Histochem Cell Biol (2008) 130:447–463

DOI 10.1007/s00418-008-0480-1

REVIEW

Virtual microscopy as an enabler of automated/quantitative assessment of protein expression in TMAs

Catherine Conway · Lynne Dobson · Anthony O’Grady · Elaine Kay · Sean Costello · Daniel O’Shea

Accepted: 5 July 2008 / Published online: 5 August 2008© Springer-Verlag 2008

Abstract Tissue Microarrays facilitate high-throughputimmuohistochemistry; however, there are key bottlenecksapparent in their analysis, particularly when conductingmicroscope-based manual reviews. Traditionally TissueMicroarray assessments were performed using a micro-scope where results were either transcribed or dictated andsubsequently entered into Xat-Wle spreadsheets. This pro-cess is labour intensive, prone to error and negates theadvantages of the high-throughput Tissue Microarray for-mat. In addition, human interpretations of staining intensityparameters are highly subjective and therefore prone tointer- and intra-observer variability. The advent of VirtualSlides has permitted the review of tissue slides across theInternet. In addition, this new technology enables the crea-tion of software solutions to assist in the manual and auto-mated review of Tissue Microarrays, through the use ofcomputer aided image analysis. There are numerous aca-demically developed and commercially available applica-tions which assist in Tissue Microarray reviews;functionality of these systems range in complexity and

application domains. The review which follows describesthese systems and outlines technical considerations to beassessed when deciding on a Tissue Microarray workXowsolution.

Keywords Image analysis · Tissue Microarrays · Immunohistochemistry · Virtual Slides

Introduction

Immunohistochemistry is a well-established and versatiletechnique which is routinely used in molecular and surgicalpathology (Kononen et al. 1998; Cregger et al. 2006).Immunohistochemistry allows for the identiWcation andlocalisation of cell-bound antigens and can be performed onnumerous cells and tissue preparations (Fejzo and Slamon2001). The technique is widely used due to its relativelylow cost, availability of materials in routine pathologylaboratories and relatively rapid turnaround (Conway et al.2006). The greatest advantage of immunohistochemistry isthat it allows the interpretation of histomorphology to dis-cern the complexity of expression patterns which cannot bedetermined from methods that rely on the extraction of bio-molecules (Hewitt 2006). However, recent advances inmolecular biology have centred on increases in throughputand quantiWcation of biologic phenomena. No longer isexperimental design focused on one gene or one protein,but rather on tens to hundreds of genes, proteins or tissueon analytical platforms (Macbeath 2002). Therefore, theapplication of immunohistochemical analysis on full-facesections as a means of biomarker validation is increasinglybeing replaced with Tissue Microarray analysis.

Tissue Microarrays (TMAs) provide high-throughputhistomorphologic examination of tissue by means of

C. Conway (&) · S. CostelloSlidePath, Dublin, Irelande-mail: catherine.conway@slidepath.com

L. DobsonSchool of Biotechnology, Dublin City University, Dublin, Ireland

A. O’Grady · E. KayDepartment of Histopathology, Beaumont Hospital and Royal College of Surgeons, Dublin, Ireland

D. O’SheaMedical Informatics Group, School of Biotechnology, Dublin City University, Dublin, Ireland

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arranging multiple tissue samples in a uniform structure ona paraYn wax block. Large amounts of tissue samples areanalysed simultaneously based on Xuorescence in situhybridisation (FISH) for genetic rearrangements, RNA insitu hybridisation for genetic expression, or immunohisto-chemistry for protein overexpression (Kononen et al. 1998;Kallioniaemi et al. 2001; Bubendorf et al. 2001). The tech-nology was developed by Kononen et al. (1998) in order tofacilitate gene expression and copy number surveys of largecohorts of tumours. Due to the nature of TMA constructionwhich allows multiple sections to be obtained from a singleTMA block, rapid analysis of hundreds of molecular mark-ers on the same cohort of specimens is possible (Moch et al.2001). TMAs provide substantial value in rapidly transla-ting genomic and proteomic information into clinical appli-cations (Torhorst et al. 2001). When initially created,TMAs were envisioned to make a dramatic impact on basiccancer research and anatomic pathology (Moch et al.2001); in ten years since TMAs inception this hypothesishas been realised through various studies. TMAs havenumerous beneWts over full-face analysis including uniformexperimental conditions, conservation of scarce tissue and areduction in the volume of reagents used (Simon and Sauter2002; Al Kuraya et al. 2004; Milanes-Yearsley et al. 2002;Hoos and Cordon-Cardo 2001).

However, at best, manual immunohistochemical analy-sis of TMAs is a semi-quantitative technique. In addition,large amounts of tissue and data are associated with TMAreviews, and as a result bottlenecks in microscopic analy-sis of TMAs have developed (Conway et al. 2006). Withthe advent of Virtual Slides high-throughput manual analy-sis of TMAs is possible. In addition image analysis ofTMAs provides a high-throughput, reproducible and quan-titative means of analysing immunohistochemicallystained tissue. Assays for molecular quantiWcation havebeen in existence for decades. In particular, popular tech-niques include reverse transcriptase polymerase chainreaction for quantiWcation of nucleic acids or antibodybased methods for protein quantiWcation (Camozzi andRazvi 2004). A major drawback to these assays is that theyrequire maceration of tissues and cells to quantitativelyassess the amount of particular biomolecules presentwhich leads to loss of critical spatial information (Creggeret al. 2006; Hewitt 2006).

The analysis of immunohistochemical staining patternsusually measures speciWc single targets rather than the rela-tively complex and intricate disease patterns, for examplethose seen on haematoxylin and eosin staining, thereforeimmunohistochemical studies are inherently amenable toautomated image analysis (Joshi et al. 2007). Sources ofvariability in immunohistochemistry are numerous andinclude Wxation conditions, specimen pre-treatment,reagents, detection methods, and interpretation of results.

Although it is not possible to standardise all the potentialvariables in immunohistochemistry, the interpretation ofimmunohistochemical results may be standardised throughquantitative methods (Cregger et al. 2006).

In theory it is not challenging to quantitate the intensityand area of brown staining using image analysis programs(Braunschweig et al. 2004). However, in comparison withother array platforms TMAs are not easy to analyse auto-matically. Every slide is stained diVerently, depending onthe laboratory, procedure, stain type and from day-to-day.In addition, every donor block may be Wxed diVerently,which hugely impacts on the quality of staining obtained.All imaging programs need to have the capability to bemanually adjusted to facilitate the diVerences in stainingconditions of each slide (Braunschweig et al. 2004). How-ever, despite technical diYculties it has become crucial toautomate TMA analysis and provide methods to manageand assess data in order to truly provide high-throughputanalysis (Braunschweig et al. 2004).

