人工智能辅助超声对中国女性乳腺病灶识别的有效性研究

沈洁; 刘雅静; 莫淼; 周瑾; 王泽洲; 周昌明; 周世崇; 常才; 郑莹

doi:10.19401/j.cnki.1007-3639.2023.11.005

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人工智能辅助超声对中国女性乳腺病灶识别的有效性研究

论著 | 更新时间：2025-12-31

- 人工智能辅助超声对中国女性乳腺病灶识别的有效性研究
- Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women
- 中国癌症杂志 2023年33卷第11期页码：1002-1008
- 作者机构：
  
  1. 复旦大学附属肿瘤医院肿瘤预防部，复旦大学上海医学院肿瘤学系，上海 200032
  2. 复旦大学附属肿瘤医院超声科，复旦大学上海医学院肿瘤学系，上海 200032
  3. 上海肿瘤疾病人工智能工程技术研究中心，上海 200032
- 作者简介：
  
  [ "沈洁（ORCID: 0000-0003-2504-4491），主管医师。" ]
  郑莹（ORCID: 0000-0002-6408-8510），主任医师，复旦大学附属肿瘤医院肿瘤预防部主任。
- 基金信息：
  
  上海市老龄化和妇儿健康研究专项(2020YJZX0206);上海市申康医院发展中心市级医院诊疗技术推广及优化管理项目(SHDC22022308);上海申康医院发展中心管理研究项目(2022SKMR-24)
- DOI：10.19401/j.cnki.1007-3639.2023.11.005
  中图分类号： R737.9
- 收稿：2023-07-13，
  
  修回：2023-09-05，
  
  纸质出版：2023-11-30
- 稿件说明：
移动端阅览
沈洁, 刘雅静, 莫淼, 等. 人工智能辅助超声对中国女性乳腺病灶识别的有效性研究[J]. 中国癌症杂志, 2023,33(11):1002-1008.

Jie SHEN, Yajing LIU, Miao MO, et al. Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women[J]. China Oncology, 2023, 33(11): 1002-1008.
沈洁, 刘雅静, 莫淼, 等. 人工智能辅助超声对中国女性乳腺病灶识别的有效性研究[J]. 中国癌症杂志, 2023,33(11):1002-1008. DOI： 10.19401/j.cnki.1007-3639.2023.11.005.

Jie SHEN, Yajing LIU, Miao MO, et al. Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women[J]. China Oncology, 2023, 33(11): 1002-1008. DOI： 10.19401/j.cnki.1007-3639.2023.11.005.

摘要

背景与目的：

人工智能（artificial intelligence，AI）技术可辅助影像学诊断。本研究探讨AI辅助超声对中国女性乳腺病灶的识别能力及其应用于乳腺癌筛查的可能性。

方法：

采用平行对照诊断性试验和前瞻性随访的研究设计，纳入至肿瘤专科医院就诊、并行乳腺超声检查的非乳腺癌女性。所有女性首先接受AI辅助超声检查，然后接受常规超声检查，比较AI辅助超声和常规超声识别乳腺病灶的差异；随访1年内乳腺癌发生情况，比较两种超声方式诊断乳腺癌的灵敏度和特异度。

结果：

研究纳入360人，共发现2 504个乳腺病灶，其中AI辅助超声报告2 217个病灶，病灶报告率为88.5%；常规超声报告1 090个病灶，病灶报告率为43.5%。以常规超声为标准，AI辅助超声识别乳腺影像报告和数据系统（Breast Imaging Reporting and Data System，BI-RADS）4级以上乳腺病灶的灵敏度为93.3%（95% CI：80.7%~98.3%），特异度为100.0%（95% CI：99.5%~100.0%）；随访发现10例乳腺癌，AI辅助超声和常规超声均判定为阳性的有8例，灵敏度均为80.0%（95% CI：44.2%~96.4%），特异度均为88.6

%（95% CI：84.6%~91.6%）。

结论：

AI辅助超声对于BI-RADS 4A以上的高危乳腺病灶及早期乳腺癌的识别能力与常规超声相当，是一种有效的乳腺癌辅助诊断手段，并具有应用于人群乳腺癌筛查的潜力。

Abstract

Background and purpose:

Artificial intelligence (AI) technology is increasingly being used in the medical field. This study aimed to assess the effectiveness of artificial intelligence ultrasound system for identifying breast lesions in Chinese women and its role in breast cancer early detection.

Methods:

A prospective study was conducted on healthy women aged 35-74 years who came to Fudan University Shanghai Cancer Center from August 2020 to December 2020 for breast ultrasonography. All the women were examined by AI-assisted ultrasound first

and then by conventional ultrasonography. We compared the differences between AI-assisted ultrasound and conventional ultrasonography in identifying breast lesions in Chinese women. One year later

we looked up the hospital medical history and Shanghai Cancer Registration Management System for the final diagnosis of breast cancer.

Results:

A total of 360 women were included in the study and received breast examinations using both AI-assisted ultrasound and conventional ultrasound. A total of 2 504 breast lesions were detected

of which

2 217 were detected by AI-assisted ultrasound

with a lesion recognition rate of 88.5%. Conventional ultrasound identified 1 090 lesions

with a lesion recognition rate of 43.5%. Using conventional ultrasound as the standard

the sensitivity and specificity of AI-assisted ultrasound for Breast Imaging Reporting and Data System (BI-RADS) level 4 and above lesions were 93.3% (95% CI: 80.7-98.3) and 100.0% (95% CI: 99.5-100.0)

respectively. During one-year follow-up

10 patients were diagnosed with breast cancer

and 8 of whom were identified by both AI-assisted ultrasound and conventional B ultrasound. The sensitivity of AI-assisted ultrasound and conventional ultrasound for breast cancer was 80.0% (9

5% CI: 44.2-96.4)

and the specificity was 88.6% (95% CI: 84.6-91.6).

Conclusion:

AI-assisted ultrasound has good identification ability for breast lesions in Chinese women. The recognition ability for high-risk breast lesions (BI-RADS 4A and above) and early breast cancer is equivalent to that of conventional ultrasound

which is suitable for breast cancer screening in large-scale community of women with general risk.

关键词

Keywords

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