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Diagnosis of epithelial mesothelioma using tree-based regression analysis andDiagnosis of epithelial mesothelioma using tree-based regression analysis and

a minimal panel of antibodiesa minimal panel of antibodies

Sonja Klebe a; Markku Nurminen b; James Leigh c; Douglas W. Henderson a

a Department of Anatomical Pathology, Flinders Medical Centre, Adelaide, South Australia b Finnish Institute

of Occupational Health, Helsinki, and Department of Public Health, University of Helsinki, Finland c Centre for

Occupational and Environmental Health, School of Public Health, University of Sydney, New South Wales,

Australia

Online Publication Date: 01 February 2009

To cite this ArticleTo cite this Article Klebe, Sonja, Nurminen, Markku, Leigh, James and Henderson, Douglas W.(2009)'Diagnosis of epithelial

mesothelioma using tree-based regression analysis and a minimal panel of antibodies',Pathology,41:2,140 — 148

To link to this Article: DOI: To link to this Article: DOI: 10.1080/00313020802579250

URL: URL: http://dx.doi.org/10.1080/00313020802579250

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A N A T O M I C A L P A T H O L O G Y

Diagnosis of epithelial mesothelioma using tree-based regression

analysis and a minimal panel of antibodies

SONJA KLEBE*, MARKKU NURMINEN{, JAMES LEIGH{ AND DOUGLAS W. HENDERSON*

*Department of Anatomical Pathology, Flinders Medical Centre, Adelaide, South Australia; {Finnish Institute of

Occupational Health, Helsinki, and Department of Public Health, University of Helsinki, Finland; {Centre for

Occupational and Environmental Health, School of Public Health, University of Sydney, New South Wales,

Australia

Summary

Aims: Immunohistochemistry with panels of antibodies is a

standard procedure to distinguish between malignant meso-

thelioma and metastatic adenocarcinoma. Most studies

assess only the sensitivity and specificity for single anti-

bodies, even when the paper concludes by recommending

an antibody panel. It was the aim of this study to use a novel

statistical approach to identify a minimal panel of antibodies,

which would make this distinction in the majority of cases.

Methods: Two hundred consecutive cases of pleural malig-

nancy (173 pleural mesotheliomas of epithelial type and 27

cases of secondary adenocarcinoma) were investigated

using a standard panel of 12 antibodies (CAM5.2, CK5/6,

calretinin, HBME-1, thrombomodulin, WT-1, EMA, CEA,

CD15, B72.3, BG8, and TTF-1). Regression and classifica-

tion tree-based methods were applied to select the best

combination of markers. The modelling procedures used

employ successive, hierarchical predictions computed for

individual cases to sort them into homogeneous classes.

Results: Labelling for calretinin and lack of labelling for BG8

were sufficient for definite correlation with a diagnosis of

malignant mesothelioma. CD15 provided further differentiat-

ing information in some cases.

Conclusion: A panel of three antibodies was sufficient in most

cases to diagnose, or to exclude, epithelial mesothelioma.

Calretinin exhibits the strongest correlative power of the

antibodies tested.

Key words: Pleural malignant mesothelioma, antibody, calretinin, panel,

metastatic adenocarcinoma, tree regression analysis.

Received 9 January, revised 24 February, accepted 3 March 2008

INTRODUCTION

The biopsy diagnosis of pleural malignant mesothelioma

(MM) can be problematic and requires the use of ancillary

techniques more frequently than most other epithelioid

tumours. In most laboratories, immunohistochemistry is

todaythemainstayforthepathologicaldiagnosisofMM.1–4

Because no single antibody has been identified with 100%

sensitivity and 100% specificity for a diagnosis of MM,

panels of antibodies that include both positive and negative

markers are routinely employed; yet even the most recent

and comprehensive of the published studies focus on the

sensitivity and specificity of single antibodies investigated

independently of the others,4and numerous recent reviews

suggesting various panels of antibodies5–13are based on this

type of analysis.5–7A recent attempt at meta-analysis has

been made to provide guidance,9but because of hetero-

geneity in the raw data in the individual studies on which the

meta-analysis was carried out, the validity of this approach

is limited. The same principle applies to the Web site

STATdxPathIq(formerlyknownas‘Immunoquery’; https://

immunoquery.pathiq.com/pathIq; accessed 2 December

2008), which provides sensitivities and specificities of

antibodies, based on published studies, and suggests IHC

panels for differential diagnosis based on those data. Only a

handful of studies have attempted to use more advanced

statistical methods, including logistic regression,1,2,14but the

only study attempting to include stepwise logistic regression

employed pleural effusion fluids and used a panel of

antibodies that included predominantly positive carcinoma

markers, relying on lack of labelling for a diagnosis of

mesothelioma.1

In this current study, a panel of mesothelial-related

antibodies and carcinoma-related antibodies, as well as

two general epithelial antibodies was used (Table 1) and a

multivariate statistical analysis—which involves observa-

tion and analysis of more than one variable at a time while

taking simultaneously into account the effects of all variates

on the endpoint of interest—was carried out.

