Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+Breast Cancer Histopathology
Computerized Image-Based Detection and Grading of Lymphocytic Infiltration in HER2+Breast Cancer Histopathology
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DOI：
10.1109/tbme.2009.2035305
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发表时间：
2010-03-01
影响因子：
4.6
通讯作者：
Madabhushi, Anant
中科院分区：
文献类型：
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作者：
Basavanhally, Ajay Nagesh;Ganesan, Shridar;Madabhushi, Anant
在乳腺癌（BC）组织病理学中识别相应分子变化的表型变化在预测疾病结局方面具有重要的临床意义。一个这样的例子是在组织病理学中存在淋巴细胞浸润（LI），其与HER 2 + BC患者中的淋巴结转移和远处复发相关。在本文中，我们提出了一种计算机辅助诊断（CADx）计划，自动检测和分级LI的程度在数字化HER 2 + BC组织病理学。淋巴细胞首先通过区域生长和马尔可夫随机场算法的组合自动检测。使用检测到的单个淋巴细胞的中心作为顶点，构建三个图（Voronoi图、Delaunay三角剖分和最小生成树），并且从每个样本中提取总共50个描述淋巴细胞排列的图像衍生特征。一个非线性降维方案，图嵌入（GE），然后使用到一个减少的3-D嵌入空间的高维特征向量投影。一个支持向量机分类器被用来区分样本与高和低LI的降维嵌入空间。本研究共考虑了12例患者的41张HER 2+苏木精-伊红染色图像。对于超过100个三重交叉验证试验，架构特征集成功区分了高和低LI水平的样品，分类准确率大于90%。使用流行的无监督Varma-Zisserman基于文本的分类方案进行比较，其分类准确率仅为60%。此外，通过GE将所有41个组织样本的50个图像衍生特征投影到降维空间中，可以可视化光滑歧管，显示低、中和高水平LI之间的连续性。由于已知BC活检标本中LI的程度是一个预后指标，因此我们的CADx方案可能有助于临床医生确定疾病结局，并使他们能够为HER 2 + BC患者提供更好的治疗建议。
The identification of phenotypic changes in breast cancer (BC) histopathology on account of corresponding molecular changes is of significant clinical importance in predicting disease outcome. One such example is the presence of lymphocytic infiltration (LI) in histopathology, which has been correlated with nodal metastasis and distant recurrence in HER2+ BC patients. In this paper, we present a computer-aided diagnosis (CADx) scheme to automatically detect and grade the extent of LI in digitized HER2+ BC histopathology. Lymphocytes are first automatically detected by a combination of region growing and Markov random field algorithms. Using the centers of individual detected lymphocytes as vertices, three graphs (Voronoi diagram, Delaunay triangulation, and minimum spanning tree) are constructed and a total of 50 image-derived features describing the arrangement of the lymphocytes are extracted from each sample. A nonlinear dimensionality reduction scheme, graph embedding (GE), is then used to project the high-dimensional feature vector into a reduced 3-D embedding space. A support vector machine classifier is used to discriminate samples with high and low LI in the reduced dimensional embedding space. A total of 41 HER2+ hematoxylin-and-eosin-stained images obtained from 12 patients were considered in this study. For more than 100 three-fold cross-validation trials, the architectural feature set successfully distinguished samples of high and low LI levels with a classification accuracy greater than 90%. The popular unsupervised Varma-Zisserman texton-based classification scheme was used for comparison and yielded a classification accuracy of only 60%. Additionally, the projection of the 50 image-derived features for all 41 tissue samples into a reduced dimensional space via GE allowed for the visualization of a smooth manifold that revealed a continuum between low, intermediate, and high levels of LI. Since it is known that extent of LI in BC biopsy specimens is a prognostic indicator, our CADx scheme will potentially help clinicians determine disease outcome and allow them to make better therapy recommendations for patients with HER2+ BC.