Diagnosis of Ovarian Cancer Based on Mass Spectrum of Blood Samples

Committee:

Eugene Fink

Lihua Li

Dmitry B. Goldgof

Hong Tang

Outline

• Introduction

• Previous work

• Feature selection

• Experiments

Motivation

Early cancer detection is criticalfor successful treatment.

Five year survival for ovarian cancer:• Early stage: 90%• Late stage: 35%

80% are diagnosed at a late stage.

Motivation

Desired features ofcancer detection:

• Early detection

• High accuracy

• Low cost

Mass spectrum

We can detect some early-stage cancersby analyzing the blood mass spectrum.

ratio of molecular weight to electrical charge

inte

nsit

y

20,0000 5,000 10,000 15,000

10–4

10–2

100

102

Mass spectrum

Mass spectrum

Data miningResults

Blood

Outline

• Introduction

• Previous work

• Feature selection

• Experiments

Initial work

• Vlahou et al. (2001): Manual diagnosis

of bladder cancer based on mass spectra

• Petricoin et al. (2002): Application of

clustering to mass spectra for the ovarian-

cancer diagnosis

Decision treesAdam et al. (2002): 96% accuracy for prostate cancerQu et al. (2002): 98% accuracy for prostate cancer

Later work

Neural networksPoon et al. (2003): 91% accuracy for liver cancer

ClusteringPetricoin et al. (2002): 80% accuracy for prostate cancer

Outline

• Introduction

• Previous work

• Feature selection

• Experiments

Feature selection

ratio of molecular weight to electrical charge

inte

nsit

y

200 400 600

CancerHealthy

2 21 2 1 2/ Statistical difference:

Feature selection

ratio of molecular weight to electrical charge

inte

nsit

y

200 400 600

Window size: minimal distance between selected points

CancerHealthy

Outline

• Introduction

• Previous work

• Feature selection

• Experiments

Data sets

Dataset

Number of cases

Cancer Healthy

1

2

3

100

100

162

116

116

91

Learning algorithms

• Decision trees (C4.5)

• Support vector machines (SVMFu)

• Neural networks (Cascor 1.2)

Control variables

• Number of features, 1–64

• Window size, 1–1024

Best control valuesDecision trees

Data set

Number of

features

Window

size

Accuracy

1 4 1 82%

2 8 4 94%

3 8 64 99%

Best control valuesSupport vector machines

Data set

Number of

features

Window

size

Accuracy

1 32 16 83%

2 4 2 94%

3 16 8 99%

Best control valuesNeural networks

Data set

Number of

features

Window

size

Accuracy

1 32 256 82%

2 32 1 96%

3 16 2 99%

Learning curveData set 1

accu

racy

(%

)

training size

90

80

60

100

70

Decision trees, SVM, Neural networks

50 100 150 200 250

accu

racy

(%

)Learning curveData set 2

training size

90

80

60

100

70

Decision trees, SVM, Neural networks

0 50 100 150 200 250

Learning curveData set 3

accu

racy

(%

)

training size

50 100 150 20060

70

90

80

100

0

Decision trees, SVM, Neural networks

250

Main results

Automated detection of ovarian cancer by

analyzing the mass spectrum of the blood

• Experimental comparison of decision

trees, SVM and neural networks

• Identification of the most informative

points of the mass-spectrum curves

Future work

• Experiments with other data sets

• Other methods for feature selection

• Combining with genetic algorithm