hazlina hamdan 31 march 2009 1 modelling survival prediction in medical data by hazlina hamdan dr....

Hazlina Hamdan31 March 2009

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Modelling survival prediction in medical data

By Hazlina HamdanDr. Jon Garibaldi


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Presentation Content Introduction

Cancer and Breast Cancer Medical Prognosis Survival Analysis

Research Background Aims & Objectives

Understanding Previous Approach Artificial Neural Network PLANN

Analysis and Results Conclusion Future Work


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Cancer Cancer is basically a disease which occurs when

cells behave abnormally and divide out of control – form visible mass or tumour.

There are two general types of tumours namely: benign malignant

Breast cancer is the most common cancer causing fatality amongst women.

Breast cancer is a malignant tumour that develops from the uncontrolled growth of cells in the breast.


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Medical Prognosis The principal factor in estimating of cure,

complication, disease recurrence or survival for a patient or group of patients after treatment.

Prognosis is important because the type and intensity of the medications are based on it.

Prognosis is only a prediction.


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Survival Analysis The analysis of data that corresponds to the time

from when an individual enter a study until the occurrence of some particular event or end-point.

Concerned with the comparison of survival curves for different combinations of risk factors.

Data contains uncensored (reach until end point) and censored (lost to follow-up or die from unrelated cause) observations.


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Survival Analysis – Survival Function

Probability an individual survive at least up to a certain time t.

S(tl)=P(T≥t)

Kaplan-Meier survival curve.

Cumulative Proportion Surviving (Kaplan-Meier)Complete Censored

Stage 1 Stage 2 Stage 3 Stage 40 1 2 3 4 5 6 7 8 9 10

Time

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Cum

ulat

ive

Prop

ortio

n S

urvi

ving


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Probability an individual will die at a certain time, conditioned on survival up to that time, and denotes the instantaneous death rate. (Collet D., 1994) hl = P(T Є Al|T>tl-1) = fl /S(tl-1)known also as conditional failure probability

Survival and Hazard function are related to each other

S(t)=∏(1-hl) l:tl≤t

Survival Analysis – Hazard Function


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Research Objectives Understand previous approaches. Apply previous approaches to our data. Develop novel approaches based on Artificial

Neural Network (ANN) and Fuzzy method. In clinical perspective is to assist doctor in

predicting survival of individual patients and planning future treatments.


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Previous ApproachArtificial Neural Network

(ANN) Artificial Neural Network (ANN) is defined as an

information processing system inspired by the structure of the human brain.

ANN gathers its knowledge by detecting a common pattern and relationships in raw data, then learning from such relationships and adapting the results as required.

The knowledge is then used to predict the outcome for new combinations of data.


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Previous Approach-Feed-Forward ANN

Transfer Function

Variables 1…x Inputs Hidden units Outputs Patients

A A1… Ax

.

.

.

.

N N1… Nx

bias

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.

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.

.

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bias


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Previous Approaches-PLANN

Partial Logistic Artificial Neural Network Proposed by Biganzoli et. al (1998)


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PLANN Model


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PLANN Model-Pre-processing

Categorical variables indicator variables

Continuous variables range(-1,1) or (0,1)

Treatment Type Indicator variables

Radiotherapy 1 0 0

Hormone therapy 0 1 0

Chemotherapy 0 0 1


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Training data - each subjects are replicated for all the intervals in which the subjects is observed and coupled with the event indicator

Time Size Treat1 Treat2 Treat3 EventSubject1 3 1.0 1 0 0 1Subject2 5 0.5 0 0 1 0

Time Size Treat1 Treat2 Treat3 EventSubject1 1 1.0 1 0 0 0

2 1.0 1 0 0 03 1.0 1 0 0 1

Subject2 1 0.5 0 0 1 02 0.5 0 0 1 03 0.5 0 0 1 04 0.5 0 0 1 05 0.5 0 0 1 0


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Testing – each subjects are replicated into full number of time interval of observed with all event indicator as zero.

Time Size Treat1 Treat2 Treat3 EventSubject1 1 1.0 1 0 0 0

2 1.0 1 0 0 03 1.0 1 0 0 04 1.0 1 0 0 05 1.0 1 0 0 0

Subject2 1 0.5 0 0 1 02 0.5 0 0 1 03 0.5 0 0 1 04 0.5 0 0 1 05 0.5 0 0 1 0

Time Size Treat1 Treat2 Treat3 EventSubject1 3 1.0 1 0 0 1Subject2 5 0.5 0 0 1 0


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PLANN Model-Post-processing

Predicted hazard is the mean calculated from the distribution of the activation.


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Analysis and Result Head and Neck Cancer – disease recurrence

Radiation therapy(Arm A)

Radiation + Chemotherapy

(Arm B)Total patients 51 45

End of time interval (in month)

47 76

Total patient recur until end interval

42 31

Total patient lost to follow up

9 14

Total training replication 628 967

Total testing 47 76


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Analysis and Result


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Analysis and Result


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Analysis and Result


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Conclusion ANN have been considered as alternative

methods for analysis of survival for individual patient or group of patients.

A smooth discrete hazard possible be model by treating the time interval and the covariates as an input variable with standard feed forward network and logistic activation function.


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Future Work Implementing PLANN model to our data (breast

cancer data from QMC). Develop fuzzy set rules in producing the survival

rate prediction for breast cancer patient.


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References Bishop, C. M. (1995). Neural Networks for Pattern Recognition, Oxford University

Press Inc., New York,. Burke, H.B., Goodman, P.H., Rosen, D.B., Henson, D.E., Weinstein, J.N., Harrell, F.E.,

Marks, J.R., Winchester, D.P. & Bostwick, D.G. (1997). Artificial neural network improve the accuracy of cancer survival prediction. Cancer, vol. 79, pp.857-862

Collett, D. (1994). Modelling Survival Data In Medical Research. Chapman and Hall, London.

Elia Biganzoli, P. B. L. M. E. M. (1998). "Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach." Statistics in Medicine 17(10): 1169-1186.

Lisboa, P. J. G., H. Wong, et al. (2003). "A Bayesian neural network approach for modelling censored data with an application to prognosis after surgery for breast cancer." Artificial Intelligence in Medicine 28(1): 1-25.

Ohno-Machado, L. (2001). "Modeling Medical Prognosis: Survival Analysis Techniques." Journal of Biomedical Informatics 34(6): 428-439.

Ripley, R. M., A. L. Harris, et al. (1998). "Neural network models for breast cancer prognosis." Neural Computing & Applications 7(4): 367-375.

Ravdin, P. and G. Clark (1992). "A practical application of neural network analysis for predicting outcome of individual breast cancer patients." Breast Cancer Research and Treatment 22(3): 285-293.

hazlina hamdan 31 march 2009 1 modelling survival prediction in medical data by hazlina hamdan dr....

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