Automated immunohistochemical protocols in combina-tion with a device that provides quantitative and objectiveoutput, can dramatically improve the quality of the dataobtained from immunohistochemical studies (Cregger et al.2006). It has been proposed that computer-based analysiscan quantify staining intensity more accurately and withgreater reproducibility than manual human-based assess-ment (Weaver et al. 2003; Johansson et al. 2001). There arenumerous commercially available, computer-based systemsdesigned for the quantiWcation of immunohistochemicalstaining. The aim of this review is to examine the applica-tion of automated software solutions for the analysis of pro-tein expression within TMAs. Particular emphasis will beplaced on the workXow and infrastructure required to pro-vide a truly high-throughput automated image analysissolution for TMA applications.

TMA technology

Kononen et al. (1998) Wrst illustrated the use of TMAs in1998. The technique involves the excision of cores ofvarying diameter (0.6–2.0 mm) from regions of histologi-cal importance on donor tissue blocks and the subsequentinsertion of these excised cores into precise co-ordinateson a recipient block. This process is repeated until a two-dimensional matrix of cores is inserted into the recipientblock. Once the array is complete, sections can be cutfrom the block, which are then available for any analysiscurrently performed on full-face tissue sections. The mostcommonly applied analysis to TMAs is immunohisto-chemistry, with approximately 80% of all TMAs analysedin this way (Braunschweig et al. 2004; Shergill et al.2004).

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Tissue Microarrays greatly increase throughput of tissueanalysis. Analysis of prognostic and predictive markers hadtraditionally been performed by testing one marker at atime (Torhorst et al. 2001). However, utilising a singleTMA block containing 1,000 cores can potentially create200 slides, and as many as 200,000 individual assays can beperformed (Shergill et al. 2004). Therefore, TMAs allowserial selection analysis of multiple markers from the samemolecular pathway in a large number of tissue samples,facilitating direct comparison of alterations of multiplemolecular targets in virtually identical histologically highlyconserved tumour regions (Wang et al. 2002).

Impact of TMA construction and staining on visual interpretation

The numerous challenges associated with immunohisto-chemistry are often magniWed with the use of TMAs due tosmall sample size of the tissue cores and the diversity ofWxation and processing conditions of tissue originatingfrom diVerent sources (Braunschweig et al. 2004). Thus,although immunohistochemistry is no more challenging onTMAs than full-face sections, due to tissue originatingfrom diVerent sources TMA immunohistochemistry is morelikely to unmask deWcient protocols (Braunschweig et al.2004).

Nonetheless, there are many advantages associated withthe use of TMAs in comparison with full-face sections.TMAs introduce standardisation of protocols into histopa-thology over and above what is possible with full-face sec-tions (Tzankov et al. 2005), removing the inherentvariability in experimental conditions from batch-to-batchanalysis. With TMAs all tissue specimens arrayed on theone slide are analysed in an identical fashion. Antigenretrieval, reagent concentrations, incubation times with pri-mary and secondary antibodies, temperatures and wash con-ditions are identical for each core within a TMA, resulting inan unprecedented level of standardisation which is unattain-able utilising full-face techniques (Shergill et al. 2004).

However, sub-optimal immunohistochemistry in full-face sections and TMAs can be caused by many factorsincluding poorly Wxed/prepared sections; incomplete sec-tion drying or dewaxing; use of unclean xylene; insuY-cient/excess antigen retrieval; inappropriate antibodydilution; and non-speciWc staining due to endogenous tissueelements. With regard to TMA analysis there are other spe-ciWc issues that may aVect immunohistochemical stainingand interpretation. TMAs are susceptible to tissue loss dueto wash-oV following slide pre-treatments (dewax and anti-gen retrieval), this can signiWcantly reduce the number ofcores available for interpretation. Therefore, it is imperativethat replica cores and core sizes are carefully considered

when constructing TMAs. For example, four 0.6 mm coresfrom diVerent regions of a tumour may prove more repre-sentative and reproducible than one 2 mm core from thesame tumour. In addition, loss of cores or miss-alignedTMAs will also cause diYculties when de-arraying virtualTMAs. De-arraying is the automated process of Wrstly iden-tifying TMA spots within a virtual array and then subse-quently associating each TMA spot with the correspondinginformation from the TMA map.

Loss of cores can be signiWcantly reduced by utilisingadhesive slides which are subjected to baking at appropriatetemperatures or alternatively utilising tape transfer tech-niques which have been found to reduce tissue loss. TMAscontaining tissue embedded from several diVerent centrescan cause problems at all stages of the TMA process, fromconstruction to IHC staining and interpretation. Disparitycan result from variance in Wxation and processing proto-cols and even in the diVerent types of waxes used to embedthe tissue. Edge eVect and staining artifacts can also lead tomisinterpretation of peripheral cores. The occurrence ofedge eVect can be reduced by using irrelevant tissue coresto form a “moat” around study cores; and by using auto-mated immunohistochemical systems with on-board anti-gen retrieval and appropriate tissue section coverage (e.g.Leica Microsystems Covertiles or Ventana’s Liquid Cover-slip) to prevent reagent evaporation.

The quality of TMAs hugely inXuences the data obtainedfrom image analysis, even more so than with microscope-based assessments. While it is possible to identify irregular-ities in the data obtained from image analysis, factors suchas edge eVect and folding of tissue are problematic forinterpretation, and will aVect the accuracy of automatedimage analysis systems. Currently, image analysis systemsare not sophisticated enough to decipher edge eVect stain-ing from actual staining of interest, unless regions of inter-est are Wrst annotated and then processed. Therefore, edgeeVect may be incorrectly interpreted as positive staining. Ofa lesser concern is the occurrence of folded tissue, whichtypically results in the over quantiWcation of protein expres-sion. However, the occurrence of folded tissue can often beidentiWed within the image analysis results by utilising thepercentage of tissue present as a classiWer for eliminatingcores. In our experience, even the presence of tissue dye onthe circumference of full-face sections/TMAs hugelyaVects protein expression quantiWcation, by falsely inXatingthe level of positive expression observed. Therefore, it isimperative that TMAs are of a high quality; otherwise theinvestment in image analysis systems is futile. In addition,the presence of positive control tissue across all slideswithin a single study is imperative where quantitativeimage analysis will be applied. Positive controls can be uti-lised to normalise the data, therefore variance in stainingprotocols and background lighting can be eliminated.

