1. Peter Moore 10/05/051 ANN survival prediction for cancer patients Peter Moore High Energy Physics University of Manchester

2. Peter Moore 10/05/052 Project Overview Funded by MRC And PPARC…… me Collaboration: –HEP at University of Manchester ANN and Software development GRID security –Ninewells Hospital Dundee. Data Clinical expertise

3. Peter Moore 10/05/053 Main Aims To set up ANN based on several available DBs to predict the probable survival outcome for the patients suffering with breast or colorectal cancers Make the ANN available via secure Internet access (GRID) for clinicians nationwide Investigate the possibilities of designing better management plans and improving cancer patients quality of life after treatment.

4. Peter Moore 10/05/054 Data Colorectal and Breast Cancer Patients Sets of records do not share parameters 50,000 records, 100+ variables Data inconsistency Noise Missing or incomplete data Filling by hand leads to errors

5. Peter Moore 10/05/055 Artificial Neural Networks Mathematical model based on neurons Many variations Multilayer Feed Forward ANN Approximate any function Inputs x i  xi wj xi wj  w1w1 w3w3 w2w2 wjwj Input summator Nonlinear converter Output

6. Peter Moore 10/05/056 General Methodology 1.Forming a training set adequately describing the survival function. 2.Tuning the synapse weights (training). 3.Testing. 4.Evaluating and Validating 5.Recommendation for patient management plan. Training set Selecting & coding Genetic Algorithm (global estimation) Gradient based Alg. (local improvement)

7. Peter Moore 10/05/057 Our Methodology PLANN Cascade Architecture Scaled Conjugate Gradient training algorithm 200 times bootstrap re- sampling 1 j 0 time J bias H 1 hh i1i1 ihih iHiH K  HK  hK  1K 0 KK

8. Peter Moore 10/05/058 Results analysis Separate (unseen by ANN) records Known as a validation set Interpreting the ANN outputs –Individual patient testing –Group testing Cancer management

9. Peter Moore 10/05/059 Individual Patient Results

10. Peter Moore 10/05/0510 ROC Curve Receiver Operating Characteristic Probability of Detection Probability of False Alarm

11. Peter Moore 10/05/0511 Kaplan Meier Survival Standard method used in medicine Actual Survival probability for any group of patients Grouping patients together by specific diagnostic factors Takes into account censoring

12. Peter Moore 10/05/0512 Kaplan Meier Example

13. Peter Moore 10/05/0513 Prognostic groupings Colon Cancer A : Dukes Stage A, node negative, no liver deposits and curative operation B : Dukes Stage B, node negative, no liver deposits and potentially curative operation C: Dukes Stage C, no liver deposits and potentially curative operation D: Dukes Stage D, multiple lymph node involvement or hepatic deposits

14. Peter Moore 10/05/0514 Prognostic groups A, B

15. Peter Moore 10/05/0515 Prognostic groups C,D

16. Peter Moore 10/05/0516 Visions Web interface Accessible by medical personnel Improved Data New Databases sources Patient management profiles Requires improved hospital patient data collection methods Medical trials data Genome and Molecular data

17. Peter Moore 10/05/0517 Visions Online Dynamic ANN training? Continuously updates with latest research results and data –( would currently fail ethics approval ) Automatic relevance determination –Problems with reliability of unsupervised ANN training Remote data uploading Confidentiality and Enforcement of privacy protection Security Healthgrid?

18. Peter Moore 10/05/0518 More info peter@hep.man.ac.uk http://www.hep.man.ac.uk/u/peter/ http://ipcrs.hep.man.ac.uk

19. Peter Moore 10/05/0519

20. Peter Moore 10/05/0520