March 14, 2026AI0 citationsOpen Access

Artificial Intelligence-Aided Detection of Breast Cancer Using Elastography: A Meta-Analysis of Diagnostic Test Accuracy

IEIbrahim ElmakatyHamad General Hospital RARuba AbdoHamad Medical Corporation AOAmr OudaHamad Medical Corporation

Key Points

This meta-analysis evaluates the diagnostic accuracy of AI-assisted ultrasound elastography for breast cancer detection.
Systematic review of observational studies from multiple databases
Included studies used AI-aided ultrasound elastography compared to histopathology
Extracted binary diagnostic accuracy data for pooled outcomes
Overall sensitivity was 90.3% and specificity 88.0%
Positive likelihood ratio was 7.5 and negative likelihood ratio 0.110
Substantial heterogeneity observed with I2 = 78.0%

Abstract

Breast cancer (BC) remains a major global health burden, consistently standing as the foremost contributor to cancer-related illness and death among women across the world. This meta-analysis aimed to evaluate the diagnostic accuracy of AI-assisted ultrasound elastography (UE) for BC detection by considering various factors, such as AI models, segmentation, cross-validation, data augmentation, the evaluation phase, and the addition of conventional ultrasound. PubMed, Cumulative Index to Nursing and Allied Health Literature, Embase, Scopus, and Web of Science were searched from inception to 22 June 2025, for observational studies using any AI-aided UE modality in BC classification compared to histopathology. We extracted binary diagnostic accuracy data and employed the split component synthesis method for pooled outcomes. Out of 501 identified records, 39 studies (6191 samples) were included in the meta-analysis. The overall diagnostic performance showed 90.3% sensitivity (95% confidence interval CI 86.4–93.1%), 88.0% specificity (95% CI 83.6%–91.4), a positive likelihood ratio of 7.5 (95% CI 5.4–10.5), a negative likelihood ratio of 0.110 (95% CI 0.078–0.156), and a diagnostic odds ratio of 68.3 (95% CI 42.3–110.1). Heterogeneity was substantial (I2 = 78.0%), and the funnel plot demonstrated mild positive asymmetry. Subgroup analyses indicated improved diagnostic performance in studies that employed automatic or no segmentation, cross-validation, data augmentation, retrospective designs, models evaluated during the training phase, classical machine learning approaches, and the combination of B-mode ultrasound with elastography. Despite the presence of heterogeneity and the possibility of overestimation, AI-aided UE demonstrated superior diagnostic performance compared to UE alone. Researchers should consider adopting automatic segmentation, cross-validation, augmentation, and combining UE with conventional ultrasound. Our meta-analysis also explored the potential integration of AI-aided UE into breast cancer screening practices.

Ask AI

Helpful

Bookmark

View Full Paper