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Manual interpretation of TMAs

Histopathology remains the gold standard for most diagno-sis and therapeutic decisions in pathology. The interpreta-tion of histologic sections however, is an inherentlysubjective process based primarily on morphologic features(Cregger et al. 2006). The bulk of cases usually lie betweenwhere the research scientists can interpret the data; how-ever, the quality of interpretation would improve with con-sultation by a pathologist (Hewitt 2006). Traditionally,human analysis has been considered the optimal method forqualifying immunohistochemical staining. Due to the com-plexity of tissue, the vast majority of TMAs continue to bescored by the human eye. However, the ability to quantifystaining intensity by human analysis has produced variedresults and is inherently Xawed (Conway et al. 2006). Inaddition, the quantiWcation of immunohistochemical stain-ing is greatly inXuenced by the complexity of the immuno-stain under assessment. Human analysis generallyquantiWes staining intensities into broad categories, ratherthan assigning exact staining intensity values. At present,alternative methodologies can accurately quantify proteinsignal when performed in conjunction with computer-assisted analysis, such as densitometry. However, in themajority of instances immunohistochemistry remains theprimary technique utilised (Bartlett et al. 2003; Ellis et al.2000, 2004; Hsi and Tubbs 2004; Hicks and Tubbs 2005;Kay et al. 2004).

It has been proposed that human assessment of immuno-histochemistry is considerably easier on TMAs comparedto full-face sections, due to the fact that it is possible tocompare staining intensities from diVerent specimens onthe same TMA. More importantly, interpretation is limitedto within a small predeWned area. Therefore, the area underinvestigation is constant for all reviewers, unlike full-facesections where diVerent reviewers will select diVerent areasof importance. In addition, due to the fact that a cohort ofsamples are typically analysed in a single review seatingwhereas traditionally this would have involved multipleseating’s (Tzankov et al. 2005; Zu et al. 2005). However,observer variability is still evident in the manual assess-ment of TMAs.

Observer variability can exist in three instances,inter-observer variability, intra-observer variability andinter-laboratory variability. Poor inter-laboratory agreementis usually attributed to variability in tissue Wxation, tissueprocessing, immunohistochemical protocols, antibodiesand scoring systems used in diVerent laboratories (Lacroix-Triki et al. 2006). Intra-observer variability has beenreported as being less frequent than inter-observer variabi-lity. It has been suggested that each pathologist adheres totheir own internal standards which in some cases, appear tobe consistently reproducible (Kay et al. 1994). Inter-

observer variability in relation to microscope-basedreviews of immunohistochemically stained tissue has beenwell-documented in literature.

Inter-observer variability, when performing tumouridentiWcation, is hugely dependent on the type of tumourassessed, the antibody under assessment and the standardcriteria available to identify the tumour in question (Schnittet al. 1992; Wei et al. 2004). In addition, inter-observer vari-ability is hugely reduced when well-deWned classiWers arein place, for example with the assessment of HER-2 proteinexpression. The semi-quantitative categories used to clas-sify HER-2 membrane staining are clearly deWned and arebased on intensity of staining, percentage and completenessof membrane staining. As a result inter-observer agree-ments when assessing HER-2 expression are greater than incomparison with other membrane antibody assessments forexample E-Cadherin protein expression, where universalwell deWned classiWcations systems are not in place (publi-cation in draft). Inter-observer variability is the greatestproblem associated with human-based microscope assess-ment. Numerous factors are attributed to inXuencing humaninterpretation of immunohistochemically stained tissue, andtherefore introducing inter- and intra-observer variability.These factors can be broadly divided into a number of cate-gories, which are brieXy described as follows:

Orientation

It is inherently diYcult to accurately track the location ofindividual cores within complex TMAs when performingmicroscope-based assessments. Reviewers often misplacetheir orientation and become confused about their locationwithin the slide, which threatens the accuracy of the resultsobtained. As mentioned previously, misplaced orientationis often exacerbated by poorly created TMAs. For example,TMA cores may be misaligned due to cores moving orwashing oV during the staining process. The orientation ofthe array is also crucial when performing automatedde-arraying, as the origin of the array has to be known inorder to assign the row and column values and associatedthe TMA spots to the TMA map. Typically control coresare used within TMAs, not only for reference tissue forreview, but also for points of reference for orientationwithin the array. Often distinctive tissue types are housedwithin the array structure, therefore each row and columnare denoted by a diVerent tissue type.

Alternatively orientation spots are positioned outside ofthe uniform TMA grid structure, in order to identify theactual origin of the array. However, depending on the trans-fer of the tissue from the microtome to the glass slide andthe actual size of the TMA, it is possible for eight diVerentorientations of TMAs to occur, therefore causing confusionwhen reviewing serial sections from one block. Figure 1

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illustrates the eight possible orientations of a TMA slide,and the arrows within the images represent the orientationin which the blocks were constructed.

Sequence of cores reviewed

Typically, the sequence in which the cores are reviewed canalso aVect reviewer’s perception of the tissue. For example,pathologists are extremely knowledgeable when identifyingtumour and can clearly recognise cores generated from thesame biopsy, especially when the tissue is reviewed insequence. Perception of staining intensity will also beaVected by the sequence in which the cores are reviewed.Reviewers often rely on previously reviewed TMA cores toform their opinion of subsequent cores. For example amoderately stained core could be categorised as weak if thecore was reviewed following a series of strongly stainedcores, as human assessment is not a true value, rather aform of “comparison” of colours. It can be argue that inorder to perform a totally impartial review, TMA coresshould be reviewed randomly. Others believe it is of beneWtto review all cores from one biopsy in sequence, in order toget an overall understanding of the tumour under review.

Workload and sample size

Pathologists are under increasing pressure to improve pro-ductivity and, are therefore generating more data andreviewing more slides. Tackling this workload manually

places a constant strain on time, resources, staV, andWnances. This burden is magniWed when reviewing TMAsdue to the volume of samples under analysis. In any Weldof science dependent on observation, accuracy is essential.However, it is well-documented that, after prolongedvisual study, eye and speciWcally cone-fatigue can signiW-cantly aVect a person’s ability to discern colour changesand identify unusual objects (Habib 2005). The TMA slideformat has compounded this eVect, and with densitiesexceeding 500 TMA spots per slide, fatigue quicklybecomes an issue.

Management of data

Due to the sheer volume and small sample size of tissuepresent on TMAs, there are diYculties in performingimmunohistochemical reviews using traditional micro-scope-based assessments. Large amounts of data are associ-ated with TMAs, ranging from information on the tissue(patient information), to their construction, subsequentstaining and assessment. As a result of the large amounts ofdata and the fact that microscope-based assessment typi-cally relies on the manual entry of results Wrst onto a work-sheet and then subsequently into a spreadsheet or databasesystem, accurate manual tracking of the TMA core data ischallenging, prone to human error and often leads to frus-tration (Tubbs et al. 2007). Therefore, it is apparent thatapplications to assist in pathologist’s reviews of TMAs arerequired, ideally online object-orientated databases.

