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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. Contents. Motivation Background Prognostic Modelling Rule Extraction Summary Further Work.
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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
Contents • Motivation • Background • Prognostic Modelling • Rule Extraction • Summary • Further Work
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
Background • Survival Analysis • Current Models • Artificial Neural Networks • Unlock the Black Box • Rule Extraction
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
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. )
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
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’
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
Prognostic Modelling • NPI vs PLANN-ARD • Kaplan- Meier survival curves
Cox Lowest Risk Highest Risk PLANN Highest Risk Lowest Risk Cross-tabulation Matrix • How well are the models correlated?
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
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
How Does Survival differ? • Statistically there is no difference! Model by NPI Model by PLANN-ARD Model by new method
150 287 89 33 Why Continue? • Statistically the same, but patient grouping differs
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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Acceptable specificity ROC Curve • True Positives • Sensitivity • False Positives • 1-specificity • [1-specificity, sensitivity] • Refine Rules
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
Further Work • Use these methods for analysis of data • For one centre • Between centres • Visualisation techniques • ART, SOM