Artificial intelligence for breast ultrasound: An adjunct tool to reduce excessive lesion biopsy. Issue 138 (May 2021)

• Record Type: Journal Article
• Authors: Wang, Xin-Yi; Cui, Li-Gang; Feng, Jie; Chen, Wen
• Is Part Of: European journal of radiology. Issue 138(2021)
• Publication Date: 2021-05
• Subjects: AI Artificial intelligence -- BI-RADS Breast Imaging-Reporting and Data System -- US Ultrasound; Breast neoplasms -- Ultrasonography -- Artificial intelligence -- S-Detect -- Biopsy; Medical radiology -- Periodicals; Radiology -- Periodicals; Radiologie médicale -- Périodiques; Medical radiology; Periodicals; 616.075705
• DOI: 10.1016/j.ejrad.2021.109624
• Languages: English
• ISSNs: 0720-048X

Abstract

Highlights: Artificial intelligence (AI) system helps to reduce unnecessary lesion biopsies. BI-RADS category 4A lesions are hypothetically downgraded to category 3. Two stratifications based on AI results are established for downgrading. It's recommended to downgrade when AI judges both orthogonal sections are benign. 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.