Fig. 1 Illustrates the eight possible orientations of a TMA. Once thetissue is sectioned and transferred to a water bath, there are four possi-ble orientations. However, if the section is inverted when being trans-

ferred to the water bath, another four orientations are possible. Thearrow within the images signiWes the direction in which the TMA col-umns advance

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Scoring forms

The general parameters recorded during assessment ofimmunohistochemically stained tissues using traditionalmicroscopes are intensity, localisation and the proportion ofcells of interest that meet the Wrst two criteria (Hewitt2006). Human assessments can accurately and consistentlyidentify the presence or absence of disease and low or highstaining intensity. However, human assessment is not ascapable when utilising intermediate categories and hugeamount of variation is introduced as a result of over-usingthe intermediate category available during reviews (Kayet al. 1994).

Manual scoring systems are qualitative or semi-quantita-tive in nature, either when performing virtual or micro-scope-based reviews. Quantitative scales are either binary(§) or normative (0, 1, 2, 3). Qualitative scales have limita-tions in resolution which can be detected by eye, thus manyresearchers build a simple scale, as 0, 1, 2, according tonegative, weak, strong (Braunschweig et al. 2004). Manualreview requires interpretative skills of well-trained investi-gators and frequently the eVorts of a specialist primarilypathologist. Staining patterns that are anticipated to be usedin clinical practice are usually scorable as positive or nega-tive, whenever possible (Braunschweig et al. 2004). Thenumber and complexity of the categories used to recordimmunohistochemical staining will aVect the levels ofinter- and intra-observer agreement.

Illumination

Apart from the quality of the microscope, the next mostimportant item in the reviewing process is the illuminationof the slides. Bulbs used in microscopes have a characteristic

tint; in general this is yellow or straw coloured. It has beensuggested that the bulb tint inXuences human perception ofstaining intensity (Conway et al. 2006). If too much or toolittle light is exposed, information about the intensity ofstaining is lost. Adjustments in lighting settings from slide-to-slide can introduce huge variability in the reviewing pro-cess. Consistency of light between slides and reviews isextremely diYcult during microscope-based assessments, astints and shades can appear to change from one setting orcontext to another. Figure 2 illustrates an area of tissuewhich was scanned using diVerent lighting exposure levels.The digital image represents the appearance of tissue undera microscope. Clearly, the perception of membrane stainingintensity is aVected by background lighting settings.

Human vision limitations

The accuracy of human vision is highly variable from per-son-to-person and is an extremely complex process; it isalso hugely objective. The nature of the human eye is suchthat every person sees an object slightly diVerently from theway others see that same object, subjectivity in this regardis therefore innate (Habib 2005). DiVerent observers mayreport seeing diVerent features on the same object, as may asingle observer at diVerent times (Habib 2005). Visualinspection can also be confounded by the inherently subjec-tive nature of human observation, which is aVected by con-text, for example the amount of tumour present,background staining, and stromal staining (Camp and Div-ito 2005). Numerous facts aVect human vision includingcontrast, borders, and colour, and these aVects can be illus-trated using a number of optical illusions.

Contrast is the local change in brightness and is deWnedas the ratio between average brightness of an object and the

Fig. 2 Represents an area of tissue scanned utilising two diVerent lighting exposure settings. Perception of membrane staining is hugely aVectedby the lighting exposure at which the slide was scanned. This is also evident and more prevalent with microscope-based analysis

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background brightness. The human eye is logarithmicallysensitive to brightness, implying that, for the same percep-tion, higher brightness requires higher contrast (Sonka andBoyle 1993). Apparent brightness depends very much onthe brightness of the local background; this eVect is called“conditional contrast”(Sonka and Boyle 1993). Figure 3 (aand b) illustrates the fallibility of human perception of con-trast (Dodek 2007). Figure 3a illustrates a vertical bar witha single colour throughout. When viewed with the contrastof a white background the vertical bar is clearly a singlecolour. However, when the vertical bar is superimposed ona background with a changing gradient of colour, our per-ception of the vertical bar has changed (Fig. 3b). Contrast isextremely applicable in the assessment of membrane-boundimmunohistochemical staining. In cases where there is nocytoplasmic staining, membrane staining will appear stron-ger than in cases where cytoplasmic staining is present.

Object borders carry a lot of information. Boundaries ofobjects and simple patterns such as circles or lines enableadaptation eVects similar to “conditional contrast”. TheEbbinghaus illusion illustrates how humans can misinter-pret size of particles when displayed in relative compari-sons (Plodowski and Jackson 2001). Figure 4a, b displaystwo circles of the same diameter; however, as they are

surrounded by circles of diVerent diameters they appear tohave diVerent diameters (Sonka and Boyle 1993).

During the assessment of immunohistochemicallystained TMAs the comparison of colour is paramount. Aspreviously mentioned, manual review utilising a micro-scope is based on comparisons of tissue rather than an inde-pendent assessment of the true colour of the tissue underreview. The Bezold EVect describes how colours appeardiVerently depending on their relationship to other colours.Figure 5 illustrates that the colour red appears lighter whenit is surrounded by a white border, and darker when sur-rounded by a black border (Lockal 2007).

Contrast is extremely applicable in the assessment ofmembrane-bound immunohistochemical staining. The con-trast between membrane and cytoplasmic staining may behugely variable and can aVect human perception. Figure 6A1 and B1 illustrates two images of bladder tissue probedwith the antibody for E-cadherin. The two images haveequivalent membrane staining intensity when quantiWed bycomputer-aided image analysis. The areas identiWed as posi-tive for membrane staining by image analysis are high-lighted in green (Fig. 6 A2 and B2). However, themembrane staining intensity appears signiWcantly diVerentwithin the two images when assessed by human review,

Fig. 3 a Single coloured bar against white background. b Illustrates the identical vertical bar as in Fig. 3a. However, within this Wgure the verticalbar is surrounded by a background with a changing gradient of colour. As a result, the vertical bar no longer appears the same colour throughout

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Wrstly due to the diVerential tumour morphology, and sec-ondly due to the presence of cytoplasmic staining withinimage 6A1. Figure 6 illustrates how both size and contrastaVects human perception of staining intensity.