Therefore, thepresent study investigated a comprehensive

antibody panel jointly. Constructing regression trees may be

seen as a type of variate selection procedure. The aim is to

differentiate reliably between pleural epithelial MM and

secondary adenocarcinoma affecting the pleura. The objec-

tive was to ascertain which specific minimal set of markers

proved most reliable in making thedistinction between these

two malignancies and correlating with a diagnosis of MM.

This approachallows a rational decision for a minimal set of

antibodies as the primary line of investigation, which is

knowntomaximisethediagnosticyield.Thismayreducethe

cost of the investigation and the time required for the

pathologist to assess the sections—an inconvenient but

important rational consideration in a climate of ever

increasing pressures associated with time constraints and

costsavings.Inotherwords,thisapproachaimstomaximise

the number of definitive diagnoses that can be made based

Pathology (February 2009) 41(2), pp. 140–148

Print ISSN 0031-3025/Online ISSN 1465-3931 # 2009 Royal College of Pathologists of Australasia

DOI: 10.1080/00313020802579250

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on a specific limited panel of antibodies. If no definite

diagnosis is reached after using the specified antibody panel,

further immunohistochemical studies or other ancillary

studies including electron microscopy may then be carried

out as necessary.

MATERIALS AND METHODS

Histological samples

A data set of 200 consecutive cases of pleural malignancy, for which there

were comprehensive immunohistochemical data, was investigated. The

cases were sourced from routine surgical specimens submitted to the

Department of Anatomical Pathology at Flinders Medical Centre, South

Australia, and referral cases to one of the authors (DWH). There were 173

cases of definite epithelial MM and 27 cases of secondary adenocarcinoma.

The bias of mesothelioma cases was due to the nature of our referral

practice. This was taken into account in the statistical modelling approach

(see below).

The diagnosis of epithelial MM was defined according to currently

accepted morphological criteria,11,15–20including the radiological findings,

histological appearances, mucin histochemistry, immunohistochemistry

and, in many instances, electron microscopy, including ultrastructural

examination of deparaffinised tissue when available and when there

remained any doubt about a definite diagnosis. EM was used in

particular when the histological findings were atypical and/or whenever

immunohistochemistry yielded one major discordant result (e.g., positive

labelling with B72.3 in a suspected MM) or two or more minor

discordancies (e.g., equivocal staining for B72.3 and CD15 in a

suspected MM).

All tissues had been fixed in 10% buffered formalin and had undergone

standard processing and embedding in paraffin wax. Sections were cut 4 mm

thick, deparaffinised and rehydrated, if blocks had been received. For some

referral cases, only unstained spare slides were received (instead of a

paraffin block) and immunohistochemical studies were performed on those

cases as necessary. Some of the referral cases were sent including the

immunohistochemical preparations that were done in the source labora-

tory, but with insufficient spare slides and no paraffin blocks, such that only

limited immunohistochemical studies could be carried out. This accounts

for the missing data (see below). The panel of antibodies used included the

low molecular weight cytokeratin antibody CAM5.2 as a general epithelial

marker, antibodies against epithelial membrane antigen (EMA), five

mesothelial cell markers and five carcinoma-related markers (Table 1).

Antigen retrieval was individualised for each antibody, and incubation with

all primary antibodies was overnight (see Table 1 for details of dilutions of

antibodies, source and antigen retrieval used). For the first 69 cases

the streptavidin-biotin-peroxidase complex method was used (Ultra

Strepatavidin Detection System; Signet Laboratories, USA) as a detection

system, while for the remaining cases the DakoCytomation EnVisionþ

Dual Link System (Dako, Denmark) was used. This system was introduced

into the Department because it reduces problems with background staining

relating to endogenous biotin, particularly in small biopsies. The primary

antibodies were still used at the same dilution as before and the retrieval

TABLE 1Details of antibodies used, including source, antigen retrieval and dilutions

General Pattern of labelling

Antigen

retrievalDilution

Markers positive in malignant mesothelioma

Calretinin (Zymed) Currently regarded by many as the most sensitive

and specific marker for MM2,12,13,46

Accept only nuclear labelling

(þ/7 cytoplasmic);

patchy cytoplasmic may be

present in some carcinomas

Membrane labelling

Trypsin 1:500

CK5/6 (Chemicon) Positive in most epithelial MM (negative in most

adenocarcinomas), but positive in ovarian serous

carcinomas47–50

A protein expressed by some fetal tissues and adult

mesothelium; reportedly good sensitivity and

specificity for epithelial mesotheliomas;7,36,51–54

not usually reactive with renal cell carcinoma

Avoids missing an epithelioid

haemangioendothelioma

Raised from human mesothelial cell line, exact

antigen not known, appears to be associated with

microvilli; variably regarded9,12,24,52,55,56

Citric acid

pH6

1:100

WT-1 (Dako)

Nuclear labellingCitric acid

pH6

1:100

Thrombomodulin

(Dako)

HBME-1 (Dako)

