SCORING OF BREAST TISSUE MICROARRAY SPOTS

THROUGH ORDINAL REGRESSION

Telmo Amaral, Stephen McKenna

School of Computing, University of Dundee, Dundee, U.K.

Katherine Robertson and Alastair Thompson

School of Medicine, University of Dundee, Dundee, U.K.

Keywords:

Breast tissue microarrays, Scoring, Immunohistochemistry, Ordinal regression.

Abstract:

Breast tissue microarrays (TMAs) facilitate the study of very large numbers of breast tumours in a single

histological section, but their scoring by pathologists is time consuming, typically highly quantised, and not

without error. This paper compares the results of different classiﬁcation and ordinal regression algorithms

trained to predict the scores of immunostained breast TMA spots, based on spot features obtained in previous

work by the authors. Despite certain theoretical advantages, Gaussian process ordinal regression failed to

achieve any clear performance gain over classiﬁcation using a multi-layer perceptron. The use of the entropy

of the posterior probability distribution over class labels for avoiding uncertain decisions is demonstrated.

1 INTRODUCTION

Tissue microarrays (TMAs) are a high-throughput

technique proposed by Kononen (Kononen et al.,

1998), to facilitate the study of large numbers of tis-

sue samples on a single histological section; a TMA

section contains hundreds of small spots of tissue ar-

ranged in a grid-pattern. TMAs are now extensively

utilised in the study of cancers. TMAs are constructed

by taking cylindrical biopsies (named cores) from

donor blocks of formalin ﬁxed wax embedded tissue

(tumour or normal) and inserting them into a recipi-

ent wax block in a grid arrangement. Sections of the

TMA block are cut and provide targets for parallel

in situ detection of DNA, RNA, and protein targets

in each specimen on the array. Every TMA section

contains an array of spots of tissue, each spot being

a section of one of the cores previously inserted into

the microarray block. Consecutive sections allow the

rapid analysis of hundreds of molecular markers in the

same set of specimens on only a few histological sec-

tions. Camp (Camp et al., 2000) concluded that two

cores per patient are sufﬁcient to adequately represent

the expression of three common antigens in invasive

breast carcinoma.

Immunohistochemistry is carried out to detect

protein expression in the tissue spots. For example,

antibodies directed against progesterone receptor can

be used to detect nuclear expression of progesterone

receptor in breast tumours. Once immunohistochem-

istry is carried out, the assessment by pathologists of

the stained breast TMA sections starts with the clas-

siﬁcation of each tissue spot. In our experience, the

spots are usually one of several types, namely: tu-

mour, normal, stroma, fat, blood, or invalid (no spot

present or spot un-assessable). This initial classiﬁ-

cation must be carried out prior to assessing the im-

munostaining (level of expression of the protein of

interest, e.g. progesterone receptor) due to the fact

that the donor cores embedded in the TMA are not

always homogeneous throughout their length; there

may be tumour in the top third of the core, but the

remainder of the core may be stroma. Therefore, to

ensure correct analysis, each tissue spot on the TMA

section should ﬁrst be classiﬁed as to the type of tis-

sue present. The degree of immunostaining is then as-

sessed and assigned a score. Once all of the spots have

been scored, the scores can be compared. Applying

this procedure to breast TMA sections incorporating

large numbers of tissue samples is time consuming

and suffers from inter- and intra-observer variability,

perceptual errors, and severe quantisation that leads

to the loss of potentially valuable information. Thus,

there is strong motivation for the development of au-

243

Amaral T., McKenna S., Robertson K. and Thompson A. (2009).

SCORING OF BREAST TISSUE MICROARRAY SPOTS THROUGH ORDINAL REGRESSION.

In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications, pages 243-248

DOI: 10.5220/0001808202430248

 SciTePress

tomated methods for quantitative analysis of breast

TMA image data.