Virtual microscopy

Virtual Slides is a term used to describe the digitisation oftraditional glass slides. Virtual Slides overcome problemsattributable to sampling bias and interpretation resultingfrom limited Weld selection, allowing telepathologists tonavigate to any Weld of view, at magniWcations comparableto that of a conventional microscope, using images of suY-cient resolution to render a correct diagnosis (Costello et al.2003). In this technique, a conventionally prepared glassslide is placed on a microscope with a motorised stage andan automatic focusing facility or alternatively a specialised

scanning device. The slide is scanned using a 10£, 20£ or40£ objective lens and these images are integrated to pro-duce a single large image Wle. This Wle can then be viewedon any computer with a virtual microscope interface wherea user can press keys to change magniWcation from an over-all low-power view up to the resolution at which it wasscanned (Cross et al. 2002). Virtual Slides provide userswith similar functionality of a microscope, but with numer-ous additional beneWts, including concurrent access formultiple users, tracking of review movements and imageannotation.

Advances in new technologies for complete slide digiti-sation in pathology have allowed the development of awide spectrum of solutions for full-face slide scanning(Rojo et al. 2006 Vicente). Typically, acquisition devicescan be broadly categorised based on their modes of action,of which three currently exist. Firstly, Weld of view deviceswhich digitise slides based on capture of many small

Fig. 4 Ebbinghaus illusion illustrates how the interpreta-tions of the size of objects are relative to their surroundings. The red circle within image a and b are identical; however, perception of the size of the red circle is altered by the blue cir-cles surrounding them

Fig. 5 Bezold EVect illustrates how the appearance of colour is altered by the colours that sur-round them. In this case, the col-our red appears lighter when surrounded by white, and darker when surrounded by black Watanabe (2007)

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regions of the slide via a microscope with a traditionalcharged coupled device (CCD) mounted camera. Thenumerous images are then stitched together to create onelarge digital image. Numerous providers utilise Weld ofview technology within their instruments, for example,Olympus dotSlide (Olympus UK Ltd), 3DHistech Ltd(Hungary) and Genetix (formally Applied Imaging, UK).Secondly, linear array devices which capture a small num-ber of contiguous overlapping image stripes (Aperio-Tech-nologies 2008). Linear array devices continuously move themicroscope slides during image acquisition, therefore,facilitating rapid slide digitisation and seamless images.The key providers which use linear array devices are Ape-rio (Aperio Technologies, Inc., USA) and Hamamatsu(TDI-CCD technology, Hamamatsu Photonics, UK).Finally, area array scanners utilise many objectives ratherthan one and therefore, can digitise large areas faster thanwhen using traditional Weld of view devices. At presentDmetrix Inc, (USA) are apparently the only vendor utilisingthis technology.

A number of new technologies are developing in theWeld of virtual microscopy. Hybrid scanners which providenumerous additional functionalities in addition to bright-Weld scanning are beginning to emerge. Extended depth-of-Weld and multi-focal scanners are broadening the domain ofvirtual microscopy to cytology applications for examplecytology. Currently, Hamamatsu provides a scanner that iscapable of multi-focal plane scanning, whereas 3DHistechprovide a scanner that can facilitate extended depth-of-Weld

scanning. In addition, the development of optical projectiontomography (OPI) microscopy which facilitates the 3Dimaging of biological specimens facilitates the mapping ofmultiple proteins distributions within the same tissue(Sharpe 2008). Currently, Bioptonics (MRC Technology,UK) claim to provide a scanner that can generate OPIimages under 30 min.

The memory requirements for storing a digitised full-face slide/TMA is dependent on the area of tissue beingscanned, the optical resolution (magniWcation) it is scannedat and the image Wle format/compression algorithm used forits storage. Typically, full-face scanning of a single stan-dard paraYn-embedded slide (18 £ 22 mm) at an opticalresolution equivalent to 40£ requires up to 1–1.2 GB ofstorage. Storage of up to 250–300 MB is required whilescanning at an optical resolution equivalent to 20£. Inorder to calculate the storage requirements for a project for1 year estimate the approximate number of slides to bescanned at a particular resolution. For example 1,000 slidesat 40£ = (1,000 £ 1.2 GB) » 1.2 TB. As a baseline formost projects it is recommended that suYcient storage beprovided for at least 3 years. It is also important to considerredundancy/backup requirements for image data.

Due to the size of the images, specialist viewer softwaremust be used in order to view Virtual Slides. Numeroussoftware applications are available which facilitate distribu-tion of images, locally and via the Internet. Image viewerstypically facilitate the viewing and panning of VirtualSlides, however more complex functionality, for example

Fig. 6 Both images A1 and B1 have equivalent membrane staining intensity when assessed by image analysis; however the intensity appearsdiVerent when assessed by eye. Within images A2 and B2 the green colouring represents the positive membrane staining assessed by image analysis

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annotations and analysis is outside the scope of typicalimage viewers. The majority of commercially availableimage viewers are provided by the scanner vendors in con-junction with the hardware. For example, Aperio provideImageScope, and Hamamatsu provide NDI Viewer. How-ever, each of these viewers are vendor speciWc and there-fore do not support alternative vendors Wle formats. As aresult, collaboration with institutes utilising alternativescanners would require additional software. There are someimage viewers that have non-priority formats, for exampleZoomify Inc. (USA). Zoomify Droplet is a MacromediaFlash application which uses the original scanned image asan input and converts it into a set of JPEG image tiles. Thistileset, once uploaded to a webserver, can be displayed viathe Internet using the Zoomify embedded object within aconventional web page (Conway et al. 2006). Typically,image viewers are only used to verify the quality of thescan.

Utilising Virtual Slides it is possible to overcome someof the problems experienced when performing microscope-based TMA reviews. Uniform lighting conditions can beachieved across many TMAs when scanning slides. Thiseliminates the possibility of variance of interpretation dueto background lighting. Integrating Virtual Slides withinTMA workXow software facilitates the integration of TMAreview data with the digital image of the TMA slide. Thesequence in which cores are reviewed can be customised,which in return reduces sample bias. Finally, by using auto-mated image analysis systems which are quantitative andproduce continuous data sets, the elimination of categorisedassessments in what is continuous data can be eliminated.High-throughput automated image analysis systems also

reduce workloads, compensate for limitations in humanvision, and as a result reduce inter- and intra-observervariability. Figure 7 illustrates the limitations of micro-scope-based assessments, and the solutions that virtualmicroscopy provides.

