Validation of Prognostic Models for Renal Cell Carcinoma Recurrence, Cancer-Specific Mortality, and All-Cause Mortality
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View abstract on PubMed
Summary
This summary is machine-generated.This study validated kidney cancer prognostic models. The Mayo Clinic models best predicted recurrence and mortality in Canadian patients, aiding adjuvant therapy decisions.
Area Of Science
- Oncology
- Urology
- Biostatistics
Background
- Postoperative prognostic tools are crucial for predicting recurrence risk, guiding patient counseling, and determining eligibility for adjuvant treatments in kidney cancer.
- Accurate prognostic models enhance follow-up surveillance strategies for kidney cancer patients.
Purpose Of The Study
- To validate existing prognostic models for predicting outcomes in kidney cancer patients.
- To assess the performance of various models in forecasting recurrence, cancer-specific mortality, and all-cause mortality.
Main Methods
- Utilized the Canadian Kidney Cancer information system, a prospective cohort of 7,174 patients treated with surgery for nonmetastatic kidney tumors.
- Assessed 15 models for recurrence, 6 for cancer-specific mortality, and 4 for all-cause mortality using C statistics and decision curve analysis.
Main Results
- Model performance varied; C statistics for recurrence ranged from 0.62 to 0.83.
- Cancer-specific mortality models showed C statistics from 0.60 to 0.89, and all-cause mortality models from 0.60 to 0.73.
- Mayo Clinic prediction models demonstrated superior performance for selecting adjuvant therapy in clear-cell renal cell carcinoma patients.
Conclusions
- Prognostic model performance differs, with some models being clinically applicable.
- The Mayo Clinic models are recommended for selecting adjuvant therapy in contemporary Canadian kidney cancer patients based on their validated performance.
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