Membrane labelling Trypsin1:400

Membrane labellingNo retrieval 1:15 000 Note:

data sheet

suggests

1:50–100

Markers positive in carcinoma

CEA (Zymed)Strong, diffuse, linear labelling supports diagnosis of

malignancy

Well characterised,12,30,34,35,37,38useful for

distinction from renal cell carcinoma (most are

positive)33,57

Complex glycoprotein expressed in breast Ca, well

established in literature10,11,26,31,58,59but variable

reports38,60

LewisYantigen, labels adenocarcinoma,5,7,9,32,61and

80% of squamous cell carcinomas,31no labelling

of renal cell carcinomas33

Very specific for primary lung carcinoma (or

carcinomas of thyroid follicular epithelium)62

Membrane þ/7 cytoplasmic

labelling

Membrane þ/7 cytoplasmic

labelling

No retrieval1:200

CD15 (Leu-M1)

(Dako)

No retrieval 1:50

B72.3 (kind gift

of Dr Cant)

Predominantly membrane

labelling

No retrieval1:4000

BG-8 (Signet)

Predominantly membrane

labelling

No retrieval1:200

TTF-1 (Dako)

Nuclear labelling Alkali

retrieval pH9

1:200

Others

EMA (clone E29)Membrane-bound glycosylated phosphoprotein

anchored to the apical surface of many epithelia62

Strong diffuse membrane

labeling supports dx of MM,

but cytoplasmic þ/7

membrane labelling in some

carcinomas

Membrane

No retrieval1:100

CAM5.2 (Becton-

Dickinson)

General epithelial marker62

Trypsin 1:100

TREE REGRESSION ANALYSIS FOR MESOTHELIOMA

141

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Page 4

systems remained unchanged. All sections were assessed independently by

two investigators (SK and DWH) and immunohistochemical labelling was

scored on a three-point ordinal scale: positive labelling¼1, equivocal

labelling¼0.5, no labelling¼0. Equivocal labelling was defined as positive

labelling in less than 2% of tumour cells or when it was uncertain whether

trace staining represented genuine labelling or simply high background

staining.

Statistical methods

Regression and classification tree-based modelling was applied in the

statistical data analysis. This is an explanatory technique for uncovering

structure in data, useful for both diagnostic and prognostic regression

problems.21,22In growing a regression tree, the process is continued until

the ‘terminal’ node is homogeneous enough, or it contains too few cases

(?5 by default). The model is fitted using a binary recursive partitioning

algorithm, whereby the data are successively split along coordinate axes of

the predictor variates (presence of labelling with different antibodies) so

that, at any node, the split that maximally distinguishes the response

variate (diagnosis of MM) in the left and the right branches is selected.

The decision to branch was based on whether a so-called deviance

statistically exceeded a cut-off value, which was chosen to optimise the

modelling.

Deviance measures the fit of a statistical model to the data when the

parameter estimation is likelihood-based, i.e., it is a measure of node

heterogeneity. The fit is inferred to be good when the deviance equals its

expected value which is the number of degrees of freedom (df), that is the

number of cases available for estimating the model minus the number of

model parameters. The deviance can also be employed to quantitate the

significance of individual antibody markers. This is achieved by computing

twice the log-likelihood of the ratio of the best model to that of the current

model.

The modelling approach followed was first to fit an overly complex tree,

and then ‘prune’ the tree down to a suitable size. The tree-construction

process has to be seen as a hierarchical refinement of probability

modelling. If the sample numbers are sufficiently large, the study yields

unbiased estimates of the disease classification probabilities (and mis-

classification rates). Any split which did not improve the model fit by a

default factor of the complexity parameter was pruned off by (10-fold)

cross-validations employed to ensure the numerical stability of the terminal

nodes.

An attractive property of the method is that at each node of a

classification tree there is a probability distribution over the classes. The

prediction probability available for each terminal node is constant, but

remains dependent on the structure of the tree (its depth). It follows that

the interpretation of this probability may not be exactly the same as the one

provided by the logistic (discriminant analysis) risk model, which was also

fitted to the data. The action taken for handling missing values was to

retain cases with partially unavailable marker values (because this most

closely represents the clinical situation where, for example in referral

practice, complete sets of immunohistochemical studies are not always

available). The method used surrogate rules if the splitting variate was

unavailable. The strategy was to pass a case down the tree as far as it will

go. If it reached a terminal node, a predicted probability of ‘caseness’

(mesothelioma or adenocarcinoma) was computed for it. Otherwise the

distribution at the node reached was employed to predict the outcome. This

is perhaps the single most useful feature of the applied modelling approach,

considering there were generally some (2–6%) missing values on the

antibody markers. For some markers the number of missing observations

was very large (over 70% for TTF-1) so as to render them practically

useless for the tree analysis.

In the application, the recursive partitioning method was used as

implemented in the rpart routine (http://mayoresearch.mayo.edu/mayo/

research/biostat/splusfunctions.cmf) of the S-PLUS system.23

We also evaluated the statistical efficacy of the investigated panel of

markers. For the malignant mesothelioma markers, we report sensitivity

(Se) and specificity (Sp) rates, whereas for the anti-markers or adenocarci-

noma markers we use their complement values (i.e., 1-Se and 1-Sp). The

cut-point for a classification was chosen based on the tree regression for the

individual markers, with scoring: 1¼positive, 0.5¼equivocal, 0¼negative.

The binary cut-point was decided by the algorithm, that is whether to align

the indeterminate score 0.5 with either 0 or 1.