Software workXow solutions for TMAs

The development of TMAs has signiWcantly increased thethroughput of tissue analysed using immunohistochemistry,compared to more traditional full-face methods. Initial TMAstudies were uni-dimensional, one stain, hundreds of sam-ples. Management of the data could be easily recorded with asimple spreadsheet. In some instances this remains true, suchas when a TMA is used to conWrm a “hit” from a microarrayexperiment (Braunschweig et al. 2004). However, studies arenow applying multiple stains to a single TMA or series ofTMAs, generating large and complex datasets. Datasets withclinical outcome and epidemiologic information paired withimmunohistochemical data can be in excess of 50,000 ele-ments. As a result object-orientated databases are essential tomanage the data. Many investigators who began with simplespreadsheets have had to abandon them as their datasets havegrown, and have migrated to more robust enterprise levelserver based platforms (Conway et al. 2006). These issuesare especially problematic for users who wish to maintainimages of the individual TMA cores within the database(Braunschweig et al. 2004). Therefore, TMAs require spe-ciWc management tools (Rojo et al. 2006).

The ability to create association between TMA spotimages and data is fundamental for successful Virtual

Fig. 7 Summaries the general limitations of microscope-based manual review of TMAs, and the solutions that Virtual Slides and automated image analysis systems can provide

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Histochem Cell Biol (2008) 130:447–463 457

Microscopy. Even with the development of automated ana-lysis it remains necessary to manually inspect and verify theimages and data at some point (Hewitt 2006). Therefore,software which can facilitate the manual analysis and stor-age of large images and associated data is imperative, espe-cially with regard to the additional complexities associatedwith TMA reviews.

There are numerous software applications that facilitatereview and data storage of TMAs. The technology variesfrom academic oVerings to highly sophisticated commer-cial applications (Conway et al. 2006; Manley et al. 2001;Liu et al. 2002). Although Microsoft Excel™ spreadsheetsare traditionally used by scientists to store data, there isalways a signiWcant risk of human error, as large amountsof data entry are required and the object-oriented nature ofthe data does not lead to optimal data storage in spread-sheets, also data is vulnerable to Wle corruption. In addition,the ability for numerous users to edit spreadsheets intro-duces potential opportunity for human errors without anyprotocol for tracking the authorships of Wles. However, it isimperative that TMA data is in a format that is easily avail-able for distribution. The importance of distributing TMAdata is evident from the creation of a TMA data exchangespeciWcation, which is a community-based open source toolfor sharing TMA data. In 2001, the Association of Patho-logy Informatics hosted the Wrst in a series of four work-shops co-sponsored by the National Cancer Institute todevelop the open community supported TMA exchangespeciWcation, which allows researchers to submit their datato journals and to public domain repositories and to shareand merge data from diVerent laboratories (Berman et al.2003).

Academic software which facilitates the storage of TMAdata and images have the advantage of being low cost andfreely assessable to other low volume researchers. How-ever, software created in an academic setting are typicallyhardcoded and therefore do not facilitate on-the-Xy modiW-cations. In addition, customer support is limited and accessis restricted to researchers only. One of the most successfulacademic oVerings is TMAJ, which is reported to consist ofa database and set of open source software tools to manageTMA data and images. TMAJ is presently implemented atThe Johns Hopkins TMA Laboratory, USA and is freelyavailable as an open-source software tool for academic useonly. TMAJ contains data from over 13,500 specimens,7,000 blocks and 235 TMA’s containing greater than35,000 tissue cores (De Marzo 2003).

There are numerous commercial oVerings which providea complete TMA workXow, these systems range in com-plexity and functionality. The leaders in the Weld of TMAspeciWc software include; SlidePath’s (OpTMA), Aperio’s(TMALab II) and Alphelys (Tisalys®). The systems func-tionality varies; however, all the above systems provide the

utility to perform manual and automated image analysisreviews and store the review and epidemiological data in anassociated database. Key features of any TMA workXowsolution should incorporate the ability to upload and de-array TMAs with automatic identiWcation and associationof cores with case information. The system should providea rapid review interface to facilitate manual reviews, withthe instant embedding of scoring data into case informationWles. The ability to perform high-throughput consolidationacross numerous reviewers’ data for multiple cores perbiopsy or patient cases should also be possible. Ideally, theability to view virtual arrays of all cores pertaining to abiopsy or patient that have been immunohistochemicallystained with numerous biomarkers would be possible.Finally the system should be fully searchable to providerapid retrieval of the review and associated data. Softwaresolutions that support some or all of the above features haverelieved the bottlenecks in TMA review and data manage-ment. However, to truly realise the full potential of TMAtechnology, high-throughput automated image analysisshould be considered.

Automated image analysis of immunohistochemically stained TMAs

It is possible to create image analysis algorithms whichquantify protein expression within TMAs utilising genericprogramming applications for example, MatLab® (TheMathWorks, Inc., USA) or ImageJ (National Institute ofHealth, USA) (Carmona et al. 2007; Francisco et al. 2004).Alternatively, it is possible to utilise commercially avail-able image analysis applications which allow generalresearchers to write and record application speciWc macrosin order to facilitate automated quantiWcation of proteinexpression, for example Image-Pro Plus® (Media Cybernet-ics, Inc., USA). Utilising Image-Pro Plus, it is possible toextract features with spatial tools that isolate an area ofinterest from the rest of the image, or with segmentationtools that extract features by colour or intensity value. Thegreatest advantage of Image-Pro Plus is that non-program-mers can create an eVective algorithm. However, Image-Pro Plus is only of beneWt for Weld of view analysis, as themanual segmentation of large images into tiles of areas ofinterest is highly labour intensive. While Image-Pro Plus isaccurate, reproducible and quantitative the software alonewill not increase throughput of analysis.

There are numerous commercial systems available thatare speciWcally designed for the quantiWcation of immuno-histochemical staining including; IHCscore (Bacus Labora-tories, Inc, USA); iVision (BioGenex Laboratories, Inc.,USA), TissueMap (DeWniens, Germany), VIAS (TriPathImaging Inc, USA); PATHIAM (BioImagene Inc, USA);

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458 Histochem Cell Biol (2008) 130:447–463

ACIS-Automated cellular imaging systems (DakoCytoma-tion, USA); AQUA-automated quantitative analysis (His-toRx Inc, USA) and TMAx (Beecher Instrument’s, USA).Cregger et al. (2006); comprehensively reviewed the func-tionality of the image capture devices and image analysiscapabilities of numerous vendors. In addition, Rojo et al.(2006); performed a comparative review of 31 digital slidesystems in pathology, describing hardware and softwarefunctionalities. However, there are number of softwaresolutions speciWcally designed for the TMA workXowanalysis. These applications include Aperio (TMALab II),Alphelys (Spot Browser®), Genetix (Ariol-SL-50) andSlidePath (OpTMA). However, the fact that the majority ofimage analysis systems only perform Weld-of-view analysisis a major limitation with regard to high-throughput analy-sis. There are only a limited number of vendors that providefull-face and TMA high-throughput analysis, for example,SlidePath and Aperio. A brief description of the TMA spe-ciWc vendors functionality follows.

