Purpose: Postoperative prognostic tools allow for improved prediction of future recurrence risk, patient counseling, and assessment of eligibility for adjuvant treatments and ensure appropriate follow-up surveillance. The purpose of this analysis was to validate existing prognostic models for patients with kidney cancer.
Materials and methods: The Canadian Kidney Cancer information system is a prospective cohort of patients managed at 14 institutions since January 1, 2011, to present. The Canadian Kidney Cancer information system was used to assess 15 predictive models for kidney cancer recurrence, 6 for cancer-specific mortality, and 4 for all-cause mortality in patients with a solitary, nonmetastatic kidney tumor treated with surgery (partial or radical nephrectomy). Discrimination was measured using c-statistics, 5-year calibration plots for calibration, and decision curve analysis at 5 years after surgery for net benefit when considering adjuvant therapy.
Results: Seven thousand one hundred seventy-four patients were included. For kidney cancer recurrence, c-statistics ranged from 0.62 to 0.83, depending on whether the model was derived and applied to all patients without further stratification, specific risk groups, or specific histological subtypes. Cancer-specific mortality models had c-statistics ranging from 0.60 to 0.89 and all-cause mortality models from 0.60 to 0.73. Using decision curve analysis in patients with clear-cell renal cell carcinoma, the best models for choosing adjuvant therapy to prevent recurrence and cancer-related death were the Mayo Clinic prediction models.
Conclusions: Model performance varied considerably with some suitable for clinical use. If using prediction models to select adjuvant therapy, the Mayo Clinic models were best when applied to a large contemporary cohort of Canadian patients.