Prognostic Modelling and Profiling of Breast Cancer Patients after Surgery

1. Prognostic Modelling and Profiling of Breast Cancer Patients after Surgery Ian Jarman School of Computer and Mathematical Sciences Liverpool John Moores University Supervisor: Prof. Paulo Lisboa

2. Contents • Motivation • Background • Prognostic Modelling • Rule Extraction • Summary • Further Work

3. Motivation • Present models developed over 20 years ago • Introduction of Breast Screening • Increasing research into Artificial Neural Networks (ANN) for censored data • Add to the toolkit of the oncologist in support of their decisions

4. Background • Survival Analysis • Current Models • Artificial Neural Networks • Unlock the Black Box • Rule Extraction

5. Survival Analysis • Survivor Function [S(t)] • Hazard Function [H(t)] • instantaneous potential per unit time for the event to occur, given that the individual has survived to time t • Censored Data • When an individual drops out of a study for reasons other than the event of interest

6. Current Models • Cox Proportional Hazard Model • Non parametric • no assumptions about the form of the data distribution • Linear in the parameters • Nottingham Prognostic Index (NPI) (0.2size + grade + nodal stage. )

7. Sigmoid Activation function Such as: 1/ (1+ exp(-a)) weights weights bias bias hidden nodes input output Artificial Neural Networks • Multi-Layer Perceptron (MLP) • Extension of logistic regression

8. Artificial Neural Networks • PLANN-ARD Partial Logistic Artificial Neural Network- Automatic Relevance Determination • Bayesian framework for network regularisation • Makes use of Censored Data • Irrelevant variables are‘soft pruned’

9. Rule Extraction (OSRE) • Developed by • Dr Terence Etchells • Prof. Paulo Lisboa • Finds explicit rules • e.g. patient is in a High Risk category if: • Nodes Ratio > 60% and Age between 40-59

10. Prognostic Modelling • NPI vs PLANN-ARD • Kaplan- Meier survival curves

11. Cox Lowest Risk Highest Risk PLANN Highest Risk Lowest Risk Cross-tabulation Matrix • How well are the models correlated?

12. P L A N N 4 3 2 1 NIL 100% censored n=1 100% censored n=8 100% censored n=41 100% censored n=19 100% censored n=35 NIL NPI 1 2 3 4 KM Survival within Matrix

13. NIL 100% censored n=1 100% censored n=8 100% censored n=41 100% censored n=19 100% censored n=35 NIL 4 3 2 1 Development of a New Prognostic Model • Group patients by survival • Distinct pattern emerges

14. How Does Survival differ? • Statistically there is no difference! Model by NPI Model by PLANN-ARD Model by new method

15. 150 287 89 33 Why Continue? • Statistically the same, but patient grouping differs

16. Rule Extraction • Problem • Many rules can be produced to describe a data set • Solution • Develop a new methodology to refine the rules

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18. Acceptable specificity ROC Curve • True Positives • Sensitivity • False Positives • 1-specificity • [1-specificity, sensitivity] • Refine Rules

19. Summary • An analysis of new methods overdue • Development of New Prognostic Model • Prognostic Models • Statistically the same, but patient grouping differs • Rule Reduction Method for Rule Extraction

20. Further Work • Use these methods for analysis of data • For one centre • Between centres • Visualisation techniques • ART, SOM