Aperio’s device, the ScanScope is designed for imageacquisition. Aperio currently has Wve generations of theScanScope; (T3, T2, CS, GL and XT) (Aperio Technolo-gies, Inc, CA, USA) (Cregger et al. 2006). The ScanScopeis capable of high-speed digital slide creation, management,and analysis. Aperio also provide software, namely TMA-Lab II which facilitates the storage, manual/automatedanalysis of TMAs and storage of associated data andimages with web-based software (Rojo et al. 2006). UsingTMALab II it is possible to view, score and annotateTMAs, and in addition images and data can be exportedfrom the database. Utilising TMALab II, it is possible toanalyse entire immunohistochemically stained TMAs, spotsor regions of interest using the following algorithms;nuclear, membrane, colour deconvolution and co-localisa-tion. In addition, TMALab II also supports third party algo-rithms, for example those written using Image-Pro Plus orMatLab®. However, the software will only support Ape-rio’s own image format SVS, therefore collaborationbetween other institutes using alternative scanners can notbe supported. Aperio’s software is extremely popularwithin the USA; however, in Europe where Zeiss, Ham-amatsu and Olympus scanners are widespread the limita-tion of the software’s proprietary Wle format restricts theapplication of TMALab II (Aperio 2008).

Alphelys provide Tisalys®, a database for archiving,reviewing and processing images and data generated duringTMA analysis. Alphelys also provide Spot Browser®, animage analysis workstation integrated with microscope,visualising and capturing images through colour CCD cam-era and using a motorised stage. It allows rapid scanning ofTMAs to build the TMA map, assignment of deWnedcoordinates to tissue spots to track and provide a user’s inter-face for pathologist’s visual inspection and TMA browsing.

Spot Browser® facilitates the analysis of TMAs eitherthrough visual inspection on the oculars or on the high reso-lution screen, or through automated detection of speciWcevents for example, nuclei counting, signal quantitation,surface determination, morphometry or both methodssimultaneously. All data collected can be exported to Excelfor further data processing (Alphelys 2008).

Genetix (formally Applied Imaging) provide the Ariol SL-50, a TMA analysis application which combines an auto-mated scanner and high-throughput automated image analy-sis application for the quantiWcation of biomarkers onmicroscope slides in both brightWeld and Xuorescent imag-ing. Ariol has been FDA approved for in vitro diagnostic useof HER-2/neu, ER and PR Immunohistochemistry. The AriolSL-50 system quantiWes nuclear, cytoplasmic and membraneimmunohistochemistry protein expression utilising bothnominal and quantitative scales. Both images and data arearchived in case Wle. Utilising industry standard SQL andXML facilitate export of data and images from the Ariol SL-50 system to third party databases. However, Ariol SL-50operates purely on Weld of view analysis (Genetix 2008).

SlidePath provides a software product called OpTMA,which is a secure web-enabled information management sys-tem that facilitates integration of project information, digitalslides (full-face and TMAs) and multimedia Wles (for exam-ple, PDF’s, Microsoft Word) into a fully searchable, hierar-chical database. OpTMA enables easy curation of digital slidearchives and rapid retrieval of slides based on associated dataattributes. OpTMA also allows users to create customiseddatabases in order to store TMA images and clinical patho-logical data. In addition, the software fully automates the de-arraying process of TMAs, and then automatically associatestissue spots with data. OpTMA also facilitates online reviewsof virtual TMAs whilst storing the generated data within thedatabase. The functionality to consolidate review data gener-ated from multiple cores from a single biopsy/patient is alsoavailable. Users are presented with a virtual array of cores andassociated review data, a consolidation form is then used torecord the overall observation of the multiple cores, and datais returned to the database. The software facilitates high-throughput automated image analysis, utilising nuclear, mem-brane, cytoplasmic and positive pixel algorithms. Results canbe presented as either nominal or quantitative data. SlidePathcreated an image analysis grid computing system which dis-tributes images across multiple processing nodes, thereforefacilitating truly high-throughput automated analysis acrossentire full-face sections and TMAs. In addition, third partyalgorithms, for example those created using ImageJ or Mat-Lab®, can be integrated into the image analysis harness. How-ever, the greatest advantage of SlidePath’s products is thesoftware is vendor neutral. Currently, SlidePaths softwaresupports Zeiss (Mirax), Aperio (SVS), Bacus (BLISS), Nikon(VSL), Olympus (WebImage) and Hamamatsu (VMS and

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Histochem Cell Biol (2008) 130:447–463 459

NDPi) image Wle formats, in addition the software also sup-ports non-propriety Wle formats for example JPEG, TIFF andBitmap (SlidePath 2008).

With respect to image analysis, it is important to notethat human analysis is still the gold standard when it comesto feature recognition and object classiWcation. Humanreviewers can easily identify and classify tumour from non-tumour and diVerentiate cell types from each other. Imageanalysis, on the other hand, is extremely accurate at quanti-Wcation of staining extent and intensity. Image analysis inconjunction with TMAs (which are punched by qualiWedhuman observers from appropriate regions of tissue) is acombination that helps eliminate the obvious deWcits thatthis technology experiences and allows developed algo-rithms to focus on quantiWcation over object recognition.

Systems performance

Automated image analysis systems need to be reproducibleand at least as accurate as traditional methods of analysis.Typically, the accuracy of these systems are validated bycomparing protein expression levels when quantiWed byautomated means with manual review data, traditional labo-ratory tests (FISH and ELISA), and prognostic outcome. Anumber of commercially available imaging systems havereceived FDA premarket approval to quantify biomarkerexpression as an aid in diagnosis. In order to obtain FDA

approval the level of concordance between manual andautomated image analysis is assessed. Table 1 illustratesthe total number of automated imaging systems that havereceived FDA approval, and the levels of concordancebetween automated and manual reviews. The table illus-trates there is a high level of correlation between manualand automated analysis. However, as previously describedhuman analysis is inherently Xawed. Therefore, correlationof biomarker expression with prognosis is a more robustevaluation of an image analysis system.

Table 2 lists the numerous publications that have utilisedimage analysis systems as a means of quantifying proteinexpression. The table illustrates the level of correlationbetween automated imaging and manual review, laboratorytests and prognostic data. The majority of publications haveutilised ACIS and AQUA systems, which as the resultsillustrate are highly accurate when quantifying proteinexpression. However, currently the systems do not have aspeciWc TMA workXow in place. Also the majority of anti-bodies that have been assessed are membrane speciWc forexample HER-2 protein expression, or nuclear speciWc.

Factors to consider when deciding on image analysis applications

As with all experiments the quality of the results obtainedare dependent on the procedure and raw materials utilised.

