Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules

Shu-Yi Lyu; Yan Zhang; Mei-Wu Zhang; Bai-Song Zhang; Li-Bo Gao; Lang-Tao Bai; Jue Wang

doi:10.12998/wjcc.v10.i2.518

Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules

World J Clin Cases. 2022 Jan 14;10(2):518-527. doi: 10.12998/wjcc.v10.i2.518.

Authors

Shu-Yi Lyu¹, Yan Zhang¹, Mei-Wu Zhang¹, Bai-Song Zhang¹, Li-Bo Gao¹, Lang-Tao Bai¹, Jue Wang²

Affiliations

¹ Interventional Therapy Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China.
² Ultrasonography Department, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo 315010, Zhejiang Province, China.

Abstract

Background: The incidence rate of breast cancer has exceeded that of lung cancer, and it has become the most malignant type of cancer in the world. BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.

Aim: To explore the diagnostic value of artificial intelligence (AI) automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.

Methods: A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital, University of Chinese Academy of Sciences. These nodules were classified by ultrasound doctors and the AI-SONIC breast system. The diagnostic values of conventional ultrasound, the AI automatic detection system, conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.

Results: Among the 107 breast nodules, 61 were benign (57.01%), and 46 were malignant (42.99%). The pathology results were considered the gold standard; furthermore, the sensitivity, specificity, accuracy, Youden index, and positive and negative predictive values were 84.78%, 67.21%, 74.77%, 0.5199, 66.10% and 85.42% for conventional ultrasound BI-RADS classification diagnosis, 86.96%, 75.41%, 80.37%, 0.6237, 72.73%, and 88.46% for automatic AI detection, 80.43%, 90.16%, 85.98%, 0.7059, 86.05%, and 85.94% for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%, 67.21%, 78.50%, 0.6069, 68.25%, and 93.18% for adjusted BI-RADS classification, respectively. The biopsy rate, cancer detection rate and malignancy risk were 100%, 42.99% and 0% and 67.29%, 61.11%, and 1.87% before and after BI-RADS adjustment, respectively.

Conclusion: Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules. Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.

Keywords: Artificial intelligence; BI-RADS classification; Breast nodules; Breast tumor.