Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival
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DOI：
10.1126/scitranslmed.3002564
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发表时间：
2011-11-09
影响因子：
17.1
通讯作者：
Koller, Daphne
中科院分区：
文献类型：
--
作者：
Beck, Andrew H.;Sangoi, Ankur R.;Koller, Daphne
组织切片的形态学解释是癌症诊断和鉴别的基础。在癌症的诊断中，病理学家对一小组形态学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告知他们对癌的侵袭性和患者预后的评估。然而，乳腺癌分级的确定仅检查了乳腺癌上皮细胞的一小部分形态学特征，自20世纪20年代以来基本上没有变化。自动定量的形态学特征的综合分析可以识别预后相关性的特征，并提供一种准确和可重复的手段，用于从显微图像数据评估预后。我们开发了C-Path（计算病理学家）系统来测量乳腺癌上皮和间质（6642个特征）的丰富定量特征集，包括图像对象的标准形态描述符和更高级别的上下文，关系和全局图像特征。这些测量用于构建预后模型。我们将C-Path系统应用于两个独立的乳腺癌患者队列的显微镜图像[来自荷兰癌症研究所（NKI）队列，n = 248，和温哥华综合医院（VGH）队列，n = 328]。在NKI和VGH队列中，我们系统生成的预后模型评分与总生存率密切相关（均为对数秩P
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