Defining the Clinical Indication for Real-Time Artificial Intelligence-Assisted Ultrasound in Breast Lesions with Low Suspicion of Malignancy.
Authors
Affiliations (5)
Affiliations (5)
- Department of Surgery, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
- Department of Radiology, CHA Gangnam Medical Center, CHA University, Seoul, Republic of Korea.
- Department of Radiology, School of Medicine, Kyungpook National University, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea.
- School of Computer Science and Engineering, Kyungpook National University, Daegu, Republic of Korea.
- Department of Surgery, CHA Gangnam Medical Center, CHA University, Seoul, Republic of Korea.
Abstract
To evaluate whether real-time artificial intelligence (AI) breast ultrasound (US) can identify a subgroup of breast imaging reporting and data system (BI-RADS) category 4A lesions without suspicious mammographic (MMG) findings in whom unnecessary biopsy may be safely avoided. This retrospective analysis of single-center biopsy registry included 641 consecutive US BI-RADS 4A lesions that underwent vacuum-assisted breast biopsy. Before conducting biopsy, the lesions were reassessed using a real-time AI US system (CadAI-B®), and MMG findings were classified as suspicious or non-suspicious based on formal reports. Seven prespecified biopsy-decision strategies were evaluated, including 3 AI-only strategies, 3 mammography-gated AI strategies, and the current standard strategy of biopsy all BI-RADS 4A lesions. The primary outcomes were biopsy reduction rate, missed cancers, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, with final pathology as the reference standard. Among 641 cases of US BI-RADS 4A lesions, 26 cases of malignancies (4.1%) were identified, including 14 cases of ductal carcinoma in situ (DCIS), 11 cases of invasive ductal carcinomas, and 1 case of apocrine carcinoma. AI reassigned 289 cases of lesions (45.1%) to BI-RADS categories 1-3. AI-only biopsy-deferral strategies missed 30.8% and 38.5% of malignancies when biopsy was deferred for AI BI-RADS ≤2 and ≤3 lesions, respectively, with DCIS accounting for most missed cancers. In contrast, among lesions without suspicious MMG findings, no malignancies were observed in AI BI-RADS 1-3 lesions. The sequential strategy of deferring biopsy only for lesions with AI BI-RADS ≤3 and non-suspicious MMG findings reduced biopsies by 43.5% (279/641) while maintaining 100.0% sensitivity (95% CI, 87.1-100.0%) and increasing specificity to 45.4% (95% CI, 41.4-49.4%). Real-time AI breast US alone could not safely support biopsy deferral in US BI-RADS 4A lesions. However, after suspicious MMG findings had been excluded, AI identified a low-risk subgroup in whom unnecessary biopsy could be substantially reduced without compromising cancer detection. These findings suggest that the clinical utility of real-time AI breast US lies in patients without suspicious MMG findings. Prospective multicenter validation is warranted before clinical implementation.