Radiomics has emerged as a promising tool for non-invasive tumour phenotyping in breast cancer, providing valuable insights into tumour heterogeneity, response prediction, and risk stratification. However, traditional radiomic approaches often rely on correlative patterns of image analysis to clinical data and lack direct biological interpretability. Combining information provided by radiomics with genomics or other multi-omics data can be important to personalise diagnostic and therapeutic work up in breast cancer management. This review aims to explore the current progress in integrating radiomics with multi-omics data—genomics and transcriptomics—to establish biologically grounded, multidimensional models for precision management of breast cancer. We will review recent advances in integrative radiomics and radiogenomics, highlight the synergy between imaging and molecular profiling, and discuss emerging machine learning methodologies that facilitate the integration of high-dimensional data. Applications of radiogenomics, including breast cancer subtype and molecular mutation prediction, radiogenomic mapping of the tumour immune microenvironment, and response forecasting to immunotherapy and targeted therapies, as well as lymph nodes involvement, will be evaluated. Challenges in technical limitations including imaging modalities harmonization, interpretability, and advancing machine learning methodologies will be addressed. This review positions integrative radiogenomics as a driving force for next-generation breast cancer care.
影像组学已成为乳腺癌无创肿瘤表型分析的重要工具，为肿瘤异质性评估、疗效预测及风险分层提供了关键信息。然而，传统影像组学方法多依赖影像特征与临床数据的相关性分析，缺乏直接的生物学解释性。将影像组学信息与基因组学等多组学数据整合，对实现乳腺癌个体化诊疗具有重要意义。本文旨在探讨影像组学与基因组学、转录组学等多组学数据融合的最新进展，以构建基于生物学基础的多维模型，推动乳腺癌精准管理。我们将综述整合性影像组学与影像基因组学的最新成果，阐释影像特征与分子谱系的协同作用，并探讨促进高维数据整合的新型机器学习方法。重点评估影像基因组学在乳腺癌亚型分型、分子突变预测、肿瘤免疫微环境图谱构建、免疫治疗与靶向治疗疗效预测以及淋巴结转移评估等方面的应用价值。同时，将针对影像模态标准化、模型可解释性及机器学习方法创新等技术挑战进行深入探讨。本综述认为，整合性影像基因组学将成为推动新一代乳腺癌诊疗发展的核心驱动力。
肿瘤(癌症)患者之家