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Published on: April 9, 2019
ROC optimization may improve risk stratification of prostate cancer patients.
R Cheung1, M D Altschuler, A V D'Amico
1Department of Radiation Oncology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Urology
|February 22, 2001
Summary
Optimizing the actuarial method improves individual clinical outcome prediction. This study demonstrates that receiver operating characteristic (ROC) optimization simplifies risk stratification for better accuracy in patient prognostication.
Area of Science:
- Medical Informatics
- Oncology
- Biostatistics
Background:
- Accurate clinical outcome projection is crucial for rational treatment decisions.
- Current clinical outcome analysis often relies on population data, limiting individual patient prognostication.
- The actuarial method is widely used but requires optimization for individual case prediction.
Purpose of the Study:
- To investigate the applicability and optimization of the actuarial method for projecting individual clinical outcomes.
- To develop and assess a Clinical Outcome Prediction Expert (COPE) system for actuarial prediction.
- To enhance the accuracy of clinical outcome prediction through optimized risk stratification.
Main Methods:
- A Clinical Outcome Prediction Expert (COPE) system was designed and implemented.
- A post-prostatectomy database of 1043 patients was used, with 60% for training and 40% for validation.
- Stratified actuarial curves, incorporating prostate-specific antigen (PSA) level, Gleason score, and American Joint Commission on Cancer Staging T-stage, were used for individual outcome projection.
- Predictive performance was measured using the area under the receiver operator characteristic (ROC) curve.
Main Results:
- Optimized stratification was achieved for pretreatment PSA level (<10 ng/mL vs. >10 ng/mL), Gleason score (≤6 vs. >6), and clinical AJCC T-stage (≤T2a vs. >T2a).
- A multivariate risk score was generated using optimized univariate risk scores.
- The higher-risk group was identified as patients with PSA >10 ng/mL, or PSA ≤10 ng/mL combined with Gleason score >6 and T-stage >T2a.
- The optimized multivariate risk score achieved the highest ROC area of 0.77.
Conclusions:
- The optimal conditions for individual actuarial prediction are not predetermined and necessitate optimization.
- Receiver operating characteristic (ROC) optimization offers a simplified approach to risk stratification.
- This optimization strategy has the potential to improve the accuracy of clinical outcome prediction for individual patients.
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