Predictive Models Are A Better Way To Stratify Patients For Clinical Trials<br />James A. Eastham, MD<br />Memorial Sloan-...
Clinical Prognostic Factors<br /><ul><li>Each clinical prognostic factor (stage, Gleason grade, PSA) independently predict...
When prognostic factors are combined the accuracy of  prediction increases
Risk-groups
Multivariate prediction models (nomograms)</li></li></ul><li>Definitions of “High-Risk” Prostate CancerProportion of RP pa...
Surgery can cure many men with“high risk” cancer<br />
Risk groups are often heterogeneous, containing patients who vary widely in prognosis<br />From Mitchell JA et al. J Urol ...
Advantages of nomograms: interpreting discordant results<br />A 59 yo man has a poorly differentiated (Gleason grade 4 + 5...
Advantages of nomograms: interpreting discordant results<br />A 59 yo man has a poorly differentiated (Gleason grade 4 + 5...
Includes pretreatment and postoperative predictions. Uses published prostate cancer nomograms.<br />Available at www.mskcc...
Patients used in the preoperative recurrence nomogram validation<br />USA		Cleveland Clinic		1168<br />			LSU			  	583<br ...
Predicted vs. Actual Freedom from Recurrence<br />Range of Nomogram Predicted Probabilities<br />Graefen et al., JCO, 2002...
Continuous Model vs. Staging/Grouping Systems<br />
Urologists vs. Preoperative Nomogram<br />10 case descriptions from 1994 MSKCC patients presented to 17 urologists<br />In...
Uses of Risk Prediction Models in Clinical Trials<br />Ethically restrict the trial to those at highest risk of failure wi...
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NY Prostate Cancer Conference - J.A. Eastham - Session 8: Debate 3: Clinical trials design (Predictive models are a better way to stratify patients for clinical trials)

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NY Prostate Cancer Conference - J.A. Eastham - Session 8: Debate 3: Clinical trials design (Predictive models are a better way to stratify patients for clinical trials)

  1. 1. Predictive Models Are A Better Way To Stratify Patients For Clinical Trials<br />James A. Eastham, MD<br />Memorial Sloan-Kettering Cancer Center<br />
  2. 2. Clinical Prognostic Factors<br /><ul><li>Each clinical prognostic factor (stage, Gleason grade, PSA) independently predicts pathologic stage and prognosis after treatment
  3. 3. When prognostic factors are combined the accuracy of prediction increases
  4. 4. Risk-groups
  5. 5. Multivariate prediction models (nomograms)</li></li></ul><li>Definitions of “High-Risk” Prostate CancerProportion of RP patients that are high risk by each definition<br />* Percentage of the 2521 patients with calculable preoperative PSA velocity<br />
  6. 6. Surgery can cure many men with“high risk” cancer<br />
  7. 7. Risk groups are often heterogeneous, containing patients who vary widely in prognosis<br />From Mitchell JA et al. J Urol 2005: 173:1126<br />Low risk<br />Intermediate risk<br />High risk<br />cT1c-T2a, Gl <7, PSA <10<br />cT2b or Gl 7 or PSA 10-20<br />cT2c or Gl 8-10 or PSA >20<br />
  8. 8. Advantages of nomograms: interpreting discordant results<br />A 59 yo man has a poorly differentiated (Gleason grade 4 + 5 = 9) cancer in 1 biopsy core taken from an impalpable (cT1c) prostate because of a PSA of 6. How likely would surgery control this cancer? 83% 63% 43% 23% 3%<br />A 53 yo man had a PSA 47 when first examined. There was a firm palpable nodule in the prostate (cT2a). Needle biopsy showed Gleason grade 3+3=6 cancer in 3 of 6 cores. Is this a “curable” lesion? What is the 5 yr. freedom from recurrence? 83% 63% 43% 23% 3%<br />
  9. 9. Advantages of nomograms: interpreting discordant results<br />A 59 yo man has a poorly differentiated (Gleason grade 4 + 5 = 9) cancer in 1 biopsy core taken from an impalpable (cT1c) prostate because of a PSA of 6. How likely would surgery control this cancer? 83% 58% 43% 23% 3%<br />Answer: 83% (37% confined, 40% ECE, 15% SVI, 8% LN)<br />A 53 yo man had a PSA 47 when first examined. There was a firm palpable nodule in the prostate (cT2a). Needle biopsy showed Gleason grade 3+3=6 cancer in 3 of 6 cores. Is this a “curable” lesion? What is the 5 yr. freedom from recurrence? 83% 58% 43% 23% 3%<br />Answer: 58% (22% confined, 60% ECE, 10% SVI, 8% LN)<br />
  10. 10. Includes pretreatment and postoperative predictions. Uses published prostate cancer nomograms.<br />Available at www.mskcc.org/prostate/nomograms<br />
  11. 11. Patients used in the preoperative recurrence nomogram validation<br />USA Cleveland Clinic 1168<br /> LSU 583<br /> UCLA 617<br /> USC 1501<br />Europe Hamburg 1134<br /> Rotterdam 475<br />Australia Sydney 754<br />Total6232 Patients<br />Graefen et al., JCO, 2002<br />
  12. 12. Predicted vs. Actual Freedom from Recurrence<br />Range of Nomogram Predicted Probabilities<br />Graefen et al., JCO, 2002<br />
  13. 13. Continuous Model vs. Staging/Grouping Systems<br />
  14. 14. Urologists vs. Preoperative Nomogram<br />10 case descriptions from 1994 MSKCC patients presented to 17 urologists<br />In addition to PSA, biopsy Gleason grades, and clinical stage, urologists were provided with patient age, systematic biopsy details, previous biopsy results, and PSA history<br />Preoperative nomogram was provided<br />Urologists were asked to make their predictions of 5 year progression-free probabilities with or without use of the preoperative nomogram<br />Concordance indices:<br />Nomogram = 0.67<br />Urologists = 0.55, p<0.05<br />Ross P et al., Semin Urol Oncol, 2002<br />
  15. 15. Uses of Risk Prediction Models in Clinical Trials<br />Ethically restrict the trial to those at highest risk of failure with standard therapy<br />Reduce sample size requirements by boosting event rates<br />
  16. 16. Conclusions<br />Nomogram predictions assist in:<br />Patient counseling<br />Shared decision-making<br />Informed consent<br />Clinical trial design and analysis<br />Free software facilitates computations<br />But…<br />
  17. 17. Conclusions<br />Nomograms are not perfect<br />Based on patients treated in the (remote) past<br />Predictive accuracy needs to be improved<br />No information is available on how nomograms impact decision making<br />Do nomograms lead to ‘better’ satisfaction with decisions?<br />

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