Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using Multiparametric MRI-Based Radiomics and Machine Learning: A Systematic Review and Meta-Analysis of 1,469 Patients.

Breast cancer, a heterogeneous malignancy, complicates predicting neoadjuvant chemotherapy (NAC) response, crucial for personalized treatment. Multiparametric magnetic resonance imaging (mpMRI)-based radiomics, combined with machine learning (ML), uses quantitative imaging features to enhance prediction accuracy non-invasively. This systematic review and meta-analysis evaluates mpMRI-based radiomics and ML for predicting NAC response in breast cancer, focusing on predictive performance, radiomic features, ML algorithms, and methodological limitations. Registered on PROSPERO (CRD420251109403) and following PRISMA guidelines, we searched PubMed, Embase, Cochrane Library, Scopus, Web of Science, and IEEE Xplore for studies using mpMRI-based radiomics and ML to predict NAC response in adult breast cancer patients. Eligible studies included mpMRI sequences (e.g., T1-weighted, T2-weighted, DWI, DCE) and ML techniques. We extracted data on study characteristics, radiomic features, ML algorithms, and predictive performance (e.g., AUC, sensitivity, specificity). Quality was assessed using QUADAS-2 and Radiomics Quality Score. A random-effects meta-analysis pooled AUCs for pathological complete response (pCR) prediction. Eight studies (six retrospective, two prospective,