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

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

12. KM Survival within Matrix 100% censored n=19 100% censored n=35NIL 100% censored n=41 100% censored n=8 NIL 100% censored n=1 NPI 1 2 3 4 PLANN4321PLANN4321

13. Development of a New Prognostic Model 1 2 3 4 100% censored n=19 100% censored n=35 NIL 100% censored n=41 100% censored n=8 NIL100% censored n=1  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. Why Continue? 1502878933 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

17. Boxed Rules + + + + + +++++ ++ ++ ++++ + + + ++ + + + ++++ ++ + + + +++ + + + + + + + ++ + + + + + + + + + + + + + ++++ + + + + + +++++ + + + + + + ++++ + + + + + ++ + + + + + + + + ++ + + +++ + + + ++ ++ ++ + + + + + + + + + + + + + + + + + + + + + + + + Rule Extraction Decision Tree + + + + + + + ++++++ + ++ ++ + ++

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

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