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Abstract：
Breast cancer, as one of the most common malignancies in women, exhibits complex and heterogeneous pathological characteristics across different subtypes. Triple-negative breast cancer (TNBC) and HER2-positive breast cancer are two common and highly invasive subtypes within breast cancer. The stability of the breast microbiota is closely intertwined with the immune environment, and immunotherapy is a common approach for treating breast cancer.Tertiary lymphoid structures (TLSs), recently discovered immune cell aggregates surrounding breast cancer, resemble secondary lymphoid organs (SLOs) and are associated with the prognosis and survival of some breast cancer patients, offering new avenues for immunotherapy. Machine learning, as a form of artificial intelligence, has increasingly been used for detecting biomarkers and constructing tumor prognosis models. This article systematically reviews the latest research progress on TLSs in breast cancer and the application of machine learning in the detection of TLSs and the study of breast cancer prognosis. The insights provided contribute valuable perspectives for further exploring the biological differences among different subtypes of breast cancer and formulating personalized treatment strategies.
摘要：
乳腺癌,作为女性最常见的恶性肿瘤之一，在不同亚型之间表现出复杂和异质的病理特征。三阴性乳腺癌（TNBC）和HER2阳性乳腺癌是乳腺癌中两种常见且高侵袭性的亚型。乳腺微生物群的稳定性与免疫环境紧密交织,免疫疗法是治疗乳腺癌的常用方法。三级淋巴样结构（TLSs），最近发现了围绕乳腺癌的免疫细胞聚集体，类似于次级淋巴器官（SLO），与一些乳腺癌患者的预后和生存有关，为免疫疗法提供新的途径。机器学习,作为人工智能的一种形式，已越来越多地用于检测生物标志物和构建肿瘤预后模型。本文系统综述了TLS在乳腺癌中的最新研究进展以及机器学习在TLS检测和乳腺癌预后研究中的应用。这些见解为进一步探索不同亚型乳腺癌之间的生物学差异和制定个性化治疗策略提供了有价值的观点。
