Predicting Oncologic Outcomes in Renal Cell Carcinoma After Surgery

Predicting oncologic outcomes is important for patient counseling, clinical trial design, and biomarker study testing. To develop prognostic models for progression-free (PFS) and cancer-specific survival (CSS) in patients with clear cell renal cell carcinoma (ccRCC), papillary RCC (papRCC), and chromophobe RCC (chrRCC). Retrospective cohort review of the Mayo Clinic Nephrectomy registry from 1980 to 2010, for patients with nonmetastatic ccRCC, papRCC, and chrRCC. Partial or radical nephrectomy. PFS and CSS from date of surgery. Multivariable Cox proportional hazards regression was used to develop parsimonious models based on clinicopathologic features to predict oncologic outcomes and were evaluated with c-indexes. Models were converted into risk scores/groupings and used to predict PFS and CSS rates after accounting for competing risks. A total of 3633 patients were identified, of whom 2726 (75%) had ccRCC, 607 (17%) had papRCC, and 222 (6%) had chrRCC. Models were generated for each histologic subtype and a risk score/grouping was developed for each subtype and outcome (PFS/CSS). For PFS, the c-indexes were 0.83, 0.77, and 0.78 for ccRCC, papRCC, and chrRCC, respectively. For CSS, c-indexes were 0.86 and 0.83 for ccRCC and papRCC. Due to only 22 deaths from RCC, we did not assess a multivariable model for chrRCC. Limitations include the single institution study, lack of external validation, and its retrospective nature. Using a large institutional experience, we generated specific prognostic models for oncologic outcomes in ccRCC, papRCC, and chrRCC that rely on features previously shown—and validated—to be associated with survival. These updated models should inform patient prognosis, biomarker design, and clinical trial enrollment. We identified routinely available clinical and pathologic features that can accurately predict progression and death from renal cell carcinoma following surgery. These updated models should inform patient prognosis, biomarker design, and clinical trial enrollment.