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系统分析乳腺癌形态学揭示了与生存相关的基质特征。

Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.

机构信息

Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.

出版信息

Sci Transl Med. 2011 Nov 9;3(108):108ra113. doi: 10.1126/scitranslmed.3002564.

Abstract

The morphological interpretation of histologic sections forms the basis of diagnosis and prognostication for cancer. In the diagnosis of carcinomas, pathologists perform a semiquantitative analysis of a small set of morphological features to determine the cancer's histologic grade. Physicians use histologic grade to inform their assessment of a carcinoma's aggressiveness and a patient's prognosis. Nevertheless, the determination of grade in breast cancer examines only a small set of morphological features of breast cancer epithelial cells, which has been largely unchanged since the 1920s. A comprehensive analysis of automatically quantitated morphological features could identify characteristics of prognostic relevance and provide an accurate and reproducible means for assessing prognosis from microscopic image data. We developed the C-Path (Computational Pathologist) system to measure a rich quantitative feature set from the breast cancer epithelium and stroma (6642 features), including both standard morphometric descriptors of image objects and higher-level contextual, relational, and global image features. These measurements were used to construct a prognostic model. We applied the C-Path system to microscopic images from two independent cohorts of breast cancer patients [from the Netherlands Cancer Institute (NKI) cohort, n = 248, and the Vancouver General Hospital (VGH) cohort, n = 328]. The prognostic model score generated by our system was strongly associated with overall survival in both the NKI and the VGH cohorts (both log-rank P ≤ 0.001). This association was independent of clinical, pathological, and molecular factors. Three stromal features were significantly associated with survival, and this association was stronger than the association of survival with epithelial characteristics in the model. These findings implicate stromal morphologic structure as a previously unrecognized prognostic determinant for breast cancer.

摘要

组织切片的形态学解释是癌症诊断和预后的基础。在诊断癌时,病理学家对一小部分形态特征进行半定量分析,以确定癌症的组织学分级。医生使用组织学分级来评估癌的侵袭性和患者的预后。然而,乳腺癌的分级仅检查乳腺癌上皮细胞的一小部分形态特征,自 20 世纪 20 年代以来基本没有改变。对自动定量形态特征的全面分析可以确定具有预后相关性的特征,并为从显微镜图像数据评估预后提供准确且可重复的方法。我们开发了 C-Path(计算病理学家)系统,从乳腺癌上皮和基质(6642 个特征)中测量丰富的定量特征集,包括图像对象的标准形态计量描述符和更高层次的上下文、关系和全局图像特征。这些测量值用于构建预后模型。我们将 C-Path 系统应用于来自两个独立乳腺癌患者队列的显微镜图像[荷兰癌症研究所(NKI)队列,n=248 和温哥华总医院(VGH)队列,n=328]。我们系统生成的预后模型评分与 NKI 和 VGH 队列的总生存率均显著相关(均对数秩 P≤0.001)。这种相关性独立于临床、病理和分子因素。三个基质特征与生存显著相关,这种相关性比模型中上皮特征与生存的相关性更强。这些发现表明,基质形态结构是乳腺癌以前未被认识的预后决定因素。

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