Ultrasound-based machine learning models for predicting response to neoadjuvant chemotherapy in breast cancer: A meta-analysis

Abstract

Background and aims: Breast cancer remains the most common cancer among women globally, with neoadjuvant chemotherapy (NAC) serving as a critical pre-surgical intervention. Ultrasound-based radiomics and machine learning (ML) models offer potential for early prediction of NAC response, aiding personalized treatment strategies. This study systematically reviews the efficacy of ultrasound-based ML models in predicting NAC response in breast cancer patients. Methods: We conducted a systematic review and meta-analysis following PRISMA-DTA guidelines, searching PubMed, Scopus, Web of Science, and Embase up to August 30, 2023. Studies developing ultrasound-based radiomics or deep learning (DL) models to predict NAC response were include. Models for complete and partial response were analyzed separately. Results: Twenty-two studies were included. For models predicting complete response, pooled sensitivity, specificity, and AUC were 85.1 (95 CI: 79.2-89.6 ), 85.8 (95 CI: 76.7-91.8 ), and 86 (95 CI: 82 - 94 ), respectively for internal validation and 82.9 (95 CI: 76.2 - 88.1 ), 89.4 (95 CI: 84.7 -92.9 ), and 93 (95 CI: 82 -94 ), respectively for external validation. For partial response, analysis could only be performed on internal validation and the pooled sensitivity was 87.5 (95 CI: 85.1-89.6 ) with pooled specificity of 82.3 (95 CI: 75.6-87.5 ), and pooled AUC of 88 (95 CI: 85 -92 ). Conclusion: Ultrasound-based ML models show strong potential for predicting NAC response in breast cancer, with delta radiomics enhancing predictive accuracy. Further research is needed to develop clinically generalizable models.