Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women
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. Me...
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| Format: | Article |
| Language: | English |
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Editorial Office of China Oncology
2023-11-01
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| Series: | Zhongguo aizheng zazhi |
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| Online Access: | http://www.china-oncology.com/fileup/1007-3639/PDF/1702517864609-626391529.pdf |
| _version_ | 1797300320697581568 |
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| author | SHEN Jie, LIU Yajing, MO Miao, ZHOU Jin, WANG Zezhou, ZHOU Changming, ZHOU Shichong, CHANG Cai, ZHENG Ying |
| author_facet | SHEN Jie, LIU Yajing, MO Miao, ZHOU Jin, WANG Zezhou, ZHOU Changming, ZHOU Shichong, CHANG Cai, ZHENG Ying |
| author_sort | SHEN Jie, LIU Yajing, MO Miao, ZHOU Jin, WANG Zezhou, ZHOU Changming, ZHOU Shichong, CHANG Cai, ZHENG Ying |
| collection | DOAJ |
| description | 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% (95% 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. |
| first_indexed | 2024-03-07T23:04:35Z |
| format | Article |
| id | doaj.art-de9dd0d8cd274413ac7e51268764e10f |
| institution | Directory Open Access Journal |
| issn | 1007-3639 |
| language | English |
| last_indexed | 2024-03-07T23:04:35Z |
| publishDate | 2023-11-01 |
| publisher | Editorial Office of China Oncology |
| record_format | Article |
| series | Zhongguo aizheng zazhi |
| spelling | doaj.art-de9dd0d8cd274413ac7e51268764e10f2024-02-22T06:28:17ZengEditorial Office of China OncologyZhongguo aizheng zazhi1007-36392023-11-0133111002100810.19401/j.cnki.1007-3639.2023.11.005Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese womenSHEN Jie, LIU Yajing, MO Miao, ZHOU Jin, WANG Zezhou, ZHOU Changming, ZHOU Shichong, CHANG Cai, ZHENG Ying01. Department of Cancer Prevention, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;2. Department of Ultrasound, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China;3. Shanghai Engineering Research Center of Artificial Intelligence Technology for Tumor Diseases, Shanghai 200032, ChinaBackground 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% (95% 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.http://www.china-oncology.com/fileup/1007-3639/PDF/1702517864609-626391529.pdf|breast cancer screening|artificial intelligence|breast ultrasonography |
| spellingShingle | SHEN Jie, LIU Yajing, MO Miao, ZHOU Jin, WANG Zezhou, ZHOU Changming, ZHOU Shichong, CHANG Cai, ZHENG Ying Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women Zhongguo aizheng zazhi |breast cancer screening|artificial intelligence|breast ultrasonography |
| title | Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women |
| title_full | Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women |
| title_fullStr | Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women |
| title_full_unstemmed | Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women |
| title_short | Effectiveness of artificial intelligence-assisted ultrasound for breast cancer screening in Chinese women |
| title_sort | effectiveness of artificial intelligence assisted ultrasound for breast cancer screening in chinese women |
| topic | |breast cancer screening|artificial intelligence|breast ultrasonography |
| url | http://www.china-oncology.com/fileup/1007-3639/PDF/1702517864609-626391529.pdf |
| work_keys_str_mv | AT shenjieliuyajingmomiaozhoujinwangzezhouzhouchangmingzhoushichongchangcaizhengying effectivenessofartificialintelligenceassistedultrasoundforbreastcancerscreeninginchinesewomen |