Molecular Bioinformatics, Institute of Computer Science, Johann Wolfgang Goethe-University Frankfurt am Main, Frankfurt am Main, Germany.
Comput Biol Chem. 2013 Oct;46:1-7. doi: 10.1016/j.compbiolchem.2013.04.003. Epub 2013 May 10.
Hodgkin lymphoma (HL) is a special type of B cell lymphoma, arising from germinal center B-cells. Morphological and immunohistochemical features of HL as well as the spatial distribution of malignant cells differ from other lymphoma and cancer types. Sophisticated protocols for immunostaining and the acquisition of high-resolution images become routine in pathological labs. Large and daily growing databases of high-resolution digital images are currently emerging. A systematic tissue image analysis and computer-aided exploration may provide new insights into HL pathology. The automated analysis of high resolution images, however, is a hard task in terms of required computing time and memory. Special concepts and pipelines for analyzing high-resolution images can boost the exploration of image databases. In this paper, we report an analysis of digital color images recorded in high-resolution of HL tissue slides. Applying a protocol of CD30 immunostaining to identify malignant cells, we implement a pipeline to handle and explore image data of stained HL tissue images. To the best of our knowledge, this is the first systematic application of image analysis to HL tissue slides. To illustrate the concept and methods we analyze images of two different HL types, nodular sclerosis and mixed cellularity as the most common forms and reactive lymphoid tissue for comparison. We implemented a pipeline which is adapted to the special requirements of whole slide images of HL tissue and identifies relevant regions that contain malignant cells. Using a preprocessing approach, we separate the relevant tissue region from the background. We assign pixels in the images to one of the six predefined classes: Hematoxylin(+), CD30(+), Nonspecific red, Unstained, Background, and Low intensity, applying a supervised recognition method. Local areas with pixels assigned to the class CD30(+) identify regions of interest. As expected, an increased amount of CD30(+) pixels is a characteristic feature of nodular sclerosis, and the non-lymphoma cases show a characteristically low amount of CD30(+) stain. Images of mixed cellularity samples include cases of high CD30(+) coloring as well as cases of low CD30(+) coloring.
霍奇金淋巴瘤 (HL) 是一种特殊的 B 细胞淋巴瘤，起源于生发中心 B 细胞。HL 的形态学和免疫组织化学特征以及恶性细胞的空间分布与其他淋巴瘤和癌症类型不同。免疫染色的复杂方案和高分辨率图像的获取已成为病理实验室的常规操作。目前，大量高分辨率数字图像的大型且不断增长的数据库正在涌现。对组织图像进行系统分析和计算机辅助探索可能会为 HL 病理学提供新的见解。然而，高分辨率图像的自动分析在计算时间和内存方面都是一项艰巨的任务。用于分析高分辨率图像的特殊概念和流程可以促进对图像数据库的探索。在本文中，我们报告了对 HL 组织切片的高分辨率数字彩色图像的分析。应用 CD30 免疫染色方案来识别恶性细胞，我们实现了一个处理和探索染色 HL 组织图像的图像数据的流程。据我们所知，这是首次对 HL 组织切片进行图像分析的系统应用。为了说明概念和方法，我们分析了两种不同 HL 类型的图像，即结节性硬化症和混合细胞性作为最常见的形式以及反应性淋巴组织进行比较。我们实现了一个适用于 HL 组织全切片图像特殊要求的流程，并识别包含恶性细胞的相关区域。使用预处理方法，我们将相关组织区域与背景分离。我们将图像中的像素分配到六个预定义类别之一：苏木精 (+)、CD30(+)、非特异性红色、未染色、背景和低强度，应用有监督的识别方法。分配给 CD30(+)类别的像素的局部区域标识感兴趣的区域。正如预期的那样，结节性硬化症的特征是 CD30(+)像素数量增加，而非淋巴瘤病例的 CD30(+)染色特征是数量低。混合细胞性样本的图像包括 CD30(+)染色程度高的病例和 CD30(+)染色程度低的病例。
