The development and validation of the Virtual Tissue Matrix, a software application that facilitates the review of tissue microarrays on line

BackgroundThe Tissue Microarray (TMA) facilitates high-throughput analysis of hundreds of tissue specimens simultaneously. However, bottlenecks in the storage and manipulation of the data generated from TMA reviews have become apparent. A number of software applications have been developed to assist in image and data management; however no solution currently facilitates the easy online review, scoring and subsequent storage of images and data associated with TMA experimentation.ResultsThis paper describes the design, development and validation of the Virtual Tissue Matrix (VTM). Through an intuitive HTML driven user interface, the VTM provides digital/virtual slide based images of each TMA core and a means to record observations on each TMA spot. Data generated from a TMA review is stored in an associated relational database, which facilitates the use of flexible scoring forms. The system allows multiple users to record their interpretation of each TMA spot for any parameters assessed. Images generated for the VTM were captured using a standard background lighting intensity and corrective algorithms were applied to each image to eliminate any background lighting hue inconsistencies or vignetting.Validation of the VTM involved examination of inter-and intra-observer variability between microscope and digital TMA reviews. Six bladder TMAs were immunohistochemically stained for E-Cadherin, β-Catenin and PhosphoMet and were assessed by two reviewers for the amount of core and tumour present, the amount and intensity of membrane, cytoplasmic and nuclear staining.ConclusionResults show that digital VTM images are representative of the original tissue viewed with a microscope. There were equivalent levels of inter-and intra-observer agreement for five out of the eight parameters assessed. Results also suggest that digital reviews may correct potential problems experienced when reviewing TMAs using a microscope, for example, removal of background lighting variance and tint, and potential disorientation of the reviewer, which may have resulted in the discrepancies evident in the remaining three parameters.

[1]  J. Kononen,et al.  Tissue microarrays for high-throughput molecular profiling of tumor specimens , 1998, Nature Medicine.

[2]  J. Seward,et al.  The transition from an analog to a digital echocardiography laboratory: the Mayo experience. , 2004, Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography.

[3]  David Delgado-Gómez,et al.  Collecting highly reproducible images to support dermatological medical diagnosis , 2006, Image Vis. Comput..

[4]  M. Rubin,et al.  Relational database structure to manage high-density tissue microarray data and images for pathology studies focusing on clinical outcome: the prostate specialized program of research excellence model. , 2001, The American journal of pathology.

[5]  Yukako Yagi,et al.  Use of whole slide imaging in surgical pathology quality assurance: design and pilot validation studies. , 2006, Human pathology.

[6]  Hitoshi Ishii,et al.  Development of a high speed imaging microscope and new software for nuclear track detector analysis , 2005 .

[7]  E. Moran,et al.  A new mdr-1 encoded P-170 specific monoclonal antibody: (6/1C) on paraffin wax embedded tissue without pretreatment of sections. , 1997, Journal of clinical pathology.

[8]  D. Rimm,et al.  Automated subcellular localization and quantification of protein expression in tissue microarrays , 2002, Nature Medicine.

[9]  Dan J. Johnston,et al.  Development and preliminary evaluation of the VPS ReplaySuite: a virtual double-headed microscope for pathology , 2005, BMC Medical Informatics Decis. Mak..

[10]  Ash A. Alizadeh,et al.  Software tools for high-throughput analysis and archiving of immunohistochemistry staining data obtained with tissue microarrays. , 2002, The American journal of pathology.

[11]  T. Stephenson,et al.  HER2 amplification status in breast cancer: a comparison between immunohistochemical staining and fluorescence in situ hybridisation using manual and automated quantitative image analysis scoring techniques , 2005, Journal of Clinical Pathology.

[12]  I. Deary,et al.  Retinal image analysis: Concepts, applications and potential , 2006, Progress in Retinal and Eye Research.

[13]  John R Davis,et al.  An array microscope for ultrarapid virtual slide processing and telepathology. Design, fabrication, and validation study. , 2004, Human pathology.

[14]  W. Dinjens,et al.  E‐cadherin—catenin cell—cell adhesion complex and human cancer , 2000, The British journal of surgery.

[15]  N. Sneige,et al.  HER-2/neu gene amplification compared with HER-2/neu protein overexpression and interobserver reproducibility in invasive breast carcinoma. , 2000, American journal of clinical pathology.

[16]  John McCafferty,et al.  Expression profiling by high-throughput immunohistochemistry. , 2004, Journal of immunological methods.

[17]  G. Parmigiani,et al.  Web-based tissue microarray image data analysis: initial validation testing through prostate cancer Gleason grading. , 2001, Human pathology.

[18]  M. Bonnefoi,et al.  Digital Microscopy Imaging and New Approaches in Toxicologic Pathology , 2004, Toxicologic pathology.

[19]  Peer Stelldinger,et al.  Connectivity preserving digitization of blurred binary images in 2D and 3D , 2006, Comput. Graph..

[20]  David R. Westhead,et al.  TmaDB: a repository for tissue microarray data , 2005, BMC Bioinformatics.

[21]  Vincenzo Della Mea,et al.  A pixel-based autofocusing technique for digital histologic and cytologic slides. , 2005, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.

[22]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.