The role of radiomics in predicting the response to neoadjuvant chemotherapy for breast cancer

作者：Yilin Chen, Ye Jun Qin, Man Yang, Wei Li, Minyi Cheng, Yuhong Huang, Teng Zhu, Kun Wang · 发表于：Cancer Biology and Medicine · 年份：2026 · DOI：10.20892/j.issn.2095-3941.2025.0655 · 被引用次数：2 · 研究领域：Radiomics and Machine Learning in Medical Imaging、Breast Cancer Treatment Studies、MRI in cancer diagnosis

Breast cancer exhibits profound biological and spatial heterogeneity, which contributes to variable responses to neoadjuvant chemotherapy (NAC) and challenges precision treatment planning. Radiomics, an emerging discipline that converts standard medical images into high-dimensional quantitative data, offers a non-invasive and reproducible means to capture tumor phenotype, heterogeneity, and treatment-induced changes. This review provides a comprehensive overview of recent advances in radiomics for breast cancer NAC, emphasizing the roles in predicting a pathologic complete response (pCR), monitoring early therapeutic efficacy, and quantifying intratumoral heterogeneity. Among imaging modalities, magnetic resonance imaging (MRI)-based radiomics, particularly utilizing dynamic contrast-enhanced and diffusion-weighted sequences, demonstrates robust predictive performance for the pCR, with multi-center studies reporting area under the curve (AUC) values >0.80. Longitudinal and delta-radiomics approaches further enhance early response evaluation by tracking temporal alterations in imaging features that precede measurable morphologic regression. Radiomic assessment of tumor heterogeneity, especially in triple-negative breast cancer (TNBC), reveals strong associations with immune infiltration, metabolic reprogramming, and therapeutic resistance, providing mechanistic insight into radiomic biomarkers. Integrative multi-omics frameworks, combining radiomics with genomics, t...