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Peter Moore 10/05/053 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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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
Peter Moore 10/05/05 3
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.
Peter Moore 10/05/05 4
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
Peter Moore 10/05/05 5
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)
Peter Moore 10/05/05 7
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
Peter Moore 10/05/05 9
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
Peter Moore 10/05/05 10
ROC Curve
• Receiver Operating Characteristic
• Probability of Detection
• Probability of False Alarm
Peter Moore 10/05/05 11
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
Peter Moore 10/05/05 12
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
Peter Moore 10/05/05 15
Prognostic groups C,D
Peter Moore 10/05/05 16
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
Peter Moore 10/05/05 17
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?
Peter Moore 10/05/05 18
More info
http://www.hep.man.ac.uk/u/peter/
http://ipcrs.hep.man.ac.uk
Peter Moore 10/05/05 19
Peter Moore 10/05/05 20