Zusammenfassung
Purpose: Collecting duct renal cell carcinoma is a rare, aggressive histological subtype of renal cell carcinoma. Since few groups have evaluated the oncological prognosis in these patients based on clinical and pathological parameters, we assessed parameters prognostic for disease specific mortality. Materials and Methods: From a cohort of 14,047 patients with renal cell carcinoma we retrieved ...
Zusammenfassung
Purpose: Collecting duct renal cell carcinoma is a rare, aggressive histological subtype of renal cell carcinoma. Since few groups have evaluated the oncological prognosis in these patients based on clinical and pathological parameters, we assessed parameters prognostic for disease specific mortality. Materials and Methods: From a cohort of 14,047 patients with renal cell carcinoma we retrieved the records of 95 with collecting duct renal cell carcinoma at a total of 16 European and American centers of the CORONA (Collaborative Research on Renal Neoplasms Association) and SATURN (Surveillance and Treatment Update Renal Neoplasms) projects, and another 2 centers. Multivariable Cox regression analysis was applied to determine the influence of parameters on disease specific mortality. Median followup was 48.1 months (IQR 24-103). Results: The disease specific survival rate at 1, 2, 5 and 10 years was 60.4%, 47.3%, 40.3% and 32.8%, respectively. American Society of Anesthesiologists (ASA) score 3-4, tumor size greater than 7 cm, stage M1, Fuhrman grade 3-4 and lymphovascular invasion independently predicted disease specific mortality. Based on these parameters, patients were divided into 26 (27%) at low, 13 (14%) at intermediate and 56 (59%) at high risk with a 5-year disease specific survival rate of 96%, 62% and 8%, respectively (bootstrap corrected c-index 0.894, 95% CI 0.820-0.967, p < 0.001). Conclusions: While patients with collecting duct renal cell carcinoma are commonly diagnosed at advanced stage and have poor prognosis after surgery, a subset has excellent survival. Histopathological features can help risk stratify patients based on the described, highly accurate risk model to predict disease specific mortality, facilitating patient counseling and risk based clinical decision making for adjuvant therapy and clinical trial inclusion.
; Becker, Andreas ; Pahernik, Sascha ; Simeone, Claudio ; Longo, Nicola ; Zucchi, Alessandro 
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