Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis

Abstract

Background Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. Objective This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. Methods A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. Results Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. Conclusion Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers’ status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

Application of AI in detection of breast cancer with laboratory results monitoring

“…AI can also play a significant role in interpreting the clinical significance of biomarkers and tumor markers by analyzing their associations with patient outcomes and treatment responses. This information can help healthcare providers in predicting disease progression, monitoring treatment efficacy, and adjusting therapeutic strategies over time [42,43]. AI has shown promising potential in the detection of human epidermal growth factor receptor 2 (HER2), a protein overexpressed in some breast cancers.…”