Prognostic Modelling and Profiling of Breast Cancer Patients after Surgery Ian Jarman School of Computer and Mathematical Sciences Liverpool John Moores.

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Presentation transcript:

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. )

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))

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

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

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 PLANN4321PLANN4321

Development of a New Prognostic Model % 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

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

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

Boxed Rules Rule Extraction Decision Tree

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

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