BACKGROUND: Predicting oncologic outcomes is important for patient counseling, clinical trial design, and biomarker study testing. OBJECTIVE: 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). DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort review of the Mayo Clinic Nephrectomy registry from 1980 to 2010, for patients with nonmetastatic ccRCC, papRCC, and chrRCC. INTERVENTION: Partial or radical nephrectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: 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. RESULTS AND LIMITATIONS: 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. CONCLUSIONS: 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. PATIENT SUMMARY: 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.
BACKGROUND: Predicting oncologic outcomes is important for patient counseling, clinical trial design, and biomarker study testing. OBJECTIVE: 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). DESIGN, SETTING, AND PARTICIPANTS: Retrospective cohort review of the Mayo Clinic Nephrectomy registry from 1980 to 2010, for patients with nonmetastatic ccRCC, papRCC, and chrRCC. INTERVENTION: Partial or radical nephrectomy. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: 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. RESULTS AND LIMITATIONS: 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. CONCLUSIONS: 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. PATIENT SUMMARY: 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.
Authors: Wenxin Xu; Maneka Puligandla; Judith Manola; Andrea J Bullock; Daniel Tamasauskas; David F McDermott; Michael B Atkins; Naomi B Haas; Keith Flaherty; Robert G Uzzo; Janice P Dutcher; Robert S DiPaola; Rupal S Bhatt Journal: Clin Cancer Res Date: 2019-08-30 Impact factor: 12.531
Authors: Yasser Ged; Ying-Bei Chen; Andrea Knezevic; Jozefina Casuscelli; Almedina Redzematovic; Renzo G DiNatale; Maria I Carlo; Chung-Han Lee; Darren R Feldman; Sujata Patil; A Ari Hakimi; Paul Russo; Robert J Motzer; Martin H Voss Journal: Clin Genitourin Cancer Date: 2019-04-01 Impact factor: 2.872
Authors: Joana B Neves; Leyre Vanaclocha Saiz; Saeed Dabestani; Maxine G B Tran; Axel Bex; Yasmin Abu-Ghanem; Marta Marchetti; My-Anh Tran-Dang; Soha El-Sheikh; Ravi Barod; Christian Beisland; Umberto Capitanio; David Cullen; Tobias Klatte; Börje Ljungberg; Faiz Mumtaz; Prasad Patki; Grant D Stewart Journal: World J Urol Date: 2021-04-13 Impact factor: 4.226
Authors: Ryan L Steinberg; Robert G Rasmussen; Brett A Johnson; Rashed Ghandour; Alberto Diaz De Leon; Yin Xi; Takeshi Yokoo; Sandy Kim; Payal Kapur; Jeffrey A Cadeddu; Ivan Pedrosa Journal: Eur Radiol Date: 2020-08-08 Impact factor: 5.315