In clinical practice, however, clear positive staining is required for a

result to be regarded as positive. In situations where this decision resulted

in a discrepancy with the algorithmic split, the computations were also

done with equivocal staining scored as negative. The clinical approach to

diagnosis has the effect of lowering the Se- and raising the Sp-values.

RESULTS

Sensitivity and specificity

Table 2 presents in the conventional way the results of the

sensitivities and specificities of the antibodies used. The

highest sensitivities of mesothelioma markers were ob-

served for calretinin (Se¼98%), while in the case of

adenocarcinoma markers, the highest sensitivity was for

CEA (1–Se¼100%, indicating that none of the mesothe-

liomas showed positive labelling for this antigen) and B72.3

(1–Se¼98%). The scoring of equivocal labelling (0.5) as

positive altered these results only marginally.

Logistic regression analysis

Table 3 presents the results of fitting a logistic model with

mesothelioma as the clinical end-point. The ‘intercept’ is

the log odds for the subpopulation with all the other terms

set equal zero. The null model with only the intercept term

had a deviance of 138 on 179 (¼200 – 20 – 1) df; 20 cases

were deleted due to missing values. When this deviance was

subtracted from the deviance of the updated model with

four additional significant parameters it yielded a residual

deviance of 20 on 175 (¼179 – 4) df. This indicates a very

good model fit as the value of the deviance statistic is much

less than its expected value, the df. The identified

set included the positive marker calretinin plus three

carcinoma-related markers which generally fail to label

MMs (with negative coefficients, indicating that lack of

labelling would support a diagnosis of mesothelioma)

TABLE 2

chemical markers for epithelial mesothelioma and adenocarcinoma, based

on 173 cases of epithelial mesothelioma and 27 cases of adenocarcinoma

Individual sensitivity (Se) and specificity (Sp) of immunohisto-

Epithelial marker Se (%)Sp (%) Total no. tests

CAM 5.2100.0 0.0194

Mesothelioma markerSe (%)Sp (%)Total no. tests

Calretinin

CK 5.6

EMA

HBME-1

Thrombomodulin

WT-1

98.2 (95.2)

96.6

90.9 (86.0)

89.2 (86.7)

89.6 (79.1)

77.8

81.5 (85.2)

57.9

7.7 (15.4)

76.0 (80.0)

56.0 (68.0)

88.9

194

137

190

191

188

126

Adenocarcinoma marker1–Se (%)1–Sp (%) Total no. tests

B72.3

BG8

CD15

CEA

Ber-EP4

TTF-1

98.2 (98.8)

83.2

68.2

100.0

82.4

92.9

54.2 (50.0)

88.5

73.1

63.0

83.3

52.9

187

193

196

193

57

59

Discrepant percentages resulting from scoring equivocal marker staining as

negative diagnosis are given in parentheses.

142

KLEBE et al.Pathology (2009), 41(2), February

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Page 5

for epithelial MM. The change in deviance, that is

138720¼118 with four df, proves that the four predictors

jointly form a statistically significant panel of antibodies.

The sensitivity of the logistic model to predict MM was

almost 99%, while the specificity was 88%.

Regression and classification tree analyses

Classification tree is the most common use of tree-based

methods whose end-point is a factor giving the diagnostic

categorisation of the patients. It is also possible to

construct regression trees in which the terminal node gives

the predicted (numerical) value. The ideas for classification

and regression tree structure are quite similar, but the

terminology differs. The justification for regression analysis

is that it serves as the basis for the classification tree-

construction procedure, so we consider it first. Table 4

presents a regression tree-based analysis of antibody

combinations for predicting the clinical outcome of the

200 patients (no cases were excluded in the final model

because of no missing values in these four markers). At the

root (i.e., top node of the tree) the predicted probability of

MM (coded as a binary outcome variable) was 86.5%. This

percentage represents the actual relative frequency of

mesothelioma cases in the combined study series. Starting

from this empirical setting, the ranking of the markers in

the order of their improvement of the deviance in

separate primary splits was the following: (1) calretinin,

(2) CD15, (3) BG8, (4) B73.2, and (5) HBME-1. The data

partition ended up in four terminal nodes defined by the

three markers calretinin, CD15 and HBME-1. To be 99%

sure of a mesothelioma end-point, the model established

that the following three conditions were sufficient (split

wise probability in parenthesis): calretinin40 (97%),

CD15¼0 (99%). In the leftmost branch, a case correlated

almost certainly with a ‘not MM’ diagnosis, based on the

indications of calretinin¼0 and HBME-151. The predic-

tion based on regression tree was almost the same as with

logistic regression. These results can be explained by the

fact that calretinin was so important a factor that it

dominated the role of the other antigens. In a less clear

circumstance, the different approaches would probably give

a more discrepant outcome.

Table 5 presents the corresponding classification tree-

based analysis for a differential diagnosis. With an equal

prior probability distribution (0.5, 0.5) for MM and

adenocarcinoma cases (defined as categorical outcome

variable), the branching terminated in a structure depicted

in Fig. 1 with four terminal nodes. In the selection of three

markers, the predominant role of calretinin remained the

same, but now CD15 complemented BG8, both of which

are adenocarcinoma-related markers (‘negative’ MM mar-

kers). The model predicted probability of MM diagnosis

based on the three antibodies was estimated to be 100%

(correct). We note parenthetically that using the priors

proportional to the empirical class counts produced a

classification tree very similar to the regression tree of

Table 3. Therefore, the bias towards MM cases in our

collection of cases did not affect the validity of the

modelling process.

