NY Prostate Cancer Conference - A. Vickers - Session 8: Debate 2: Categorical staging versus continuous prediction: How can we integrate predictive models into the staging systems? (Categorical staging vs prediction models: what is the evidence?)
•
1 j'aime•204 vues
Recommandé
Recommandé
Contenu connexe
Tendances
Tendances (20)
En vedette
En vedette (9)
Similaire à NY Prostate Cancer Conference - A. Vickers - Session 8: Debate 2: Categorical staging versus continuous prediction: How can we integrate predictive models into the staging systems? (Categorical staging vs prediction models: what is the evidence?)
Similaire à NY Prostate Cancer Conference - A. Vickers - Session 8: Debate 2: Categorical staging versus continuous prediction: How can we integrate predictive models into the staging systems? (Categorical staging vs prediction models: what is the evidence?) (20)
Plus de European School of Oncology
Plus de European School of Oncology (20)
Dernier
Dernier (20)
NY Prostate Cancer Conference - A. Vickers - Session 8: Debate 2: Categorical staging versus continuous prediction: How can we integrate predictive models into the staging systems? (Categorical staging vs prediction models: what is the evidence?)
- 1. Categorical staging vs. prediction models: what is the evidence? Andrew J. Vickers, Ph.D. Assistant Attending Research Methodologist Memorial Sloan-Kettering Cancer Center
- 2. How do we normally assess risk in cancer? We don’t do so explicitly We use stage as an approximate surrogate
- 3. Clinical decisions based on stage Adjuvant chemotherapy for stage II but not stage I gastric cancer Why is chemotherapy effective if “tumor invades into the muscularis propria layer” but not if it “extends only into the muscularis mucosa”?
- 4. Some basic decision theory We want to know a patient’s risk to help determine treatment e.g.: High risk of recurrence: adjuvant therapy Low risk of recurrence: no adjuvant therapy The more accurately we estimate risk, the better our decisions
- 5. Statistical model vs. AJCC: Gastric cancer AJCC IV (32) IIIB (24) IIIA (69) II (117) IB (115) IA (102)
- 6. Statistical model vs. AJCC: Melanoma IIC 53% IIB 34% IIA 24% IB 9% IA 2%
- 7. As the man on the Clapham ominbus would tell you… More information => better prediction A man with low grade cancer might have positive lymph nodes and high PSA suggesting metastases A man with high grade cancer might have very localized disease curable by prostatectomy
- 8. Risk group approach Based on features, we make a crude tree. Most cancer staging systems do this. High grade N Y High PSA & high stage Y HIGH RISK LOW RISK N
- 9. Kattan MW, et al., J Clin. Oncol., 2000.
- 10. Theory vs. Practice Risk modeling sounds like a great idea, but what data do we have that it actually helps? Typical study: Obtain a data set with predictors and outcome Compare predictive accuracy of model and AJCC stage or other risk grouping
- 11. Comparison of prediction models vs. risk grouping
- 13. Conclusion:
- 14. “In this single institution study that involved data dependent variable selection, a small change in effect size is unlikely to withstand external validation.”
- 16. Nam nomogram (AUC 0.68) Biopsy all (AUC 0.5) PCPT (AUC 0.61)
- 17. Prediction models vs. risk groups Have the benefits of prediction models really been demonstrated? BUT: Prediction modeling allows rational decision making.
- 18. Rational decision making Prostate biopsy if: PSA > 4 ng / ml? Risk of prostate cancer > 20%? Harms and benefits of biopsy can be considered to choose a risk threshold Risk threshold can be individualized
- 19. Ultimately, we must towards prediction modeling To facilitate optimal decision making