國立雲林科技大學 National Yunlin University of Science and Technology Intelligent Database...

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國立雲林科技大學National Yunlin University of Science and Technology

Intelligent Database Systems Lab

Self-organizing map for cluster analysis of a breast cancer database

Mia K. Markey, Joseph Y. Lo, Georgia D. Tourassi, Carey E. Floyd Jr.Artificial Intelligence in Medicine 27 (2003) 113–127

Advisor : Professor Chung-Chian HsuReporter : Wen-Chung Liao

2006/5/17

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Outline

Motivation Objectives Data Methods Results Discussion Comments

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Motivation The decision to biopsy is complicated

breast cancer can present itself in a variety of ways on a mammogram

considerable overlap in the appearance of benign and malignant lesions.

Unsupervised learning may provide an alternate avenue to a priori knowledge for identifying subsets in the data that should be handled separately in the development or evaluation of computer-aided diagnosis or detection systems.

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Objectives The purpose of this study was to identify and

characterize clusters in a heterogeneous breast cancer computer-aided diagnosis database. Identification of subgroups within the database

could help elucidate clinical trends and facilitate future model building.

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Data available data:4435

model development: 2258 (982 malignant) 751 suspicious breast lesions at Duke Univ. Med. Center

mammographers described each case using BI-RADS lexicon each of the cases was read by one of seven readers :six BI-RAD

S features and the patient age. 260 (35%) were malignant.

501 from Univ. of Penn. Med. Center, 200 (40%) were malignant. 1006 lesions randomly selected from the Digital Database for Scr

eening Mammography, 522 (52%) were malignant. model validation: 2177 The overall malignancy fraction was 43%.

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Methods Self-organizing map

using the SOM toolbox in MATLAB 2-D grid of 4 x 4 neurons, but different configurations were

considered. Features standardization seven input features, the biopsy outcome was not provided to the

SOM Constraint satisfaction neural network (CSNN)

determine the profiles of the clusters 1000 iterations, weights determined by auto-BP Each category of BI-RADS features corresponded to a binary

variable and associated neuron. the mass margin with its five non-zero categories : five

separate neurons Patient age : five levels (<40, 40 x< 50, 50 x<60, 60 x<70, 70 )≦ ≦ ≦ ≦

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Methods Back-propagation artificial neural network (BP-ANN)

predict the biopsy outcome from the mammographic findings and patient age.

Single hidden layer of 14 neurons Logistic activation function Input: 6 BI-RADS features and age One output node to indicate malignancy

ROC curves show the trade-off in sensitivity and specificity achievable by a

classifier by varying the threshold on the output decision variable sensitivity=t_pos/pos, specificity=t_neg/neg

The area under the ROC curve is often used as a measure of classifier performance.

Only techniques with high sensitivity would be acceptable. 98% sensitivity.

Error

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ROCRf: Fawcett (2006). An introduction to ROC analysis. Pattern Recognition Letters 27.

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Results

Fig. 2

Fig. 3(d)

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Results BP-ANN predict the biopsy outcome The SOM can do a malignancy

prediction For example, if a case belonged to

cluster #4, then the classifier output for that case would be 0.83.

Fig. 6 shows the ROC curve for the BP-ANN and SOM.

The performance at the highest sensitivities was comparable.

In particular, at 98% sensitivity the SOM operates with 0.26±0.03

specificity the BP-ANN operates with 0.25±0.03

specificity (P=0.93).

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Results Fig. 7 lists the BP-ANN’s recomme

ndations for follow up instead of biopsy on the subsets identified by the SOM (320 cases)

A threshold was applied to the BP-ANN outputs such that sensitivity = 98% (965/982) specificity = 24% (303/1276). In other words, 320 cases (303 act

ual negatives and 17 actual positives) fell below the threshold.

The majority of the benign lesions that the BP-ANN would have spared biopsy (242/303=80%) were in the cluster defined by neuron #6.

False NegativeTrue Positive

False Positive True Negative

965 17

303971

Fig. 7

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Results A classification rule based on the cluster

profiles (Figs. 4 and 5) of neuron #6 and a classification and regression tree (CART)

The classification rule was: if the mass margin was well-circumscribed or

obscured and the age was less than 59 years and there were no calcifications, associated findings, or special findings, then do not biopsy, otherwise do biopsy.

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Results On the 2258 training cases,

this rule gave 961/982=98% sensitivity and 336/1276=26% specificity.

this rule performed comparably to the BP-ANN with a threshold of 0.1842 (965/982=98% sensitivity, 303/1276=24% specificity).

On the validation set, the classification rule gave 886/904=98% sensitivity and

339/1273=27% specificity the BP-ANN with a threshold of 0.1842 gave 884/904=98%

sensitivity and 296/1273=23% specificity. Thus, both the BP-ANN and the rule-based

approach generalized and they performed comparably at this high sensitivity point.

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Discussion Considerable variability from cluster to cluster. One of the major goals of computer-aided diagnosis

of breast cancer is to identify very likely benign cases, in order to reduce the n

umber of benign biopsies. It is possible to use the clusters and their malignanc

y fractions directly as a tool for predicting biopsy outcome.

The SOM prediction method, similar to a case-based reasoning system.

the SOMs with similar architectures showed substantial agreement in clustering the data.

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Discussion The SOM prediction method in conjunction wi

th the CSNN profiling method has the potential advantage physicians may understand the intuition the BP-ANN, which is often seen as a ‘‘black box’’.

The successful separation of a priori known, coarse lesion types (masses, clustered microcalcifications, focal asymmetric densities, and architectural distortions) provided some quality assurance of the clustering.

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Discussion Classification based on the SOM was competitive to that

achieved by the BP-ANN at high sensitivity levels (Fig. 6). the SOM clustering and CSNN profiling technique could be used

to provide the physician with an alternative description of what the BP-ANN does for certain types of cases.

The identification of a single cluster that accounted for the majority of the cases that the BP-ANN would have recommended for follow up also suggests the investigation of rule-based methods to identify relatively simple diagnostic criteria which might be applied to these cases to aid the radiologists in their decision making process.

Based on the profiles of the clusters (#6) identified by the SOM, a simple classification rule was developed and performed comparably to the BPANN

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Comments Advantage:

Divide and conquer approach A good classification rule A good example of knowledge discovery

Disadvantage high false positive rate: 971/1276=0.76 Low accuracy: (965+303)/2258=0.56

Solution: Remove cluster #6, then repeat the divide and conquer approach?

Such a research depends on domain knowledge heavily.

國立雲林科技大學 National Yunlin University of Science and Technology Intelligent Database...

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