diagnosis of ovarian cancer based on mass spectrum of blood samples
DESCRIPTION
Diagnosis of Ovarian Cancer Based on Mass Spectrum of Blood Samples. Hong Tang. Committee: Eugene Fink Lihua Li Dmitry B. Goldgof. Outline. Introduction Previous work Feature selection Experiments. Motivation. Early cancer detection is critical for successful treatment. - PowerPoint PPT PresentationTRANSCRIPT
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Diagnosis of Ovarian Cancer Based on Mass Spectrum of Blood Samples
Committee:Eugene Fink
Lihua LiDmitry B. Goldgof
Hong Tang
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Outline
• Introduction
• Previous work
• Feature selection
• Experiments
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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.
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Motivation
Desired features ofcancer detection:
• Early detection
• High accuracy
• Low cost
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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
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Mass spectrumMass spectrum
Data miningResults
Blood
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Outline
• Introduction
• Previous work
• Feature selection
• Experiments
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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
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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
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Outline
• Introduction
• Previous work
• Feature selection
• Experiments
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Feature selection
ratio of molecular weight to electrical charge
inte
nsity
200 400 600
CancerHealthy
2 21 2 1 2/ Statistical difference:
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Feature selection
ratio of molecular weight to electrical charge
inte
nsity
200 400 600
Window size: minimal distance between selected points
CancerHealthy
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Outline
• Introduction
• Previous work
• Feature selection
• Experiments
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Data sets
Dataset
Number of cases Cancer Healthy
123
100100162
116116 91
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Learning algorithms
• Decision trees (C4.5)
• Support vector machines (SVMFu)
• Neural networks (Cascor 1.2)
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Control variables
• Number of features, 1–64
• Window size, 1–1024
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Best control valuesDecision trees
Data set
Number of features
Window size
Accuracy
1 4 1 82%2 8 4 94% 3 8 64 99%
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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%
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Best control valuesNeural networks
Data set
Number of features
Window size
Accuracy
1 32 256 82%2 32 1 96% 3 16 2 99%
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Learning curveData set 1
accu
racy
(%)
training size
90
80
60
100
70
Decision trees, SVM, Neural networks
50 100 150 200 250
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accu
racy
(%)
Learning curveData set 2
training size
90
80
60
100
70
Decision trees, SVM, Neural networks
0 50 100 150 200 250
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Learning curveData set 3
accu
racy
(%)
training size
50 100 150 20060
70
90
80
100
0
Decision trees, SVM, Neural networks
250
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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
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Future work
• Experiments with other data sets
• Other methods for feature selection
• Combining with genetic algorithm