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Hazlina Hamdan 31 March 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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Hazlina Hamdan31 March 2009
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Modelling survival prediction in medical data
By Hazlina HamdanDr. Jon Garibaldi
Hazlina Hamdan31 March 2009
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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
Hazlina Hamdan31 March 2009
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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
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N N1… Nx
bias
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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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PLANN Model-Pre-processing
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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PLANN Model-Pre-processing
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.