Most of the published work on automated “rank-

ing” of breast tissue sections aims not at pre-

dicting immunohistochemical scores, but rather at

distinguishing between different Bloom-Richardson

grades, given tissue sections stained solely with

Hematoxylin & Eosin. Petushi (Petushi et al., 2006)

used supervised learning (namely linear, quadratic,

neural network, and decision tree classiﬁers) to dis-

tinguish low, intermediate, and high grades of histol-

ogy slides, based on tissue texture parameters derived

from spatial information on cell nuclei distribution.

Axelrod (Axelrod et al., 2008) performed step-wise

forward Cox regressions with clinical and pathologi-

cal factors and image features describing nuclear mor-

phometry, densitometry, and texture, to distinguish

low, intermediate, and high worst grades. More re-

cently, Doyle (Doyle et al., 2008) used a support vec-

tor machine (SVM) to distinguish low and high grades

from digitised histopathology, based on textural and

nuclear architecture features. Additional recent work

on nuclear grading has been published by Chapman

(Chapman et al., 2007), Dalle (Dalle et al., 2008), and

Zhang (Zhang et al., 2008). In contrast, Kostopoulos

(Kostopoulos et al., 2007) applied k-nearest neigh-

bour weighted votes classiﬁcation to colour-textural

features, in order to predict the oestrogen receptor’s

(ER) positive status of biopsy images, traditionally

assessed via a scoring protocol that takes into ac-

count the percentage of epithelial nuclei that are im-

munopositive.

In this paper, we compare the results of ordi-

nal regression and classiﬁcation algorithms trained

to predict the immunoscores of breast TMA spots.

Ordinal regression differs from classiﬁcation in that

the existence of an order between the different cat-

egories is taken into account. So, in the prediction

of tumour scores, ordinal regression should in prin-

ciple achieve better results than classiﬁcation. We

trained neural network classiﬁers and ordinal regres-

sion algorithms based on Gaussian processes to pre-

dict the Quickscores (Detre et al., 1995) of breast

TMA spots subjected to progesterone receptor im-

munohistochemistry which results in nuclear staining

in positive cases. A Quickscore is composed of two

integer values, namely a value between 0 and 6 that

estimates the proportion of epithelial nuclei that are

immunopositive, and a value between 0 and 3 that es-

timates the strength of staining of those nuclei (these

values will henceforth be referred to as QSP and QSS,

respectively). In our experiments, each spot is charac-

terised by two features obtained in previous work by

the authors, derived from colour and texture features

of pixels, as summarised in section 2.

The remainder of this paper is organised as fol-

lows. Section 3 provides an overview of the data and

algorithms. Section 4 describes the experiments car-

ried out and presents their results. Section 5 discusses

the results and section 6 presents some conclusions

and recommendations.

2 PREVIOUS WORK

Our previous work included the classiﬁcation of

breast-TMA spots into two classes, as to the pres-

ence or absence of immunopositive epithelial nu-

clei (regardless of the type of spot) (Amaral et al.,

2008). The analysed data consisted of 110 spots (2

for each of 55 participants) subjected to progesterone-

receptor (PR) nuclear staining and whose immunos-

tates (positive or negative) were assigned by a pathol-

ogist. In addition, the contours of several hundred ep-

ithelial nuclei were marked within randomly selected

sub-regions and labelled as immunopositive or neg-

ative. In a ﬁrst stage, the pixels within annotated

sub-regions were used to estimate the likelihoods of

RGB and differential invariant features (computed for

two scales up to the 2nd order) for three classes,

namely: epithelial positive, epithelial negative, and

background. Assuming these features to be inde-

pendent, their likelihoods were then used to classify

the pixels of whole spots into the three considered

classes, using Bayes’ rule. In a second stage, the pre-

viously classiﬁed pixels were used to compute fea-

tures for each spot that aimed to formalise the two

Quickscore values assigned by pathologists. A gener-

alised linear model (GLM) was then trained to clas-

sify spots as to their immunostate, based on the two

computed features. A leave-2-out experiment was

carried out, in order to assess the ability of the sys-

tem to deal with data from new participants. Differ-

ent combinations of features were tested, leading to

the conclusion that the use of differential invariants in

addition to colour yielded a small improvement in ac-

curacy. The most favourable combination of features

resulted in a correct-classiﬁcation rate of 84%.

