Peter Moore 10/05/05 1

ANN survival prediction for cancer patients

Peter Moore

High Energy PhysicsUniversity of Manchester

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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

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0.2

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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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