DISCUSSION

Comparison of the findings in this study with those in the

published literature

Although many studies have evaluated the differential

diagnostic value of antibody sets in the differential

diagnosis of epithelial MM versus adenocarcinoma, no

TABLE 5

antibodies for a diagnosis of epithelial mesothelioma

Classification tree-based analysis of the deviance of multiple

Order and

criterion of

node partition

No.

cases

Prediction

misclassification*

rate (%)

Probability* of

mesothelioma

caseness (%)

Terminal

node{

0, Root node

1 L, calretinin¼0

1 R, calretinin40

2 L, BG840

3 L, CD1540

3 R, CD15¼0

2 R, BG8¼0

200

26

174

32

50 50/50

97.2/2.8

15.9/84.1

54.3/45.7

82.8/17.2

35.8/64.2

0/100

8.9

10.6

48.8

33.0

28.6

0

{

7

{

{

{

25

142

*Probability means relative frequency (empirical or theoretical), as in ‘the

clinically observed relative frequency of mesothelioma cases in our series of

patients with pleural malignancies was 0.865’ or ‘the model predicted

probability of mesothelioma diagnosis based on the three antibodies was

estimated to be 100% (correct), that is the misclassification rate (frequency

in the particular patient population experience) was zero’.

{Terminal node.

L, left branch; R, right branch.

TABLE 3

diagnosis of epithelial malignant mesothelioma

Logistic regression analysis of multiple antibodies for the

Regression

term

Standard

error

Statistical significance

Estimatet valuep value

Intercept

Calretinin

BG.8

B72.3

CD.15

2.02

4.53

1.21

1.63

1.99

1.62

1.68

1.62

2.77

0.11

0.006

0.026

0.017

0.030

74.47

73.55

74.05

72.25

72.19

72.41

TABLE 4

antibodies for the diagnosis of epithelial malignant mesothelioma

Regression tree-based analysis of the deviance of multiple

Criterion of partition

for nodes of the

regression tree

No.

cases

Deviance

statistic

within

the node

Predicted

probability*

of caseness (%)

Terminal

node{

Top node of the tree 20023.486.5

Left branch

Calretin¼0

HBME-151

HBME-1¼1

Right branch

Calretin40

26

19

3.4

0.9

1.7

15.4

5.3

1.7

{

{

7

174 4.997.1

Left branch

CD15409 2.088.7

{

Right branch

CD15¼0 1652.098.8

{

*Probability at the top of the tree equals the relative frequency of

mesothelioma cases in the series of patients with pleural malignancies.

{Denotes a terminal node.

TREE REGRESSION ANALYSIS FOR MESOTHELIOMA

143

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Page 6

conclusive shortlist of antibodies has been identified to

date, and there is no consensus on the optimal antibody

panel. There are numerous problems with the published

series available: in the directly comparable series, the case

numbers have not always been large. For example,

one study that concluded that the sensitivity of the

mesothelioma-related antibody HBME-1 for malignant

mesothelioma was 100%24based its findings on a series

of only 17 cases. Other studies have attempted meta-

analysis of all published studies, but this approach suffers

from the inability to control the quality of the primary

data. Such difficulties include the diversity of material

included, with cytology specimens, autopsy material and

surgically removed tissue being compared indiscriminately.

Furthermore, different studies have used different sources

and dilutions of antibodies, different antigen retrieval

techniques and different secondary antibodies. Different

researches have followed different criteria for assessing

positive labelling, and so forth.9Some of these difficulties

are particularly apparent with two of the antibodies that we

found to be valuable in the diagnosis of MM, namely

HBME-1 and calretinin.

With regard to calretinin, our results confirm some of the

observations made by other investigators. A considerable

number of studies over recent years have identified

calretinin as a particularly useful marker, with a number

of studies identifying 100% of MM cases as positive for

calretinin.13,25–28This may be in part due the fact that we

restricted our carcinoma group to adenocarcinomas, and

we applied strict criteria for assessing positive labelling, in

that positive labelling of nuclei was required for a result to

be designated as positive, whereas some previous publica-

tions have accepted cytoplasmic staining only, resulting in

higher numbers of lung adenocarcinomas labelling with

calretinin.2,28Like us, other investigators have found high

specificity for mesotheliomas, if nuclear labelling was

required for a positive result, irrespective of cytoplasmic

staining.7It is worth noting that the same clone of antibody

was used for all of our cases (Table 1). Previously, one

group29considered calretinin to be ‘useless’ when a

Chemicon guinea pig antibody was used but when a

different antibody was used conceded that it showed ‘the

highest sensitivity for mesothelial cells’.29

The high sensitivity and specificity we found with

calretinin may of course in part be related to the selection

of our cases: some tumours known to show positive

labelling for calretinin, such as squamous cell carcinoma

of lung origin and which may label for calretinin in up to

40% of cases,30,31were not included in our study which

assessed only the value of antibody panels for the

distinction between mesothelioma and adenocarcinomas

metastatic to the pleura.

