Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A
Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS ca...
| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-10-01
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| Series: | BMC Cancer |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12885-020-07413-z |
| _version_ | 1818130813171007488 |
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| author | Sihua Niu Jianhua Huang Jia Li Xueling Liu Dan Wang Ruifang Zhang Yingyan Wang Huiming Shen Min Qi Yi Xiao Mengyao Guan Haiyan Liu Diancheng Li Feifei Liu Xiuming Wang Yu Xiong Siqi Gao Xue Wang Jiaan Zhu |
| author_facet | Sihua Niu Jianhua Huang Jia Li Xueling Liu Dan Wang Ruifang Zhang Yingyan Wang Huiming Shen Min Qi Yi Xiao Mengyao Guan Haiyan Liu Diancheng Li Feifei Liu Xiuming Wang Yu Xiong Siqi Gao Xue Wang Jiaan Zhu |
| author_sort | Sihua Niu |
| collection | DOAJ |
| description | Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions. |
| first_indexed | 2024-12-11T08:11:00Z |
| format | Article |
| id | doaj.art-64258fedbc23469a9eff766dc23c4414 |
| institution | Directory Open Access Journal |
| issn | 1471-2407 |
| language | English |
| last_indexed | 2024-12-11T08:11:00Z |
| publishDate | 2020-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj.art-64258fedbc23469a9eff766dc23c44142022-12-22T01:14:52ZengBMCBMC Cancer1471-24072020-10-012011710.1186/s12885-020-07413-zApplication of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4ASihua Niu0Jianhua Huang1Jia Li2Xueling Liu3Dan Wang4Ruifang Zhang5Yingyan Wang6Huiming Shen7Min Qi8Yi Xiao9Mengyao Guan10Haiyan Liu11Diancheng Li12Feifei Liu13Xiuming Wang14Yu Xiong15Siqi Gao16Xue Wang17Jiaan Zhu18Department of Ultrasound, Peking University People’s HospitalSchool of Computer Science and Technology, Harbin Institute of TechnologyDepartment of Ultrasound, Southeast University Zhongda HospitalDepartment of Ultrasound, The First Affiliated Hospital of Guangxi University of Chinese MedicineDepartment of Ultrasound, The First Affiliated Hospital of Guangxi University of Chinese MedicineDepartment of Ultrasound, Zhengzhou University First Affiliated HospitalDepartment of Ultrasound, Southeast University Zhongda HospitalDepartment of Ultrasound, Southeast University Zhongda HospitalDepartment of Ultrasound, Southeast University Zhongda HospitalSchool of Computer Science and Technology, Harbin Institute of TechnologySchool of Computer Science and Technology, Harbin Institute of TechnologyDepartment of Ultrasound, Zhengzhou University First Affiliated HospitalDepartment of Ultrasound, Peking University People’s HospitalDepartment of Ultrasound, Peking University People’s HospitalDepartment of Ultrasound, Peking University People’s HospitalDepartment of Ultrasound, Peking University People’s HospitalDepartment of Ultrasound, Peking University People’s HospitalDepartment of Ultrasound, Peking University People’s HospitalDepartment of Ultrasound, Peking University People’s HospitalAbstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.http://link.springer.com/article/10.1186/s12885-020-07413-zArtificial intelligenceBreastBI-RADS 4ADifferential diagnosis |
| spellingShingle | Sihua Niu Jianhua Huang Jia Li Xueling Liu Dan Wang Ruifang Zhang Yingyan Wang Huiming Shen Min Qi Yi Xiao Mengyao Guan Haiyan Liu Diancheng Li Feifei Liu Xiuming Wang Yu Xiong Siqi Gao Xue Wang Jiaan Zhu Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A BMC Cancer Artificial intelligence Breast BI-RADS 4A Differential diagnosis |
| title | Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A |
| title_full | Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A |
| title_fullStr | Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A |
| title_full_unstemmed | Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A |
| title_short | Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A |
| title_sort | application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of bi rads 4a |
| topic | Artificial intelligence Breast BI-RADS 4A Differential diagnosis |
| url | http://link.springer.com/article/10.1186/s12885-020-07413-z |
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