Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks
Abstract Purpose We developed an artificial intelligence system (AIS) using multi-view multi-level convolutional neural networks for breast cancer detection, diagnosis, and BI-RADS categorization support in mammography. Methods Twenty-four thousand eight hundred sixty-six breasts from 12,433 Asian w...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | Insights into Imaging |
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| Online Access: | https://doi.org/10.1186/s13244-025-01983-x |
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| _version_ | 1850125443540189184 |
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| author | Hongna Tan Qingxia Wu Yaping Wu Bingjie Zheng Bo Wang Yan Chen Lijuan Du Jing Zhou Fangfang Fu Huihui Guo Cong Fu Lun Ma Pei Dong Zhong Xue Dinggang Shen Meiyun Wang |
| author_facet | Hongna Tan Qingxia Wu Yaping Wu Bingjie Zheng Bo Wang Yan Chen Lijuan Du Jing Zhou Fangfang Fu Huihui Guo Cong Fu Lun Ma Pei Dong Zhong Xue Dinggang Shen Meiyun Wang |
| author_sort | Hongna Tan |
| collection | DOAJ |
| description | Abstract Purpose We developed an artificial intelligence system (AIS) using multi-view multi-level convolutional neural networks for breast cancer detection, diagnosis, and BI-RADS categorization support in mammography. Methods Twenty-four thousand eight hundred sixty-six breasts from 12,433 Asian women between August 2012 and December 2018 were enrolled. The study consisted of three parts: (1) evaluation of AIS performance in malignancy diagnosis; (2) stratified analysis of BI-RADS 3–4 subgroups with AIS; and (3) reassessment of BI-RADS 0 breasts with AIS assistance. We further evaluate AIS by conducting a counterbalance-designed AI-assisted study, where ten radiologists read 1302 cases with/without AIS assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score were measured. Results The AIS yielded AUC values of 0.995, 0.933, and 0.947 for malignancy diagnosis in the validation set, testing set 1, and testing set 2, respectively. Within BI-RADS 3–4 subgroups with pathological results, AIS downgraded 83.1% of false-positives into benign groups, and upgraded 54.1% of false-negatives into malignant groups. AIS also successfully assisted radiologists in identifying 7 out of 43 malignancies initially diagnosed with BI-RADS 0, with a specificity of 96.7%. In the counterbalance-designed AI-assisted study, the average AUC across ten readers significantly improved with AIS assistance (p = 0.001). Conclusion AIS can accurately detect and diagnose breast cancer on mammography and further serve as a supportive tool for BI-RADS categorization. Critical relevance statement An AI risk assessment tool employing deep learning algorithms was developed and validated for enhancing breast cancer diagnosis from mammograms, to improve risk stratification accuracy, particularly in patients with dense breasts, and serve as a decision support aid for radiologists. Key Points The false positive and negative rates of mammography diagnosis remain high. The AIS can yield a high AUC for malignancy diagnosis. The AIS is important in stratifying BI-RADS categorization. Graphical Abstract |
| format | Article |
| id | doaj-art-1bd15803afce415e8aeedd645c54a8ba |
| institution | OA Journals |
| issn | 1869-4101 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Insights into Imaging |
| spelling | doaj-art-1bd15803afce415e8aeedd645c54a8ba2025-08-20T02:34:07ZengSpringerOpenInsights into Imaging1869-41012025-05-0116111210.1186/s13244-025-01983-xMammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networksHongna Tan0Qingxia Wu1Yaping Wu2Bingjie Zheng3Bo Wang4Yan Chen5Lijuan Du6Jing Zhou7Fangfang Fu8Huihui Guo9Cong Fu10Lun Ma11Pei Dong12Zhong Xue13Dinggang Shen14Meiyun Wang15Department of Radiology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou UniversityBeijing United Imaging Research Institute of Intelligent ImagingDepartment of Radiology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Cancer Hospital, Affiliated Cancer Hospital of Zhengzhou University ZhengzhouDepartment of Radiology, The First Affiliated Hospital of Zhengzhou UniversityDepartment of Radiology, The Third Affiliated Hospital of Zhengzhou UniversityDepartment of Radiology, Zhengzhou Central HospitalDepartment of Radiology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou UniversityDepartment of Radiology, Fuwai Central China Cardiovascular HospitalBeijing United Imaging Research Institute of Intelligent ImagingShanghai United Imaging Intelligence Co. LtdShanghai United Imaging Intelligence Co. LtdDepartment of Radiology, Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou UniversityAbstract Purpose We developed an artificial intelligence system (AIS) using multi-view multi-level convolutional neural networks for breast cancer detection, diagnosis, and BI-RADS categorization support in mammography. Methods Twenty-four thousand eight hundred sixty-six breasts from 12,433 Asian women between August 2012 and December 2018 were enrolled. The study consisted of three parts: (1) evaluation of AIS performance in malignancy diagnosis; (2) stratified analysis of BI-RADS 3–4 subgroups with AIS; and (3) reassessment of BI-RADS 0 breasts with AIS assistance. We further evaluate AIS by conducting a counterbalance-designed AI-assisted study, where ten radiologists read 1302 cases with/without AIS assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, and F1 score were measured. Results The AIS yielded AUC values of 0.995, 0.933, and 0.947 for malignancy diagnosis in the validation set, testing set 1, and testing set 2, respectively. Within BI-RADS 3–4 subgroups with pathological results, AIS downgraded 83.1% of false-positives into benign groups, and upgraded 54.1% of false-negatives into malignant groups. AIS also successfully assisted radiologists in identifying 7 out of 43 malignancies initially diagnosed with BI-RADS 0, with a specificity of 96.7%. In the counterbalance-designed AI-assisted study, the average AUC across ten readers significantly improved with AIS assistance (p = 0.001). Conclusion AIS can accurately detect and diagnose breast cancer on mammography and further serve as a supportive tool for BI-RADS categorization. Critical relevance statement An AI risk assessment tool employing deep learning algorithms was developed and validated for enhancing breast cancer diagnosis from mammograms, to improve risk stratification accuracy, particularly in patients with dense breasts, and serve as a decision support aid for radiologists. Key Points The false positive and negative rates of mammography diagnosis remain high. The AIS can yield a high AUC for malignancy diagnosis. The AIS is important in stratifying BI-RADS categorization. Graphical Abstracthttps://doi.org/10.1186/s13244-025-01983-xArtificial intelligenceBreast neoplasmsDeep learningDiagnosisMammography |
| spellingShingle | Hongna Tan Qingxia Wu Yaping Wu Bingjie Zheng Bo Wang Yan Chen Lijuan Du Jing Zhou Fangfang Fu Huihui Guo Cong Fu Lun Ma Pei Dong Zhong Xue Dinggang Shen Meiyun Wang Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks Insights into Imaging Artificial intelligence Breast neoplasms Deep learning Diagnosis Mammography |
| title | Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks |
| title_full | Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks |
| title_fullStr | Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks |
| title_full_unstemmed | Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks |
| title_short | Mammography-based artificial intelligence for breast cancer detection, diagnosis, and BI-RADS categorization using multi-view and multi-level convolutional neural networks |
| title_sort | mammography based artificial intelligence for breast cancer detection diagnosis and bi rads categorization using multi view and multi level convolutional neural networks |
| topic | Artificial intelligence Breast neoplasms Deep learning Diagnosis Mammography |
| url | https://doi.org/10.1186/s13244-025-01983-x |
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