Automated macrophage counting in DLBCL tissue samples: a ROF filter based approach

BackgroundFor analysis of the tumor microenvironment in diffuse large B-cell lymphoma (DLBCL) tissue samples, it is desirable to obtain information about counts and distribution of different macrophage subtypes. Until now, macrophage counts are mostly inferred from gene expression analysis of whole tissue sections, providing only indirect information. Direct analysis of immunohistochemically (IHC) fluorescence stained tissue samples is confronted with several difficulties, e.g. high variability of shape and size of target macrophages and strongly inhomogeneous intensity of staining. Consequently, application of commercial software is largely restricted to very rough analysis modes, and most macrophage counts are still obtained by manual counting in microarrays or high power fields, thus failing to represent the heterogeneity of tumor microenvironment adequately.MethodsWe describe a Rudin-Osher-Fatemi (ROF) filter based segmentation approach for whole tissue samples, combining floating intensity thresholding and rule-based feature detection. Method is validated against manual counts and compared with two commercial software kits (Tissue Studio 64, Definiens AG, and Halo, Indica Labs) and a straightforward machine-learning approach in a set of 50 test images. Further, the novel method and both commercial packages are applied to a set of 44 whole tissue sections. Outputs are compared with gene expression data available for the same tissue samples. Finally, the ROF based method is applied to 44 expert-specified tumor subregions for testing selection and subsampling strategies.ResultsAmong all tested methods, the novel approach is best correlated with manual count (0.9297). Automated detection of evaluation subregions proved to be fully reliable. Comparison with gene expression data obtained for the same tissue samples reveals only moderate to low correlation levels. Subsampling within tumor subregions is possible with results almost identical to full sampling. Mean macrophage size in tumor subregions is 152.5±111.3 μm2.ConclusionsROF based approach is successfully applied to detection of IHC stained macrophages in DLBCL tissue samples. The method competes well with existing commercial software kits. In difference to them, it is fully automated, externally repeatable, independent on training data and completely documented. Comparison with gene expression data indicates that image morphometry constitutes an independent source of information about antibody-polarized macrophage occurence and distribution.

[1]  J. Pollard,et al.  Diverse Functions of Macrophages in Different Tumor Microenvironments. , 2018, Cancer research.

[2]  L. Staudt,et al.  The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. , 2002, The New England journal of medicine.

[3]  R. Gascoyne,et al.  The tumour microenvironment in B cell lymphomas , 2014, Nature Reviews Cancer.

[4]  R. Xiang,et al.  M2 tumour-associated macrophages contribute to tumour progression via legumain remodelling the extracellular matrix in diffuse large B cell lymphoma , 2016, Scientific Reports.

[5]  L. Staudt,et al.  Stromal gene signatures in large-B-cell lymphomas. , 2008, The New England journal of medicine.

[6]  A. Rosenwald,et al.  The reliability of immunohistochemical analysis of the tumor microenvironment in follicular lymphoma: a validation study from the Lunenburg Lymphoma Biomarker Consortium , 2014, Haematologica.

[7]  Marc Lartaud,et al.  Analyzing huge pathology images with open source software , 2013, Diagnostic Pathology.

[8]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

[9]  J. Delabie,et al.  Prognostic influence of macrophages in patients with diffuse large B-cell lymphoma: a correlative study from a Nordic phase II trial , 2015, Haematologica.

[10]  E. Meijering.  Cell Segmentation : 50 Years Down the Road , 2012 .

[11]  Abhishek Dutta,et al.  The VGG Image Annotator (VIA) , 2019, ArXiv.

[12]  Kai Fu,et al.  Determining cell-of-origin subtypes of diffuse large B-cell lymphoma using gene expression in formalin-fixed paraffin-embedded tissue. , 2014, Blood.

[13]  Tony F. Chan,et al.  Image processing and analysis - variational, PDE, wavelet, and stochastic methods , 2005 .

[14]  L. Staudt,et al.  Loss of MHC class II gene and protein expression in diffuse large B-cell lymphoma is related to decreased tumor immunosurveillance and poor patient survival regardless of other prognostic factors: a follow-up study from the Leukemia and Lymphoma Molecular Profiling Project. , 2004, Blood.

[15]  Jeffrey W. Pollard,et al.  Macrophage Diversity Enhances Tumor Progression and Metastasis , 2010, Cell.

[16]  Yi Xia,et al.  High expression of tumor-infiltrating macrophages correlates with poor prognosis in patients with diffuse large B-cell lymphoma , 2012, Medical Oncology.

[17]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Ross B. Girshick,et al.  Mask R-CNN , 2017, 1703.06870.

[19]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[20]  K. Bredies,et al.  Computer-assisted counting of retinal cells by automatic segmentation after TV denoising , 2013, BMC Ophthalmology.

[21]  John W. Eaton,et al.  GNU Octave Manual Version 3 , 2008 .

[22]  S. Swerdlow.  WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues , 2017 .

[23]  R. Spang,et al.  Molecular classification of mature aggressive B-cell lymphoma using digital multiplexed gene expression on formalin-fixed paraffin-embedded biopsy specimens. , 2013, Blood.

[24]  M. West.  Introduction to stereology. , 2012, Cold Spring Harbor protocols.

[25]  G. Steiner,et al.  Automated data acquisition by confocal laser scanning microscopy and image analysis of triple stained immunofluorescent leukocytes in tissue. , 2000, Journal of immunological methods.

[26]  Erik H. W. Meijering,et al.  Cell Segmentation: 50 Years Down the Road [Life Sciences] , 2012, IEEE Signal Processing Magazine.

[27]  D. Ribatti,et al.  Microvascular density, CD68 and tryptase expression in human diffuse large B-cell lymphoma. , 2014, Leukemia research.

[28]  Arkadiusz Gertych,et al.  Spatial Mapping of Myeloid Cells and Macrophages by Multiplexed Tissue Staining , 2018, Front. Immunol..

[29]  R. Advani,et al.  Gene expression-based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma. , 2013, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[30]  M. Salto‐Tellez,et al.  Digital pathology and image analysis in tissue biomarker research. , 2014, Methods.

[31]  R. Spang,et al.  Spindle-Cell Variants of Primary Cutaneous Follicle Center B-Cell Lymphomas Are Germinal Center B-Cell Lymphomas by Gene Expression Profiling Using a Formalin-Fixed Paraffin-Embedded Specimen. , 2017, The Journal of investigative dermatology.

[32]  M. Jensen,et al.  Comparison of Methods for Analyzing Human Adipose Tissue Macrophage Content , 2017, Obesity.

[33]  R. Gascoyne,et al.  Tumor-associated macrophages in diffuse large B-cell lymphoma , 2015, Haematologica.

[34]  M. Calaminici,et al.  Revisiting the immune microenvironment of diffuse large B-cell lymphoma using a tissue microarray and immunohistochemistry: robust semi-automated analysis reveals CD3 and FoxP3 as potential predictors of response to R-CHOP , 2015, Haematologica.

[35]  Ash A. Alizadeh,et al.  Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling , 2000, Nature.