Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
Mod Pathol. 2010 May;23 Suppl 2:S60-4. doi: 10.1038/modpathol.2010.33.
For many years, patient age, axillary lymph node status, tumor size, histological features (especially histological grade and lymphovascular invasion), hormone receptor status, and HER2 status have been the major factors used to categorize patients with breast cancer in order to assess prognosis and determine the appropriate therapy. These factors are most often viewed in combination to group patients into various risk categories. Although these risk categories are useful for assessing prognosis and risk in groups of patients with breast cancer, their role in determining prognosis and evaluating risk in an individual patient is more limited. Therefore, better methods are required to help assess prognosis and determine the most appropriate treatment for patients on an individual basis. Recently, various molecular techniques, particular gene expression profiling, have been increasingly used to help refine breast cancer classification and to assess prognosis and response to therapy. Although the precise role of these newer techniques in the daily management of patients with breast cancer continues to evolve, it is clear that they have the potential to provide value above and beyond that provided by the traditional clinical and pathological prognostic and predictive factors.
多年来，患者年龄、腋窝淋巴结状态、肿瘤大小、组织学特征（尤其是组织学分级和脉管侵犯）、激素受体状态和 HER2 状态一直是用于对乳腺癌患者进行分类的主要因素，以评估预后并确定适当的治疗方法。这些因素通常结合起来将患者分为不同的风险类别。尽管这些风险类别对于评估乳腺癌患者群体的预后和风险很有用，但它们在个体患者中预测预后和评估风险的作用更为有限。因此，需要更好的方法来帮助个体患者评估预后并确定最合适的治疗方法。最近，各种分子技术，特别是基因表达谱分析，已越来越多地用于帮助细化乳腺癌分类，并评估预后和对治疗的反应。尽管这些新技术在乳腺癌患者的日常管理中的确切作用仍在不断发展，但显然它们有可能提供超越传统临床和病理预后和预测因素的价值。
