peter moore 10/05/051 ann survival prediction for cancer patients peter moore high energy physics...
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Peter Moore 10/05/05 1
ANN survival prediction for cancer patients
Peter Moore
High Energy PhysicsUniversity of Manchester
Peter Moore 10/05/05 2
Project Overview
• Funded by MRC• And PPARC…… me
• Collaboration:– HEP at University of Manchester
• ANN and Software development• GRID security
– Ninewells Hospital Dundee.• Data• Clinical expertise
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Main Aims
• To set up ANN based on several available DBs to predict the probable survival outcome for the patients suffering with breast or colorectal cancers
• Make the ANN available via secure Internet access (GRID) for clinicians nationwide
• Investigate the possibilities of designing better management plans and improving cancer patients quality of life after treatment.
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Data
• Colorectal and Breast Cancer Patients
• Sets of records do not share parameters• 50,000 records, 100+ variables
• Data inconsistency • Noise
• Missing or incomplete data• Filling by hand leads to errors
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Artificial Neural Networks
• Mathematical model based on neurons
• Many variations• Multilayer Feed
Forward ANN
• Approximate any function
Inpu
ts xi
xi wj
w1
w3
w2
wj
Input summator
Nonlinear converter
Output
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General Methodology
1. Forming a training set adequately describing the survival function.
2. Tuning the synapse weights (training).
3. Testing.
4. Evaluating and Validating
5. Recommendation for patient management plan.
Training set
Selecting & coding
Genetic Algorithm (global estimation)
Gradient based Alg. (local improvement)
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Our Methodology
• PLANN
• Cascade Architecture
• Scaled Conjugate Gradient training algorithm
• 200 times bootstrap re-sampling
1
j
0
time
J
bias
H
1h
i1
ih
iH
K
HK
hK
1K
0
K
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Results analysis
• Separate (unseen by ANN) records• Known as a validation set
• Interpreting the ANN outputs– Individual patient testing– Group testing
• Cancer management
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Individual Patient Results
ANN predicted probabilty of survival
0
0.5
1
0 60
Months
Prob
abili
ty o
f 60
mon
th S
urvi
val
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ROC Curve
• Receiver Operating Characteristic
• Probability of Detection
• Probability of False Alarm
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Kaplan Meier Survival
• Standard method used in medicine
• Actual Survival probability for any group of patients
• Grouping patients together by specific diagnostic factors
• Takes into account censoring
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Kaplan Meier Example
KP-M plot of survival in scotland
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60 70 80 90 100
Age (Years)
Prob
abilit
y of
sur
viva
l
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Prognostic groupings Colon Cancer
• A : Dukes Stage A, node negative, no liver deposits and curative operation
• B : Dukes Stage B, node negative,no liver deposits and potentially curative operation
• C: Dukes Stage C, no liver deposits and potentially curative operation
• D: Dukes Stage D, multiple lymph node involvement or hepatic deposits
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Prognostic groups A, B
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Prognostic groups C,D
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Visions
• Web interface• Accessible by medical personnel
• Improved Data• New Databases sources
• Patient management profiles• Requires improved hospital patient data collection
methods• Medical trials data• Genome and Molecular data
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Visions
• Online Dynamic ANN training?• Continuously updates with latest research results and
data – ( would currently fail ethics approval )
• Automatic relevance determination– Problems with reliability of unsupervised ANN training
• Remote data uploading• Confidentiality and Enforcement of privacy protection• Security
• Healthgrid?
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More info
peter@hep.man.ac.uk
http://www.hep.man.ac.uk/u/peter/
http://ipcrs.hep.man.ac.uk
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