Xin-Yi Wang1, Li-Gang Cui2, Jie Feng3, Wen Chen4. 1. Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China; Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/ Beijing), Breast Center, Peking University Cancer Hospital & Institute, Beijing 100142, China. 2. Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China. Electronic address: cuiligang_bysy@126.com. 3. Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China; Department of Ultrasound, Haicang Hospital, Xiamen 361000, China. 4. Department of Ultrasound, Peking University Third Hospital, Beijing 10091, China.
Abstract
PURPOSE: To determine whether adding an artificial intelligence (AI) system to breast ultrasound (US) can reduce unnecessary biopsies. METHODS: Conventional US and AI analyses were prospectively performed on 173 suspicious breast lesions before US-guided core needle biopsy or vacuum-assisted excision. Conventional US images were retrospectively reviewed according to the BI-RADS 2013 lexicon and categories. Two downgrading stratifications based on AI assessments were manually used to downgrade the BI-RADS category 4A lesions to category 3. Stratification A was used to downgrade if the assessments of both orthogonal sections of a lesion from AI were possibly benign. Stratification B was used to downgrade if the assessment of any of the orthogonal sections was possibly benign. The effects of AI-based diagnosis on lesions to reduce unnecessary biopsy were analyzed using histopathological results as reference standards. RESULTS: Forty-three lesions diagnosed as BI-RADS category 4A by conventional US received AI-based hypothetical downgrading. While downgrading with stratification A, 14 biopsies were correctly avoided. The biopsy rate for BI-RADS category 4A lesions decreased from 100 % to 67.4 % (P < 0.001). While downgrading with stratification B, 27 biopsies could be avoided with two malignancies missed, and the biopsy rate would decrease to 37.2 % (P < 0.05, compared with conventional US and stratification A). CONCLUSION: Adding an AI system to breast US could reduce unnecessary lesion biopsies. Downgrading stratification A was recommended for its lower misdiagnosis rate.
PURPOSE: To determine whether adding an artificial intelligence (AI) system to breast ultrasound (US) can reduce unnecessary biopsies. METHODS: Conventional US and AI analyses were prospectively performed on 173 suspicious breast lesions before US-guided core needle biopsy or vacuum-assisted excision. Conventional US images were retrospectively reviewed according to the BI-RADS 2013 lexicon and categories. Two downgrading stratifications based on AI assessments were manually used to downgrade the BI-RADS category 4A lesions to category 3. Stratification A was used to downgrade if the assessments of both orthogonal sections of a lesion from AI were possibly benign. Stratification B was used to downgrade if the assessment of any of the orthogonal sections was possibly benign. The effects of AI-based diagnosis on lesions to reduce unnecessary biopsy were analyzed using histopathological results as reference standards. RESULTS: Forty-three lesions diagnosed as BI-RADS category 4A by conventional US received AI-based hypothetical downgrading. While downgrading with stratification A, 14 biopsies were correctly avoided. The biopsy rate for BI-RADS category 4A lesions decreased from 100 % to 67.4 % (P < 0.001). While downgrading with stratification B, 27 biopsies could be avoided with two malignancies missed, and the biopsy rate would decrease to 37.2 % (P < 0.05, compared with conventional US and stratification A). CONCLUSION: Adding an AI system to breast US could reduce unnecessary lesion biopsies. Downgrading stratification A was recommended for its lower misdiagnosis rate.
Authors: Qi Wei; Yu-Jing Yan; Ge-Ge Wu; Xi-Rong Ye; Fan Jiang; Jie Liu; Gang Wang; Yi Wang; Juan Song; Zhi-Ping Pan; Jin-Hua Hu; Chao-Ying Jin; Xiang Wang; Christoph F Dietrich; Xin-Wu Cui Journal: Eur Radiol Date: 2022-01-23 Impact factor: 5.315