The diagnostic utility of calretinin, among other markers,

was reviewed in two recent publications,4,12which were

based essentially on the same published reports and data,

and although both studies drew slightly different conclu-

sions in their assessment of diagnostic usefulness of this

antibody, they both regarded calretinin as a ‘useful’ positive

mesothelioma marker. Interestingly, one of those studies

then concluded by recommending panels of antibodies

‘based on their sensitivities and specificities’ but, like so

many other studies, not taking into account the complex

relations between antibody reactions. In contrast to those

papers, which attempted to analyse data collected over

many years, from many different laboratories and assessed

and scored by many different investigators, our study

enrolled 173 consecutive cases of epithelial MM and

applied a recursive partitioning algorithm to select a

probabilistically founded array of reliable correlative

markers for the type of malignancy present. There is only

one other study that attempted standard multiple logistic

regression analysis, which was performed on various

indicator combinations.2That analysis pinpointed calreti-

nin as an important marker, and can thus be compared

with a selected list of markers in our corresponding logistic

analysis. It is reassuring that our more sophisticated

statistical approach confirms the role of calretinin as a

useful positive marker for the differential diagnosis between

epithelial mesothelioma and adenocarcinoma. In fact, in

our series calretinin emerged as the premier correlate, and

this had the effect of rendering marginal the supplementary

information provided by the two next-best selected markers

BG8 and CD15.

BG8 has been found useful in the distinction of

adenocarcinoma from epithelioid MM.5,9,32In a study

investigating 12 antibodies and using regression analysis,

BG8 was found to be one of the three most useful

antibodies.7However, in contrast to our study cytology

samples were used, and the statistical approach used also

differed from the approach used by us, discussed in detail

below, with regards to calretinin. Nonetheless, it is

FIG. 1

assessment of epithelial mesothelioma, assuming equal likelihood of the

tumour representing mesothelioma or adenocarcinoma (0.5 likelihood of

being a malignant mesothelioma at the first node). Each of the following

nodes then provides the allocation ratio of cases as either tumour

(adenocarcinoma/mesothelioma) and provides the likelihood of a lesion

being a MM based on the immunohistochemical labelling. The inferior

nodes (ellipses) reached by those initial distinctions are further analysed by

left and right splits. The terminal nodes (rectangles) give the final

differentiation between the cases. The decimal figure in each node is the

predicted probability (ranging from 0 to 1) that an individual case

represents an epithelial MM. For example, if a tumour was negative for

calretinin at the first node, there was a 2.8% chance (probability of 0.028)

of the tumour being finally diagnosed as epithelial MM. Marker scoring: 1,

positive; 0.5, equivocal; 0, negative. The binary cut-point was decided by

the algorithm (i.e., whether to align the indeterminate score 0.5 with either

0 or 1).

Decision tree (based on Table 5) for the immunohistochemical

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reassuring that others have also proven the value of this

antibody.

Apart from the majority of adenocarcinomas, BG8 labels

80% of squamous cell carcinomas.31It does not label renal

cell carcinomas, which were not included in our study, and

this may in part account for the high sensitivity and

specificity we found with this antibody.33

CD15 has been established for over 20 years as a positive

adenocarcinoma marker for the distinction from mesothe-

lioma,34,35despite some authors reporting positivity in

up to 32% of malignant mesotheliomas.26,36Like us,

others have found that MM is only rarely positive for

CD15.12,30,37,38Again, the relative high sensitivity and

specificity for this antibody may be in part related to the

fact that we excluded squamous cell carcinomas, which are

generally not positive.39The majority of recent stu-

dies34,35,40have found CD15 to be undetectable in most

or all mesotheliomas investigated, while it was found to be

expressed in all or most adenocarcinomas, particularly

those of pulmonary origin.38It has been noted that CD15

expression is often focal and that false-negative reactions

can be related to small biopsies, and the high sensitivity and

specificity seen in our study may be related to the fact that

our study consisted predominantly of larger video-assisted

thoracoscopy (VAT) biopsies.40

Interestingly, the one study that used logistic regression

on a panel that included CD15 found other antibodies

more suitable for the diagnosis of adenocarcinoma, despite

high sensitivity and specificity of CD15.7As mentioned

above, that study used cytology samples and used an

antibody panel, which was heavily weighted towards

carcinoma-related markers with only one mesothelial-

related antibody (EMA with a membranous pattern of

staining). In addition, the statistical approach used also

differs from the approach used by ourselves: the authors

applied a stepwise discriminant logistic regression analysis

to select the ‘parameters’ of the model function for a

differential diagnosis between mesothelioma and adenocar-

cinoma cases, which is essentially a backwards selection

approach. In contrast, our study used a forward variable

selection approach and based the intercept on the distribu-

tion of diagnoses in our collection of cases.

