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Introduction <p>Breast cancer Mammography (MAM) screening was proven to improve survival worldwide. However, younger patients with higher breast density made MAM less effective in China. It is necessary to establish Chinese-specific effective screening strategies. This study aims to explore the efficacy of artificial intelligence (AI)-assisted ultrasound breast cancer screening in China.</p> Methods <p>Eligible participants were those aged 35–69&#xa0;years and were attending the Chinese "Two Cancer (breast and cervical cancer) Screening" program. Two districts were selected as cluster to receive either AI-assisted ultrasound screening or routine ultrasound screening. We obtained data on cancer diagnosis through active follow-up and linkage with municipal cancer registry. The primary outcome was improved screening sensitivity enabling the detection of more true-positive cases. This study is registered at ClinicalTrials.gov under the number NCT06521788 (Initial Release Date: 07/22/2024).</p> Results <p>A total of 21,790 individuals in two districts were included in this study, with 8,736 participants in Hongkou district receiving AI-assisted ultrasound screening and 13,054 in Pudong district undergoing routine ultrasound screening. Of the 21,790 screened participants, 232 (10.7‰) tested positive, with AI detecting similar positivity rates compared to routine screening (12.2‰ vs. 9.6‰, <i>P</i> = 0.07). After one year of follow-up, 49 participants were diagnosed with breast cancer: 30 were screen-detected cancers, and 19 were interval cancers. The AI group demonstrated a significantly higher screening sensitivity (75%, 95% CI 54.8–88.6) compared to the routine group (42.8%, 95% CI 22.6–65.6). AI-assisted screening identified more breast cancers than the routine screening group (AI: 21 of 8736; routine: 9 of 13,054, <i>P</i> = 0.001). However, there was no significant difference between the two groups in terms of interval cancer detection (AI: 7 of 8736; routine: 12 of 13,054, <i>P</i> = 0.789). Furthermore, the proportion of early-stage cancers among screen-detected cases was significantly higher in the AI group (95.2%, 20/21) than in the routine group (88.9%, 8/9; <i>p</i> &lt; 0.001).</p> Conclusions <p>AI-assisted ultrasound screening significantly increases the detection rate of early breast cancers.</p> <p><i>Trial registration</i> This study is registered at ClinicalTrials.gov under the number NCT 06521788 (Initial Release Date: 07/22/2024).</p>

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Artificial intelligence-assisted ultrasound screening for breast cancer in China: a prospective, clustered, controlled, population-based study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月7日

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  • Jie Shen,
  • Yajing Liu,
  • Aihong Liu,
  • Xiaoqin Gu,
  • Jin Zhou,
  • Peng Jiang,
  • Miao Mo,
  • Li Zhang,
  • Chen Yang,
  • Changming Zhou,
  • Zezhou Wang,
  • Zhenyu Xie,
  • Wen Yao,
  • Shichong Zhou,
  • Ying Zheng,
  • Cai Chang

摘要

Introduction

Breast cancer Mammography (MAM) screening was proven to improve survival worldwide. However, younger patients with higher breast density made MAM less effective in China. It is necessary to establish Chinese-specific effective screening strategies. This study aims to explore the efficacy of artificial intelligence (AI)-assisted ultrasound breast cancer screening in China.

Methods

Eligible participants were those aged 35–69 years and were attending the Chinese "Two Cancer (breast and cervical cancer) Screening" program. Two districts were selected as cluster to receive either AI-assisted ultrasound screening or routine ultrasound screening. We obtained data on cancer diagnosis through active follow-up and linkage with municipal cancer registry. The primary outcome was improved screening sensitivity enabling the detection of more true-positive cases. This study is registered at ClinicalTrials.gov under the number NCT06521788 (Initial Release Date: 07/22/2024).

Results

A total of 21,790 individuals in two districts were included in this study, with 8,736 participants in Hongkou district receiving AI-assisted ultrasound screening and 13,054 in Pudong district undergoing routine ultrasound screening. Of the 21,790 screened participants, 232 (10.7‰) tested positive, with AI detecting similar positivity rates compared to routine screening (12.2‰ vs. 9.6‰, P = 0.07). After one year of follow-up, 49 participants were diagnosed with breast cancer: 30 were screen-detected cancers, and 19 were interval cancers. The AI group demonstrated a significantly higher screening sensitivity (75%, 95% CI 54.8–88.6) compared to the routine group (42.8%, 95% CI 22.6–65.6). AI-assisted screening identified more breast cancers than the routine screening group (AI: 21 of 8736; routine: 9 of 13,054, P = 0.001). However, there was no significant difference between the two groups in terms of interval cancer detection (AI: 7 of 8736; routine: 12 of 13,054, P = 0.789). Furthermore, the proportion of early-stage cancers among screen-detected cases was significantly higher in the AI group (95.2%, 20/21) than in the routine group (88.9%, 8/9; p < 0.001).

Conclusions

AI-assisted ultrasound screening significantly increases the detection rate of early breast cancers.

Trial registration This study is registered at ClinicalTrials.gov under the number NCT 06521788 (Initial Release Date: 07/22/2024).