1. How and when to use nomograms for counseling patients with prostate cancer ? By the BAU Working Group of Urology

2. Patient counselling 2 62 years old PSA 8,3 ng/ml DRE, benign prostatic hypertrophy Ultrasound (+) 12 biopsies : 2 positive on right for a Gleason 7 (3+4) in 25 and 35% of the sample. Patient is concerned and is looking at more information, about which treatment to choose, is think about a radical… He is discussing the need for a extended lymph nodes dissection. Patient is concerned and is looking at more information, about which treatment to choose, is think about a radical… He is discussing the need for a extended lymph nodes dissection.

3. Doctors’ attitude 3 Le malade: Je n’ai rien moi, monsieur le docteur. Knock: Qu’est ce que vous en savez. Knock ou le triomphe de la médecine, Jules Romain, Acte 2 scène VI Unidirectional, paternalistic ‘expert’ physician judgement Personal physician bias Historical, no controlled data and attitudes Overall average outcome prediction

4. “Health care is changing fast and patients' experiences and expectations are also changing Patients no longer see themselves as passive recipients of care: increasingly they expect to be involved in all decisions that affect them. ” The European Patient Of The Future (State of Health) by Angela Coulter and Helen Magee, Open University Press, 2003. Patient’s expectation

5. Tailor based approach 5 Bi directional Patient preference Evidence based data Individualised outcome prediction and prognosis

6. Patient counselling 6 62 years old PSA 8,3 ng/ml DRE, small nodule (0,7 mm ) in the right prostatic lobe Ultrasound (+) 12 biopsies : 2 positive on right for a Gleason 7 (3+4) in 25 and 35% of the sample. Example : what is the risk of lymph nodes invasion ?

7. 7 What are the different level of EBM supported prediction ? Risk groupings and probability tables Classification and regression tree(CART) analysis Nomograms Artificial neural networks (ANN)

8. 8 Risk Grouping Patients are “group” according to several prognostic factors into “risk category” Univariate or multivariate regression analysis are then performed to estimate the % of occurence of the endpoint. Ex: Partin table

9. 9 Probability tables Partin table

10. Parker table for risk of death in untreated men with PCa PCa deaths Other cause of death C. Parker et al. British Journal of Cancer (2006) 94, 1361 – 1368

11. Classification And Regression Tree analysis Stephenson et al. Clin. Oncol. 25:2035-2041, 2007 4-year probability after salvage radiotherapy

12. Stephenson nomogram Stephenson et al. Clin Oncol 25:2035-2041, 2007

13. 13 Risk Grouping Limitations Category summarize cohorts of patients Increment in category may lead to overestimation of true frequency of endpoint T1c, Gleason 3+4 Risk of (+) LN PSA 5,9 ng/dl 8 % PSA 6,1 ng/dl 12 %

14. 14 Nomograms - definition Statistical definition Graphical representation of a mathematical formula or algorithm Incorporating several predictors modeled as continuous variables To predict a particular end point Using traditional statistical methods –Multivariable logistic regression –Cox proportional hazard analysis

15. 15 Ex Kattan nomograms for predicting prostate-specific antigen recurrence after Radical prostatectomy

16. Nomogram allows progressive changes of the variables 16

17. Nomogram allows progressive changes of the variables 17

18. Nomogram allows progressive changes of the variables 18

19. 19 Predictive tool and nomogram criteria Cohorts Developmental vs control Validation Internal vs external Predictive accuracy Discrimination and calibration Generalizability Level of complexity Head-to-head comparison

20. 20 Nomograms and cohorts Cohort Developmental cohort –Patient study population >Initial statistical patient sample >Single center of excellence series and/or >Data of high volume surgeons/pathologists from highly specialized tertiary care centers Control cohort –Control population to test the model and confirm initial predictive accuracy >Internal and/ or external cohort

21. 21 Nomograms and validation Validation Internal validation –Specific statistical methods, f.e bootstrapping External validation –Ideal Gold Standard method of validation –Single or multicenter,same/different level of care Validation end points Predictive accuracy Discrimination and calibration ability

22. 22 Artificial neural networks Neural networks Layers of nodes –Input, hidden, output Dendrites:input –Interconnections by weigthed connection lines Axons : output Computational model High complexity

23. 23 Nomograms for predicting prostate-specific antigen recurrence

24. 24

25. 25

26. 26 Nomograms limitations Retrospective statistical approach Despite prospective data collection Modeling criteria Model selection criteria exclude certain other patient subgroups Total PSA Total PSA is an important variable in most nomograms –Lack of specificity –Testing variability ( 30%) –Stage migration

27. 27 Nomograms limitations Contemporaneity Tool development in non-contemporary situations –Stage migration/ screen detected populations –Diagnostic and therapeutic standards –E.g. sextant biopsies vs 10 -12 core biopsies –Dose of radiotherapy –Surgical standards –….

28. 28 Ex of contemporary impact Briganti Nomogram Contemporaneity Tool development in non-contemporary situations –Stage migration/ screen detected populations –Diagnostic and therapeutic standards –E.g. sextant biopsies vs 10 -12 core biopsies –Dose of radiotherapy –Surgical standards –….

29. 29 Nomogram predicting the probability of lymph node invasion in patients undergoing extended pelvic lymphadenectomy Briganti et al. Eur Urol 2007 Change in the technique induce a 20% risk increase…

30. 30 Nomograms for prediction of prostate cancer at needle biopsy Karakiewicz PI and Hutterer GC (2008) Predictive models and prostate cancer. Nat Clin Pract Urol 5: 82–92

31. 31 Prediction of specific pathological features of clinically localized prostate cancer (before treatment) Karakiewicz PI and Hutterer GC (2008) Predictive models and prostate cancer

32. 32 Prediction of biochemical recurrence with preoperative variables Karakiewicz PI and Hutterer GC (2008) Predictive models and prostate cancer Nat Clin Pract Urol 5: 82–92 doi:10.1038/ncpuro0972

33. 33 Predictive accuracy of existing nomograms Chun F et al. World J Urol 2007

34. 34 What do we need in the future? Ultimately, improved imaging studies and high-throughput genomics may replace the use of nomograms, as they will provide a real patient-specific staging and prognostication, and allow patient-tailored treatment decisions to be made In the meantime, nomograms are the best possible alternative and should be actively implemented in EAU prostate cancer guidelines