected breast cancer within one year, with a focus on cancer detection rates (CDRs) and recall rates (RRs) of radiologists. A total of 24,543 women are included in the final cohort, with 140 (0.57%) screen-detected breast cancers. The CDR is significantly higher by 13.8% for breast radiologists using AI-CAD (<i>n</i> = 140 [5.70‰]) compared to those without AI (<i>n</i> = 123 [5.01‰]; <i>p</i> &lt; 0.001), with no significant difference in RRs (<i>p</i> = 0.564). These preliminary results show a significant improvement in CDRs without affecting RRs in a radiologist’s standard single-reading setting (ClinicalTrials.gov: NCT05024591).</p>

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Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月6日

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  • Yun-Woo Chang,
  • Jung Kyu Ryu,
  • Jin Kyung An,
  • Nami Choi,
  • Young Mi Park,
  • Kyung Hee Ko,
  • Kyunghwa Han

摘要

Artificial intelligence (AI) improves the accuracy of mammography screening, but prospective evidence, particularly in a single-read setting, remains limited. This study compares the diagnostic accuracy of breast radiologists with and without AI-based computer-aided detection (AI-CAD) for screening mammograms in a real-world, single-read setting. A prospective multicenter cohort study is conducted within South Korea’s national breast cancer screening program for women. The primary outcomes are screen-detected breast cancer within one year, with a focus on cancer detection rates (CDRs) and recall rates (RRs) of radiologists. A total of 24,543 women are included in the final cohort, with 140 (0.57%) screen-detected breast cancers. The CDR is significantly higher by 13.8% for breast radiologists using AI-CAD (n = 140 [5.70‰]) compared to those without AI (n = 123 [5.01‰]; p < 0.001), with no significant difference in RRs (p = 0.564). These preliminary results show a significant improvement in CDRs without affecting RRs in a radiologist’s standard single-reading setting (ClinicalTrials.gov: NCT05024591).