Nomograms provide predictions of outcomes for prostate cancer patients based on known treatment outcomes of similar patients. However, nomograms have several limitations including bias from the development cohort, lack of external validation, and lack of updates using contemporary patient populations. Additionally, nomograms often use surrogate endpoints rather than clinically meaningful endpoints and predictive accuracy is not 100%. While nomograms can help guide clinical decision making, good clinical judgement is still needed and nomograms may not accurately capture all risk factors or change clinical decisions for individual patients.

1. Do I need a NOMOGRAM to make a good decision ? Hein Van Poppel, MD,PhD Steven Joniau, MD Leuven, Belgium SecondInterdisciplinary Conference MSKCC – ESO New York, 9-4-2011

2. In 2008 about 33.000 man died of PCa in N.Am., and 90.000 in Europe Howcan we do better?

3. Do I need a NOMOGRAM to make a good decision ? “The answer is no” Hein Van Poppel “The answer is yes” Michael Kattan

5. Situation in the past For decades, staging and prognosticationinvolvedwell-knownprognostic factors: cTNM Gleason score PSA However, these parameters provide ratherlimitedinformationwhenconsideredseparately

6. Whydecision aids? Physicianjudgement is the basis for risk estimation, patient counseling, and decisionmaking Thisjudgementmaybebiasedbecause of subjective and objectiveconfounders Development of predictive and prognostic tools Recent explosion in the field of PCadecision aids Shariat SF et al. Cancer 2008;113:3075-99

7. Nomograms Currently the most accurate tool to predictoutcomes in PCapatients Basedonknowntreatmentoutcomesfor a groupwithsufficientsimilarities to the patient, and having been treated in the samefashion, a number of yearsago

8. Whatcan we usenomogramsfor? 1. Pre-biopsyprostate cancer risk calculation 2. LocalPCastagingpost-biopsy Estimation of pTNM 3. Pre-treatmentestimation of outcome Biochemicalrelapse Clinicalrelapse Deathfrom prostate cancer

9. Whatcan we usenomogramsfor? 4. Post-treatmentestimation of outcome Biochemicalrelapse Clinicalrelapse Deathfrom prostate cancer 5. Predictionof local / systemicfailurewhenbiochemicalrelapse 6. Predictionof death in HRPC

10. Predictiveaccuracy of existingnomograms Chun F et al. World J Urol 2007;25:131-42

11. Limitations of nomograms Bias due to development cohort Oftenbasedonsingle-centre series and/or data fromtertiary care centres Retrospectivestatisticalapproach Despiteprospective data collection Specific model selection criteria Model selection criteria excludecertainsubgroups, e.g. patientswho had neoadjuvant HT are excluded in most models Lack of externalvalidation Chun F et al. World J Urol 2007;25:131-42

12. Limitations of nomograms Imply a concept of stability: No change, noevolution in surgerytechniques and methods,…. impossible to consider recent improvements in technique, knowledge in tumourbiology, and disease characteristics1 Lack of periodic updates in contemporarycohorts Development in non-contemporarysituations = inaccurate predictions in contemporary patients2 Stage migration/ screen detectedpopulations Change in diagnostic and therapeuticstandards For example: sextant biopsies vs. 10-12 core biopsies Type and dose of Radiotherapy, Surgical techniques Guillonneau B. EurUrol2007 2.Chun F et al. World J Urol2007

13. Limitations of nomograms Surrogateendpoints in most nomograms Pathologic stage prediction Biochemicalrecurrence Lack of “hard endpoint” nomograms Requiredendpoints: Local and distantrecurrence Disease- specific and overall survival Correct and long-term follow-up and competingcomorbidityanalysis Chun F et al. World J Urol 2007;25:131-42

14. Summary: limitations of nomograms Bias due to development cohort High volume, tertiary care centers Retrospectivestatisticalmethodology Specific model selection criteria Lack of externalvalidation Lack of periodic updates in contemporarycohorts Lack of novel more specific markers Surrogate versus clinicallymeaningfulendpoints Predictiveaccuracynot 100%

16. Thismay all beveryinteresting, BUT… Do these predictionsreallychangeyourclinicaljudgement? Will theyinfluenceyourdecision-makingprocess? Are these predictionsreallyhelpfulforyourindividualpatient?

17. Questions to Michael Kattan about a givenpatient Should weomit a biopsy…? Should weadvocate Active Surveillance…? Shall we not go for a RPr…? Can we safelyomit a LND…? Do we give give adjuvant therapy to all…? Will our patient appreciate an estimation of the chance of his time to failure, or to death…?

19. No proofonadvantage of use of nomograms Use of nomograms has notyet been implementedsufficientlyinto routine urologicalpractice Studies providingevidence-basedproofon the advantage of usingnomograms over clinicaljudgement are virtually ABSENT No nomogramwill ever take the place of goodclinicaljudgement and information to the patient

21. Whatis the future? Ultimately, improvedimagingstudies and high-throughputgenomicswillreplace the use of nomograms, as theywill provide a realpatient-specificstaging and prognostication, and allowpatient-tailoredtreatmentdecisions GenomicSignaturesforPersonalisedTherapy Mammaprint, Coloprint, “Prostaprint “?