3 MATERIALS AND METHODS

3.1 Data

The data used in this work consist of two features

(extracted as described previously in section 2) char-

acterising each of 190 breast TMA spots of normal

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

244

or tumour tissue subjected to progesterone receptor

(PR) nuclear staining, along with the Quickscore val-

ues assigned to those spots by a pathologist. The digi-

tised TMA slides originate from the National Cancer

Research Institute’s Adjuvant Breast Cancer (ABC)

Chemotherapy Trial (Adjuvant Breast Cancer Trials

Collaborative Group, 2007).

3.2 Algorithms

Two types of neural networks were trained to classify

spots into their QSP and QSS values, namely single-

layer networks (also called generalised linear models,

or GLMs) and two-layer networks (also called multi-

layer perceptrons, or MLPs) (Bishop, 2006). The

GLMs were trained through the iterated re-weighted

least squares (IRLS) algorithm. The learning al-

gorithm used with the MLPs was scaled conjugate-

gradients (SCG) optimisation. For both types of net-

work, softmax was chosen as the activation function.

The Netlab (Nabney, 2002) implementations of the

GLM and the MLP were used.

For the prediction of QSP and QSS values through

ordinal regression, we employed the Gaussian process

techniques reported by Chu (Chu and Ghahramani,

2005), brieﬂy summarised in the following. Consid-

ering a data set composed of n samples, where the

ith sample is a pair of input vector x

∈ R

and target

∈ {1, 2, ..., r} (without loss of generality). Gaussian

processes assume each x

to be associated with an un-

observable latent function f (x

) ∈ R (a zero-mean ran-

dom variable), on which the ordinal variable y

in turn

depends. The process is speciﬁed by the covariance

matrix for the set of functions, whose elements can

be deﬁned by Mercer kernel functions. In this work,

we used two types of kernel, namely a linear kernel

and a Gaussian kernel, as deﬁned in equations 1 and

2, respectively.

Cov[ f (x

), f (x

)] = κ

∑

(1)

Cov[ f (x

), f (x

)] = κ

exp(−

∑

ς=1

− x

)

) (2)

Every Gaussian process has a number of hyper-

parameters that need to be optimised, such as κ

and

in the formulas above. In this work, for each

type of kernel, two Bayesian techniques were used for

hyper-parameter learning, here referred to simply as

maximum a posteriori estimate (MAP) and expecta-

tion propagation (EP). We used the Gaussian process

algorithms made available by Chu (Chu and Ghahra-

mani, 2005).

Along with the predicted score for each spot, both

the classiﬁcation and ordinal regression algorithms

output r real values that can be interpreted as the

posterior probabilities of the spot belonging to each

score. As discussed later in section 5, this type of out-

put proved to be useful.

The batch code used to run the experiments and

process their results was implemented in Matlab.

4 EXPERIMENTS AND RESULTS

Leave-one-out experiments were carried out to pre-

dict the QSP and the QSS values of the 190 available

spots, for the classiﬁcation and ordinal regression al-

gorithms described previously in section 3. For each

value type (QSP and QSS), two types of experiment

were carried out. In the ﬁrst case, the models were

trained to predict raw Quickscore values. These ex-

periments are referred to as Raw in table 1. In the

second case, the models were trained to predict col-

lapsed Quickscore values, obtained from the raw val-

ues as shown in equations 3 and 4. These experiments

are referred to as Collapsed in table 1. In addition, the

raw predicted values resulting from the ﬁrst case were

collapsed a posteriori, so as to be comparable with

those resulting from the second case. These modiﬁed

results are referred to as Raw c.a.p. in table 1.