Particular versus general case series

Our alternative classification or regression tree-based

analysis began at the root of the tree with an a priori

probability of a case being an epithelial mesothelioma case

at 0.865. From this starting-point, the graph set forth a

hierarchical construct for the correlative values of the

antibody probe markers calretinin, CD15, and BG8 or

HBME-1. However, the prevalence of mesotheliomas in

our series does not represent the relative frequency of

mesotheliomas versus adenocarcinomas in general clinical

practice (about seven secondary adenocarcinomas for every

mesothelioma).41On the other hand, not all cases of

metastatic carcinoma come to biopsy, due to the known

clinical history. We pooled all the adenocarcinoma cases,

regardless of primary tumour site, which for obvious

reasons does not provide a homogenous immunohisto-

chemical profile, but the scenario of a ‘malignant pleural

effusion secondary to metastatic carcinoma of unknown

primary site’ represents a common problem. This approach

to the unknown primary tumour resembles the true clinical

dilemma. One of the strengths of our study is that we used

only sections of the actual pleural deposits of secondary

carcinomas, as opposed to performing immunohistochem-

ical studies on the sections of primary carcinoma which has

been done in some other studies,42,43because there is

always a possibility that the immunohistochemical profile

for the pleural deposits is a little different from that of the

primary tumour.

Therefore, one may ponder whether the specified

assumption of prior probabilities of ‘caseness’ affects the

tree analysis qualitatively, or how much it changes the

quantitative values of the nodal markers. The rpart model

allows the specification of optional prior parameters. The

cases of equal priors (i.e., the probability of being a

mesothelioma or a adenocarcinoma is 0.5) produce a

different result, both in terms of the hierarchical ordering of

the markers and the magnitude of their prediction

probabilities. Basically, the statistical inference question is

whether the study aims to address a particularistic

(descriptive) problem or a general (causal) one.44In other

words, any such case series that come from hospital-based

and referral populations are somewhat place-specific and

time-specific. However, this is no different to the variability

between sensitivities and specificities between the same

antibody in the hands of different investigators.

Also, because the settings in particular places (say,

Adelaide, Australia versus Houston, Texas) are never

absolutely the same, it is not surprising that different

studies, using representative samples from the study

populations, can produce discrepant results and inferences.

Therefore, in investigating the problem of the role of the

various antibodies in the diagnosis of mesothelioma, the

relative sizes of the compared series is a matter of study

design, which the investigator can choose. Assuming an

equal (or ‘ignorance’) distribution is theoretically appro-

priate when little is known beforehand about the tumour in

question.

Stability of the statistical model

Both the logistic regression and the tree-based methods

identified the same three antibodies (calretinin, CD15 and

BG8) that can be used to diagnose or exclude epithelial

MM. However, the constructed tree can extract comple-

mentary information over the traditional regression analy-

sis. This is conveyed, especially, by the hierarchical

ordering of the included variates that lends itself to

evaluation of the marker’s predictive importance. The

basic rule is that the closer to the root node a factor

appears, the more important is its effect on the outcome.

Moreover, the dichotomous representation of markers

allows convenient characterisations of individuals falling

below or above a certain cut-point. On the other hand, the

relation between a marker and the outcome may not be

natural in the sense that the cut-point divides the group

essentially into two homogeneous subgroups regarding the

marker’s influence on the outcome. In these circumstances,

deciding on the existence of a suitable cut-point is

problematic. The recursive partition algorithm is a data-

driven procedure and as such it behaves, by and large, as a

black-box system concerning the choice of an adequate

cut-point. In the present study, the antibody labelling used

TREE REGRESSION ANALYSIS FOR MESOTHELIOMA

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only three score values. Therefore, the constructed tree is

likely to be stable.

Generally speaking, the process of model selection may

be viewed as a pattern-recognition process. To assert that a

model fits the data well using some appropriate criterion in

the ordinary sense, means that the data appear consistent

with the pattern predicted by the model.45Conversely, to

assert that the model fit is poor means that the data appear

to deviate appreciably from the model pattern. Model

correctness cannot be inferred from the fact that the fit of

the model to the data is good, since there are alternative

models that may also provide a good fit. A good fit is,

however, a necessary condition for inference.

In the context of predictive modelling, where the

objective is interpretation, given specific states of knowl-

edge, the function of ‘automatic’ procedures for the

selection of variates into a model (such as stepwise forward

selection in logistic regression) seems limited. In principle,

all simple models adequately fitting the original data set

(statistical validation) should be listed, and any choice

between them should be made on how successfully a model

performs on a new case series (clinical validation). Issues of

interaction between variates are handled implicitly; there is

no need to enter product terms in the model formula. The

questions are reduced to which variates to divide on, and

how to achieve the split. The justification for the tree-based

methodology is to view the tree as providing a probability

model. The decision on a split is made, based on a change

in a deviance measure for the tree under a likelihood

function that is conditioned on a fixed set of observed

random variates. Note that once fixed by observation, a

random variate is in no sense variable, so it might better be

called a correlative or predictive marker. The unknown

prediction probabilities are estimated from the proportions

in the split node. The tree construction process chooses the

split according to the maximum reduction in the deviance

measure. When the objective is prediction, classification

error rate is the appropriate criterion for judging any

particular model form.

