CD30 cell graphs of Hodgkin lymphoma are not scale-free--an image analysis approach.

MOTIVATION: Hodgkin lymphoma (HL) is a type of B-cell lymphoma. To diagnose the subtypes, biopsies are taken and immunostained. The slides are scanned to produce high-resolution digital whole slide images (WSI). Pathologists manually inspect the spatial distribution of cells, but little is known on the statistical properties of cell distributions in WSIs. Such properties would give valuable information for the construction of theoretical models that describe the invasion of malignant cells in the lymph node and the intercellular interactions. RESULTS: In this work, we define and discuss HL cell graphs. We identify CD30(+) cells in HL WSIs, bringing together the fields of digital imaging and network analysis. We define special graphs based on the positions of the immunostained cells. We present an automatic analysis of complete WSIs to determine significant morphological and immunohistochemical features of HL cells and their spatial distribution in the lymph node tissue under three different medical conditions: lymphadenitis (LA) and two types of HL. We analyze the vertex degree distributions of CD30 cell graphs and compare them to a null model. CD30 cell graphs show higher vertex degrees than expected by a random unit disk graph, suggesting clustering of the cells. We found that a gamma distribution is suitable to model the vertex degree distributions of CD30 cell graphs, meaning that they are not scale-free. Moreover, we compare the graphs for LA and two subtypes of HL. LA and classical HL showed different vertex degree distributions. The vertex degree distributions of the two HL subtypes NScHL and mixed cellularity HL (MXcHL) were similar. AVAILABILITY AND IMPLEMENTATION: The CellProfiler pipeline used for cell detection is available at https://sourceforge.net/projects/cellgraphs/. CONTACT: ina.koch@bioinformatik.uni-frankfurt.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

QuateXelero: an accelerated exact network motif detection algorithm.

Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks' structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.

Image database analysis of Hodgkin lymphoma.

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.