影像组学在乳腺癌分子分型及肿瘤微环境研究中的应用

Research progress of radiomics in molecular typing and tumor microenvironment of breast cancer

摘要:

摘 要：乳腺癌发病率居全球女性恶性肿瘤之首。随着科技进步，乳腺疾病诊断技术也从传统阅片诊断向人工智能 辅助诊断的方向发展，包括影像组学、机器学习和深度学习等技术。影像组学是一种综合利用多模态医学影像数据的定 量分析方法，旨在提取和分析影像中的大量特征，并将其与临床、病理、分子等数据关联，以实现个体化的疾病诊断、预测 和治疗策略的制定。该文旨在综述影像组学在乳腺癌研究中的进展，特别关注分子分型的识别和肿瘤微环境的探索。

Abstract:

Abstract: The incidence of breast cancer is the highest among female malignant tumors worldwide. With the development of science and technology, the diagnostic methods for breast diseases have evolved from traditional reading diagnosis to artificial intelligence-assisted diagnosis. This includes the utilization of radiomics, machine learning, and deep learning technologies. Radiomics is a quantitative analysis technique that comprehensively uses multimodal medical imaging data. Its objective is to extract and analyze a multitude of features in images and correlate them with clinical, pathological, molecular, and other data. This correlation allows for personalized disease diagnosis, prediction, and treatment strategy formulation. This article aims to review the progress of radiomics in breast cancer research, with a specific focus on the identification of molecular subtypes and the exploration of the tumor microenvironment.