26. BACK - UP 26

29. 29 Prostate cancer: Decision aids Univariate and multivariableanalysis Risk groupings Probability tables Artificialneuralnetworks (ANN) Classification and regression tree (CART) analysis Nomograms Shariat SF et al. Cancer 2008;113:3075-99

30. 30 Prostate cancer: Evaluatingpredictive tools Predictiveaccuracy of the model Internal and externalvalidation to ensuregeneralizability Model calibration Level of complexicity Clinicalimplication Head-to-headcomparisons Shariat SF et al. Cancer 2008;113:3075-99 Capitanio U et al. The Prostate 2010;70:1371-78

31. Definition of nomogram Statisticaldefinition Graphicalrepresentation of a mathematicalformulaoralgorithm Incorporatingseveralpredictors modeled as continuous variables To predict a particularendpoint Using traditional statisticalmethods - Multivariablelogisticregression - Cox proportional hazard analysis

32. A smallexercise… Patient XX PSA 6.4 cT2a BiopsyGleason score 6 in 2 cores

33. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

34. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

35. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

36. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

37. What is the risk of biochemicalrelapseaftersurgery… 25% 50% 75% 100%

38. What is the risk of biochemicalrelapseaftersurgery… 25% 50% 75% 100%

39. Anothersmallexercise… Patient YY PSA 8.6 cT2c BiopsyGleason score 7 on 2 biopsies

40. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

41. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

42. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

43. What is the risk of… ECE? 20% 40% 60% 80% SVI invasion? 5% 10% 15% 20% pN+? 2% 4% 6% 8%

44. What is the risk of biochemicalrelapseaftersurgery… 25% 50% 75% 100%

45. What is the risk of biochemicalrelapseaftersurgery… 25% 50% 75% 100%

46. The American World

47. The Real World

48. BRUSSSELS BELGIUM België Belgique Belgien LEUVEN

49. Nomogramtopredictlowvolumeinsignificantprostatecancer (n=258) 58 years 30 grams PSA 3,0 4mm tumor Low-volume/low-grade cancer was defined as pathologic organ-confined disease and a tumor volume < 0.5 cc with no Gleason grade 4 or 5 cancer. Nakanishi et al., Cancer 2007

50. Prediction of biopsyoutcome Karakiewicz PI and Hutterer GC.Nat Clin Pract Urol 2008;5:82–92 Karakiewicz PI and Hutterer GC.Nat ClinPractUrol 2008;5: 82–92

51. Prediction of pathological features clinically localized PCa(before treatment) Karakiewicz PI and Hutterer GC.Nat ClinPractUrol 2008;5: 82–92

52. Prediction of pathological features clinically localized PCa(before treatment) Karakiewicz PI and Hutterer GC.Nat ClinPractUrol 2008;5: 82–92

53. Prediction of biochemical recurrence with preoperative variables Karakiewicz PI and Hutterer GC.Nat ClinPractUrol 2008;5: 82–92

54. PCa metagram PCa metagram is constructed of 16 different treatmentoptions 10 outcomesrelated to cancercontrol, survival and morbidity 160 treatment/outcomecombinations Only 31 cells are populatedwithavailable tools Areas of deficiency in the currentcatalog of prediction tools Nguyen CT and Kattan MW. Cancer 2009;115(Suppl 13):3160-2

55. PCa metagram Data willbeincorporatedinto a software program Physician will enter patient-specific variables willgenerategraphical and tabularpresentation of predictions of treatmentendpoints, with all availablealternativestailored to the individualpatient Limitations of the metagram Not all cells are populated  more tools are needed Lack of prediction tools for LRP, cryoablation and HIFU Survival and morbidityoutcomes are poorlyrepresented Additionalprediction tools assessing risk of metastasis and cancer-specificmortality are needed Nguyen CT and Kattan MW. Cancer 2009;115(Suppl 13):3160-2

56. PCa metagram

57. What to do to implement the use of nomograms? Update nomograms to contemporarypatientpopulations Novelbiomarkers to improvepredictions Head-toheadcomparisonsbetweennomograms to select the best-suited model in selectedfields of PCaoutcomes We neednomogramsthat provide accurate predictions of hard clinicalendpoints (clinicalfailure, deathfrom the disease) accuratelypredictdeathfromcomorbiddisease in men withlocalizeddiseaseselectedforradicaltreatment predicttreatment-relatedtoxicity Efforts are needed to improve the accuracy, accessibility and flexibility of nomograms and to provide more evidence to justifytheir routine use in clinical practice1 Lughezzani G et al. EurUrol 2010;58:687-700

58. Whymightnomogramsbeimplementedintoclinicalpractice? Appropriatepatient counseling / decision-making ? Betterdiseaseprognostication ? Follow-up monitoring ? Selection of patientsforclinical trials !

59. Limitations of nomograms Suboptimalpredictiveaccuracy Nomogramprediction is not 100% accurate Lack of consideration of all predictive risk factors Inability to assemble all knownprognostic factors optimally1 To improvepredictiveaccuracy (PA) we need Novelbiomarkersassociatedwith the biologicbehaviour of PCa 8% increase in PA withinclusion of IL-6 and TGF-β12 Plasminogen activator inhibitor type 1, humanglandular kallicrein-2, gene expressionsignatures, plasma endoglin, … Larger datasets and systematic and clean data collection More sophisticated modeling procedures3 Chun F et al. World J Urol 2007;25:131-42 Kattan MW et al. J ClinOncol 2003;21:3573-9 Shariat SF et al. Cancer 2008; 113:3075-99

60. EAU PCaguidelines Use of nomograms is onlyincluded 3 times Preoperativestaging(Kattannomogram, Partin tables (= look-up tables) Indication of extendedlymph node dissection(Briganti nomogram) Indication of nerve-sparingsurgery(Partin tables)