Problem of overfitting predictive models

There are deficiencies in the standard modelling methods. It

is well known that analyses that are not pre-specified but

are data-dependent and liable to lead to over-optimistic

conclusions. Many applications involve a large number of

variates to be modelled using a relatively small patient

sample. In the case of very expressive models such as

regression trees, there is the danger that the models come

up with chance idiosyncrasies of the particular data, which

are not true in general. It is also important that the

minimum size of the terminal nodes is large enough.

Problems of overfitting and of identifying important

markers are exacerbated in predictive modelling, because

the accuracy of a model is more a function of the number of

events than of thetotal

indicates that a complex model is more likely to give

over-optimistic prediction when extensive variate selection

has been done.

A related problem of variable selection is multicollinear-

ity. We encountered a high correlation within clusters of

positive and negative mesothelioma antigens and vis-a ` -vis

the clusters. Thus, it was difficult to disentangle their

sample size.Experience

individual effects and impossible to identify a unique

solution for the regression tree. This means that correlated

markers, epitomised by r(CEA, WT-1)¼70.9, may be

essentially measuring the same underlying pathology or

construct. In statistical and predictive terms, they both

convey essentially the same information, although this may

obviously not be true in a biological sense.

Comparison to other statistical models

Our type of tree-modelling logistic regression analysis is

different from the standard multiple logistic regression

analysis by Carella et al.,2which was performed on various

indicator combinators. It is nonetheless reassuring that

their analysis also pinpointed calretinin as an important

marker, and their approach can be compared with a

selected list of markers in our corresponding logistic

analysis. As mentioned above, Dejmek and Hjerpe1applied

a backwards selection based regression analysis approach,

while our tree-construction process is essentially a forward

variable selection. We favour the forward selection

approach on intuitive grounds. Apart from the direction

of selection, the adjustment of the intercept parameter

(simply, the average proportion of mesothelioma) was

adjusted in a manner to represent the proportion in the

general population, instead of that in the study series.

An attractive property of our statistical approach is that

at each node of a classification tree there is a probability

distribution over the classes. The prediction probability

available for each terminal node is constant, but remains

dependent on the structure of the tree (related to its depth).

It follows that the interpretation of this probability may not

be exactly the same as the one provided by the logistic

regression analysis risk model, which was also fitted to the

data.

The action taken for handling missing values was to

retain cases with partially unavailable marker values. The

method used surrogate rules if the splitting variate was

unavailable. The strategy was to pass a case down the tree

as far as it will go. If it reached a terminal node, a predicted

probability of caseness was computed for it.

CONCLUSIONS

The factors underpinning the selection of antibodies in

large panels of antibodies for the distinction between MM

of epithelial type and metastatic adenocarcinoma are often

based on an individual antibody’s characteristics rather

than the behaviour of the whole panel. This study provides

a novel and unique approach to this situation, because

advanced tree-based partitioning methods are decidedly

different from customary regression methods for predicting

class membership on a binary response. Unlike any other

statistical approach applied to this problem previously, our

approach employs hierarchical modelling, with successive

predictions being applied to particular cases, to sort the

cases into homogeneous classes. Traditional methods use

simultaneous techniques to make one and only one

prediction for each and every case.

Our findings, based on a large prospective study

encompassing 200 consecutive pleural biopsies, indicate

that a panel consisting of three antibodies, namely

calretinin, BG8, and CD15, are jointly sufficient to

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accurately diagnose or exclude a case as a primary

epithelial MM in the majority of cases. This correlates

quite closely with the recommendation from the Interna-

tional Mesothelioma Panel that at least two mesothelial cell

markers and two carcinoma-related markers be used for the

immunohistochemical investigation of suspected epithelial

mesothelioma. However, we emphasise that this study

represents a statistical correlation using cases with an

established diagnosis of mesothelioma. We will now use the

three markers (calretinin, CD15, BG8) as a prospective

first-line approach to mesothelioma diagnosis and then

ascertain if the use of additional markers influences the

confidence index for a diagnosis of mesothelioma. How-

ever, this approach is only suitable for a differential

diagnosis of epithelial mesothelioma versus metastatic

adenocarcinoma. In clinical practice, often a much wider

differential diagnosis including epithelioid haemanagioen-

dothelioma and biphasic synovial sarcoma needs to be

considered, necessitating the use of additional antibodies.

We also emphasise that the findings in this study are valid

for our department, but because of the differences in

methodology for immunohistochemistry (for example

variable tissue processing, antigen retrieval, dilutions of

primary antibodies and different detection systems, even

when using the same antibodies produced by the same

manufacturer), other laboratories may record different

findings. Ideally, each laboratory should establish their

own optimal panel of markers for mesothelioma diagnosis.

Our rational and systematic approach to mesothelioma

diagnosis indicates that overly exhaustive panels of anti-

bodies may not improve the diagnostic confidence, and in

our laboratory we will proceed with the prospective study

of a limited panel of three markers, as indicated above. We

believe that this approach deserves wider application for

the immunohistochemicaldiagnosis of

tumours.

a variety of

Address for correspondence: Dr S. Klebe, Flinders Medical Centre,

Department of Anatomical Pathology, Flinders Drive, Bedford Park, SA

5042, Australia. E-mail: sonja.klebe@health.sa.gov.au

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