QSP.collapsed











0 if v

QSP.raw

= 0

1 if v

QSP.raw

∈ {1, 2}

2 if v

QSP.raw

∈ {3, 4}

3 if v

QSP.raw

∈ {5, 6}

(3)

QSS.collapsed











0 if v

QSS.raw

= 0

1 if v

QSS.raw

∈ {1, 2}

2 if v

QSS.raw

= 3

(4)

Chu (Chu and Ghahramani, 2005) reported results

for various data sets and algorithms, to support the

comparison between algorithms. The result reported

for each experiment consists of the mean absolute er-

ror (i.e. the average deviation of the prediction from

the true target) over all test samples, along with the

standard deviation of partial mean absolute errors.

Each partial error is computed over the test samples

included in a given random partition of the data. For

each data set, a number of random partitions is de-

ﬁned. A standard deviation computed in this way,

however, has the disadvantage of depending on the

partitioning of the data (speciﬁcally, on the number

of test samples per partition). In our work, for each

experiment, we chose to compute the mean and stan-

dard deviation of the absolute error over all samples.

These values are presented in table 1, the best results

on each row being typed in boldface.

SCORING OF BREAST TISSUE MICROARRAY SPOTS THROUGH ORDINAL REGRESSION

245

Table 1: Means and standard deviations of the absolute errors yielded by the various experiments.

Target QS value Algorithm

Netlab Gaussian process ordinal regression

GLM MLP MAP EP

Lin. Gau. Lin. Gau.

P Raw 1.400 ±1.677 0.926 ±1.215 1.126 ±1.397 0.921 ±1.172 0.900 ±1.129 0.888 ±1.175

P Raw c.a.p. 0.774 ±0.935 0.516 ±0.733 0.626 ±0.805 0.537 ±0.702 0.500 ±0.680 0.503 ±0.698

P Collapsed 0.684 ±0.870 0.432 ±0.677 0.579 ±0.757 0.426 ±0.619 0.463 ±0.639 0.426 ±0.611

S Raw 0.937 ±1.097 0.763 ±0.988 0.937 ±1.106 0.784 ±1.003 0.800 ±1.025 0.779 ±0.994

S Raw c.a.p. 0.674 ±0.727 0.547 ±0.655 0.663 ±0.729 0.558 ±0.662 0.568 ±0.677 0.553 ±0.655

S Collapsed 0.589 ±0.626 0.495 ±0.589 0.526 ±0.606 0.495 ±0.561 0.489 ±0.589 0.489 ±0.561

For each experiment, besides the values reported

in table 1, a confusion matrix was computed. The

matrices for some of the experiments are shown in

Table 2.

0 0.5 1 1.5 2

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

entropy threshold

(66)-qs-s-c-be-gpor-ep--gau

proportion of spots

mean absolute error

a) QSS Collapsed, EP Gau

0 0.5 1 1.5 2

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

entropy threshold

(96)-qs-p-c-be-gpor-ep--gau

proportion of spots

mean absolute error

b) QSS Collapsed, EP Gau

Figure 1: Fraction of processed spots, and mean absolute er-

ror over those spots, versus conﬁdence threshold, for two of

the experiments (lower entropy means higher conﬁdences.

As mentioned previously in section 3, all of the

employed algorithms output, along with each predic-

tion, a posterior probability distribution over the r

targets. The entropy of a posterior distribution can

be used as a simple measure of classiﬁcation or or-

dinal regression conﬁdence (the lower the entropy,

the higher the conﬁdence). For two of the experi-

ments, ﬁgure 1 shows the fraction of test spots that

can be predicted below a given entropy threshold.

Also shown is the mean absolute error computed over

each fraction of spots.

5 DISCUSSION

Models trained to predict collapsed Quickscore values

consistently yielded better mean absolute errors than

models trained to predict the same Quickscores in raw

format (collapsed only a posteriori for the purpose

of comparison). This difference in quality of the re-

sults was also reﬂected in the confusion matrices. All

matrices for the prediction of raw Quickscores (QSPs

or QSSs, regardless of the algorithm) showed one or

two middle targets with zero predictions, but this ef-

fect was not observable in the matrices for the predic-

tion of collapsed Quickscores. To illustrate this, ta-

bles 2(a) and (b) show the matrices for the prediction

of raw and collapsed QSPs, respectively, via the EP

algorithm with Gaussian kernel; and tables 2(c) and

(d) show the matrices for the prediction of QSSs via

MLP and EP with Gaussian kernel, respectively. This

may indicate a lack of training examples for middle

targets, or inadequacy of the features used to charac-

terise TMA spots, or even that the number of scoring

ordinals used in practice is excessive.

