diagnosis of ovarian cancer based on mass spectrum of blood samples

Diagnosis of Ovarian Cancer Based on Mass Spectrum of Blood Samples

Committee:Eugene Fink

Lihua LiDmitry 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

nsity

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

10–4

10–2

100

102

Mass spectrumMass 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

nsity

200 400 600

CancerHealthy

2 21 2 1 2/ Statistical difference:

Feature selection

ratio of molecular weight to electrical charge

inte

nsity

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

123

100100162

116116 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 byanalyzing 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