Table 1 FDA 510k Approved Automated Image Analysis Systems and their performance

a In general, the likelihood of the image analysis systems to produce a consistent score on a given slide is as likely as the pathologists are to agreewith each otherb Depending on cut-oV thresholds of pos ¸1, 5 or 10% positive stained tumour cells

Manufacturer System Approved use Assay Sample size Automated vs manual score % concordance

Genetix Ariol HER-2 DAKO HercepTest 124 a

HER-2 (FISH) Abbott Vysis PathVysion DNA Probe kit

82 98

ER Kisight nuclear IHC 75 93.2–98.6b

PR Kisight nuclear IHC 75 84.4–96.1b

TriPath Imaging VIAS HER-2 Ventana PATHWAY anti-HER-2/neu (clone cb11)

201 77

HER-2 PATHWAY (4B5) 206 86

PR Ventana anti-ER 210 88.2–94.1b

ER Ventana anti-PR 210 94.6–98.5b

P53 Ventana CONFIRM anti-p53 204 86–98b

Ki67 Ventana anti-Ki-67 207 88.4–97b

Chromavision ACIS HER-2 DAKO HercepTest 90 75

ER&PR No data No data No data

Cell analysis QCA ER DAKO Cytomation (1D5) 192 85.15

BioImagene PATHIAM HER-2 DAKO HercepTest 176 80.4

Aperio ScanScope XT System HER-2 DAKO HercepTest 180 86.5

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460 Histochem Cell Biol (2008) 130:447–463

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123

Histochem Cell Biol (2008) 130:447–463 461

In the case of image analysis applications this translates toimage and stain quality (Hewitt 2006). Image analysis per-formed on poorly scanned Virtual Slides or tissue withstaining artefacts will result in all likelihood in inaccurateprotein expression quantiWcation.

There are numerous factors to consider when selectingimage analysis software. Firstly, the system has to be userfriendly. There are numerous publications listing the meritsof image analysis as a means to quantify protein expressionon TMAs. However, the inXux of commercially availableimage analysis applications utilised to quantitate proteinexpression maybe extremely complex and diYcult to use.Researchers have to decide upon creating their own algo-rithms using software which facilitates macro developmentfor example, Image-Pro Plus or to purchase complete TMAsoftware solutions for example OpTMA (SlidePath). How-ever, if algorithms are created using applications likeImage-Pro Plus extensive validation of the quality of theresults obtained is required. Typically, it is not possible topurchase oV-the-shelf systems that require no knowledge orunderstanding of image analysis. The intended users shouldcomprehend the basic principles of image analysis, and alsobe able to interpret the large amounts of data that are gene-rated from image analysis reviews. This skill set is notinnate in scientists, and therefore it is often more viable toout-source image analysis requirements.

Secondly, there is a perception that image analysis andvirtual applications are prohibitively expensive. Research-ers tend to focus on the most expensive component withintelepathology, which is the purchase of the scanningdevice. Currently, the cost of purchasing high-throughputscanners usually run at between 60,000 and 180,000 Euros(Rojo et al. 2006). As a result, commercial systems are notalways viable in research or small laboratories (Camp andDivito 2005; Camp et al. 2002, 2003), especially, asresearchers typically only produce a small volume ofTMA slides per year. However, collaborations can be cre-ated between institutions, where a scanner is purchased bya consortium of institutes, and slides are posted for digiti-sation. By using a web-based information managementsystem, slides are then available for manual and auto-mated image analysis. The costing of software solutionsthat provide high-throughput automated analysis is rela-tively low in relation to other “materials” that are pur-chased in wet laboratories. The costing of these systemshas to be oV-set against the return on investment. Auto-mated systems are proven to increase accuracy of resultsand are more reproducibility than manual assessments.However, most importantly automated analysis systemsincrease throughput and therefore save pathologists’ pre-cious time. In addition, numerous vendors provide man-aged services whereby the digitisation and image analysisrequirements can be out-sourced, if the scale of project

does not merit the purchase of software or a scanner (forexample SlidePath).

Thirdly, a major consideration when utilising image ana-lysis systems is how best to interpret the data. Typically,quantitative computer aided image analysis results in contin-uous variable data rather than an ordinal parameter. There-fore, the users must decide how best to classify the biomarkerexpression data, if at all. If the objective is to identify a prog-nostically signiWcant biomarker it is possible to evaluate thedata as a continuous variable, by using the Cox proportional-hazard regression. However, if users wish to persist withordinal or nominal classiWcation (i.e. use of Kaplan–Meieranalysis) the dataset must Wrst be segmented into categories,by implementing arbitrary cut-points. Unbiased assignmentof cut-points can be achieved by creating two categoriesabove or below mean or modal continuous variable value orby assessing top vs bottom quartile of a continuous variablerange. However, assignment of cut-points based on minimi-sation of P values is a Xawed strategy. Users of this approachwill have to divide their datasets into training and test sets,validating the signiWcance of these cut-points in a separatecohort of patients. This in turn creates a requirement for theprovision of greater numbers of patients to increase statisticalpower. X-tile (Camp et al. 2004) is a particularly usefulutility to help identify the optimal cut points in continuousdata based on P value minimisation strategies.

Conclusions

Tissue Microarrays facilitate high-throughput biomarkervalidation, by arranging hundreds of tissue samples in auniform structure on the surface of a glass slide. However,due to the sheer volume of tissue present within TMAs,there are bottlenecks when performing microscope-basedreviews. In addition, human interpretation of staining inten-sity is inherently Xawed. Utilising Virtual Microscopy, it ispossible to overcome the bottlenecks associated with tradi-tional microscope-based TMA reviews. Numerous softwaresolutions exist which provide an end-to-end solution forTMA-based analysis, facilitating both manual and auto-mated reviews. Currently, Virtual Microscopy is preferableto traditional microscope-based reviews. In addition, imageanalysis has proven to be more accurate when quantiWngbiomarker expression. However, human interpretation offeature recognition is still superior to any image analysissystem currently available.

References

Al Kuraya K, Simon R, Sauter G (2004) Tissue microarrays for high-throughput molecular pathology. Ann Saudi Med 24:169–174

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Virtual microscopy as an enabler of automated/quantitative assessment of protein expression in TMAsAbstractIntroductionTMA technologyImpact of TMA construction and staining on visual interpretationManual interpretation of TMAsOrientationSequence of cores reviewedWorkload and sample sizeManagement of dataScoring formsIlluminationHuman vision limitations

Virtual microscopySoftware workXow solutions for TMAsAutomated image analysis of immunohistochemically stained TMAsSystems performanceFactors to consider when deciding on image analysis applicationsConclusionsReferences

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