Hendrik Schäfer1, Tim Schäfer1, Jörg Ackermann1, Norbert Dichter1, Claudia Döring2, Sylvia Hartmann2, Martin-Leo Hansmann2, Ina Koch1. 1. Department of Molecular Bioinformatics, Institute of Computer Science, Cluster of Excellence Macromolecular Complexes, Johann Wolfgang Goethe-University Frankfurt am Main, Robert-Mayer-Str. 11-15, 60325 Frankfurt am Main and. 2. Dr. Senckenbergisches Institut für Pathologie, Universitätsklinikum Frankfurt, Theodor-Stern-Kai 7, 60590 Frankfurt am Main, Germany.
Abstract
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.
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.
Authors: Alexander Schmitz; Sabine C Fischer; Christian Mattheyer; Francesco Pampaloni; Ernst H K Stelzer Journal: Sci Rep Date: 2017-03-03 Impact factor: 4.379
Authors: Katharina Hötte; Michael Koch; Lotta Hof; Marcel Tuppi; Till Moreth; Monique M A Verstegen; Luc J W van der Laan; Ernst H K Stelzer; Francesco Pampaloni Journal: Sci Rep Date: 2019-11-21 Impact factor: 4.379