Early Identification of Pathologic Complete Response to Neoadjuvant Chemotherapy Using Multiphase DCE-MRI by Siamese Network in Breast Cancer: A Longitudinal Multicenter Study

MRI-based artificial intelligence models for post-neoadjuvant surgery personalization in breast cancer: a narrative review of evidence from Western Pacific

Breast magnetic resonance imaging (MRI) is the most sensitive imaging method for diagnosing breast cancer and assessing treatment response. Artificial intelligence (AI) and radiomics offer new opportunities to identify patterns in imaging data, supporting personalized post-neoadjuvant surgical decisions. This paper reviewed breast MRI-based AI models for predicting outcomes after neoadjuvant therapy, with a focus on evidence from the Western Pacific region, to evaluate the quality of existing models, discuss their inherent limitations, and outline potential future directions. A literature search in MEDLINE, EMBASE, and Web of Science identified 51 relevant studies in the region, with the majority conducted in China, followed by South Korea and Japan. Most studies focused on predicting pathologic complete response (pCR), with a median sample size of 152 and largely retrospective single-center designs. Model performance was commonly assessed using validation sets, with pooled sensitivity and specificity for pCR prediction showing promising results. Models incorporating multitemporal MRI features were associated with improved accuracy. While MRI-based AI models show potential for guiding surgical planning, improved methodological quality and algorithmic explainability are needed to facilitate clinical translation.

Longitudinal MRI-Driven Multi-Modality Approach for Predicting Pathological Complete Response and B Cell Infiltration in Breast Cancer

Accurately predicting pathological complete response (pCR) to neoadjuvant treatment (NAT) in breast cancer remains challenging due to tumor heterogeneity. This study enrolled 2279 patients across 12 centers and develops a novel multi-modality model integrating longitudinal magnetic resonance imaging (MRI) spatial habitat radiomics, transcriptomics, and single-cell RNA sequencing for predicting pCR. By analyzing tumor subregions on multi-timepoint MRI, the model captures dynamic intra-tumoral heterogeneity during NAT. It shows superior performance over traditional radiomics, with areas under the curve of 0.863, 0.813, and 0.888 in the external validation, immunotherapy, and multi-omics cohorts, respectively. Subgroup analysis shows its robustness across varying molecular subtypes and clinical stages. Transcriptomic and single-cell RNA sequencing analysis reveals that high model scores correlate with increased immune activity, notably elevated B cell infiltration, indicating the biological basis of the imaging model. The integration of imaging and molecular data demonstrates promise in spatial habitat radiomics to monitor dynamic changes in tumor heterogeneity during NAT. In clinical practice, this study provides a noninvasive tool to accurately predict pCR, with the potential to guide treatment planning and improve breast-conserving surgery rates. Despite promising results, the model requires prospective validation to confirm its utility across diverse patient populations and clinical settings.