Abstract 3449: Is AI-enhanced breast ultrasound ready for breast cancer screening in low-resource environments? A systematic review
Authors: Arianna Bunnell, Dustin Valdez, Fredrik Strand, Yannik Glaser, Peter Sadowski, John A. Shepherd
Journal: Cancer Research (2024)

Abstract Purpose. Screening mammography is unavailable in many low-resource areas. We ask if the state-of-the-art in artificial intelligence (AI)-enhanced breast ultrasound (BUS) is sufficiently accurate to be used for primary breast cancer screening in low-resource regions. Background. Since the 1980s, high-income countries have implemented mammographic screening programs, leading to breast cancer mortality reduction in screened women.1 Mammography is unavailable in many low-resource regions, such as the USAPI. Furthermore, travel difficulties and lack of radiologists hinder implementation. AI combined with portable BUS may address limitations of the high-income paradigm. In this systematic review, we ask if AI-enhanced BUS can detect/segment lesions (Objective 1) and classify lesions as cancerous (Objective 2). Methods. Two reviewers independently assessed articles from 1/1/2016 to 8/6/2023 from PubMed, Google Scholar, and citation searching. Studies which report on AI development and report performance on a patient-wise, held-out test set met the inclusion criteria. Studies were characterized by AI task and clinical application time. Dataset composition and performance were examined via narrative data synthesis. QUADAS-2 bias assessment was performed using criteria for each AI task. Success in (2) is defined by meeting minimum screening performance guidelines.2,3 Results. PubMed yielded 281 studies, Google Scholar yielded 225 studies, and a manual citation search yielded 41 studies. From 382 unique full texts evaluated, 52 articles met all inclusion criteria: 3 frame selection, 2 real-time detection, 2 combination, 14 segmentation-only, and 31 classification-only. Lesion segmentation-only models achieved a 90th percentile Dice similarity coefficient of 0.913 on generally small datasets. The best evidence for lesion cancer classification reported 0.976 area under the curve. All studies faced elevated bias and applicability concerns under QUADAS-2. Conclusion. Reported performance for (1) is insufficient to introduce AI-enhanced BUS for breast cancer screening. Evidence supporting AI-enhanced BUS for (2) is dependent on few studies relying on internal datasets, limiting generalizability. Geographically diverse clinical trials are needed to confirm and improve robustness of performance of AI-enhanced BUS for (1) and (2). References. 1. Marmot MG, et al. The benefits and harms of breast cancer screening: an independent review. British journal of cancer. 2013;108(11):2205-2240. 2. Lehman CD, et al. National Performance Benchmarks for Modern Screening Digital Mammography: Update from the Breast Cancer Surveillance Consortium. Radiology. 2017-04-01 2017;283(1):49-58. doi:10.1148/radiol.2016161174 3. Rosenberg RD, et al. Performance Benchmarks for Screening Mammography. Radiology. 2006-10-01 2006;241(1):55-66. doi:10.1148/radiol.2411051504 Citation Format: Arianna Bunnell, Dustin Valdez, Fredrik Strand, Yannik Glaser, Peter Sadowski, John A. Shepherd. Is AI-enhanced breast ultrasound ready for breast cancer screening in low-resource environments? A systematic review [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3449.