The GLM algorithm performed poorly. Besides

yielding the highest mean absolute error in every ex-

periment, the prediction of collapsed QSPs yielded a

confusion matrix that showed a middle target with no

predictions, something that did not happen with any

VISAPP 2009 - International Conference on Computer Vision Theory and Applications

246

Table 2: Confusion matrices for some of the experiments.

(a) QSP Raw, EP, Gau (b) QSP Collapsed, EP, Gau.

Test Predicted

0 1 2 3 4 5 6

0 65 00 04 00 01 00 00

1 16 00 02 00 00 00 00

2 13 00 08 00 03 00 01

3 03 00 08 00 04 03 00

4 04 00 04 00 04 04 02

5 03 00 03 00 02 01 07

6 00 00 01 00 00 04 17

Test Predicted

0 1 2 3

0 59 11 02 00

1 21 15 06 01

2 03 09 18 07

3 01 03 06 28

Test Predicted

0 1 2 3

0 68 00 00 04

1 15 00 00 16

2 08 00 00 29

3 13 00 02 35

Test Predicted

0 1 2

0 52 20 00

1 16 28 24

2 06 21 23

Test Predicted

0 1 2 3

0 65 00 03 04

1 39 00 02 02

2 19 00 09 09

3 05 00 05 28

other algorithm. This matrix is shown in table 2(e).

Ordinal regression with EP and Gaussian kernel

could be said to be the best algorithm, based solely

on the mean absolute errors. It yielded an error that

was always either the lowest or very close to the low-

est. However, the large values of the absolute error’s

standard deviation shown in table 1 seem to render a

comparison between algorithms inconclusive.

It should also be noted that the MLP algorithm

performed surprisingly well, when compared with the

ordinal regression methods. This suggests that further

research to improve the ordinal regression method is

needed, given the expectation that formulating the tis-

sue scoring problem as ordinal regression should rep-

resent an advantage over classiﬁcation. A possibil-

ity would be to investigate modiﬁcations to the ordi-

nal regression algorithms that could model the way

in which pathologists mislabel the ground-truth. The

MLP also consumed a computational time per TMA

spot that is is at least one order of magnitude below

that taken by the ordinal regression (tenths of second

versus several seconds).

As the entropy threshold set on the predic-

tions was decreased (i.e., as the minimum conﬁ-

dence threshold is increased), the mean absolute error

tended to decrease, as exempliﬁed in Figures 1(a) and

(b). This suggests that it is possible to automatically

process, with quite low mean errors, reasonable frac-

tions of spots that are more unequivocal, while iden-

tifying the more difﬁcult spots that cannot dispense

with human assessment.

6 CONCLUSIONS

AND RECOMMENDATIONS

This paper compared the results of ordinal regres-

sion and classiﬁcation algorithms trained to predict

the scores of breast tissue sections. Purely in terms

of mean absolute errors, ordinal regression via EP

with Gaussian kernel yielded the best results in most

experiments, but the MLP classiﬁer’s performance is

practically at the same level. The reasons behind this

should be further investigated. In turn, GLM was

found to perform poorly. Models trained to predict

collapsed ordinal targets achieve considerably better

results than models trained to predict raw targets (col-

lapsed only a posteriori for comparison). It would be

interesting to further investigate this limitation, too.

By setting conﬁdence thresholds, it should be pos-

sible to use the methods discussed in this paper to

process reasonable fractions of spots with low mean

absolute errors. Future work should also investigate

how to take into account the costs of different kinds

of error (e.g. predicting a score of 2 as 1 should in

principle have a lower cost than predicting a 1 as 0),

and build those into the ordinal regression model.

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