Maxine Sun1, Guillermo De Velasco2, Priscilla K Brastianos3, Ayal A Aizer4, Allison Martin4, Raphael Moreira2, Paul L Nguyen4, Quoc-Dien Trinh5, Toni K Choueiri6. 1. Lank Center for Genitourinary Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Department of Urology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 2. Lank Center for Genitourinary Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. 3. Division of Neuro-Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. 4. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 5. Department of Urology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 6. Lank Center for Genitourinary Oncology, Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA. Electronic address: toni_choueiri@dfci.harvard.edu.
Abstract
BACKGROUND: The incidence of brain metastases (BM) in patients with renal cell carcinoma (RCC) is hypothesized to have increased in the last 2 decades. OBJECTIVE: To define incidence trends according to patient and clinical characteristics, to identify risk factors, and to describe outcomes of patients with BM for RCC. DESIGN, SETTING, AND PARTICIPANTS: Patients diagnosed with RCC between the years 2010 and 2013 within the Surveillance, Epidemiology, and End Results database. An external validation was also considered using patients diagnosed with RCC between 2010 and 2012 within the National Cancer Database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Incidence proportions of BM were calculated. Risk factors correlated with BM at diagnosis were identified via a 1000-bootstrap corrected multivariable logistic regression model. A risk model was then developed and evaluated using measures of predictive accuracy. Overall survival was examined using Cox regression analyses. RESULTS AND LIMITATIONS: The overall incidence proportions of BM at RCC diagnosis was 1.51% (95% confidence interval: 1.39-1.64%). White/other race, clear cell histology, and sarcomatoid differentiation, T2-4 disease, tumor dimension >10 cm, and N+ disease were significantly associated with BM at RCC diagnosis, and retained within the final prediction model. A risk score was created based on these variables (c-index: 0.803). BM at RCC diagnosis occurred in 0.5%, 3.6%, and 7.7% of patients categorized as low risk, intermediate risk, and high risk. Patients with BM were more likely to succumb to any death than those without BM at diagnosis (median overall survival: 6.4 mo vs not reached, respectively, adjusted hazard ratio: 1.87, 95% confidence interval: 1.67-2.08, p < 0.001). The real incidence of BM at RCC diagnosis is likely underestimated given that the observed rate likely reflects patients who presented with symptoms. CONCLUSIONS: Patients with BM at RCC have poor oncological outcomes. We have characterized the epidemiology of BM at RCC diagnosis and developed a clinical risk model for the purpose of predicting the development of BMs in patients diagnosed with a cortical renal mass. PATIENT SUMMARY: In this report we examined recent proportions of patients with brain metastases at kidney cancer diagnosis in a large community database originating from the US. We developed a model that may be used during routine clinical practice to predict brain metastases. The urologic-oncological community may consider baseline imaging for brain metastases in patients without any symptoms but at high risk of having brain metastases according to the risk model. However, the proposed model certainly needs further testing and validation in the clinical setting. Future studies on brain metastases survival and treatment options are also needed.
BACKGROUND: The incidence of brain metastases (BM) in patients with renal cell carcinoma (RCC) is hypothesized to have increased in the last 2 decades. OBJECTIVE: To define incidence trends according to patient and clinical characteristics, to identify risk factors, and to describe outcomes of patients with BM for RCC. DESIGN, SETTING, AND PARTICIPANTS: Patients diagnosed with RCC between the years 2010 and 2013 within the Surveillance, Epidemiology, and End Results database. An external validation was also considered using patients diagnosed with RCC between 2010 and 2012 within the National Cancer Database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Incidence proportions of BM were calculated. Risk factors correlated with BM at diagnosis were identified via a 1000-bootstrap corrected multivariable logistic regression model. A risk model was then developed and evaluated using measures of predictive accuracy. Overall survival was examined using Cox regression analyses. RESULTS AND LIMITATIONS: The overall incidence proportions of BM at RCC diagnosis was 1.51% (95% confidence interval: 1.39-1.64%). White/other race, clear cell histology, and sarcomatoid differentiation, T2-4 disease, tumor dimension >10 cm, and N+ disease were significantly associated with BM at RCC diagnosis, and retained within the final prediction model. A risk score was created based on these variables (c-index: 0.803). BM at RCC diagnosis occurred in 0.5%, 3.6%, and 7.7% of patients categorized as low risk, intermediate risk, and high risk. Patients with BM were more likely to succumb to any death than those without BM at diagnosis (median overall survival: 6.4 mo vs not reached, respectively, adjusted hazard ratio: 1.87, 95% confidence interval: 1.67-2.08, p < 0.001). The real incidence of BM at RCC diagnosis is likely underestimated given that the observed rate likely reflects patients who presented with symptoms. CONCLUSIONS:Patients with BM at RCC have poor oncological outcomes. We have characterized the epidemiology of BM at RCC diagnosis and developed a clinical risk model for the purpose of predicting the development of BMs in patients diagnosed with a cortical renal mass. PATIENT SUMMARY: In this report we examined recent proportions of patients with brain metastases at kidney cancer diagnosis in a large community database originating from the US. We developed a model that may be used during routine clinical practice to predict brain metastases. The urologic-oncological community may consider baseline imaging for brain metastases in patients without any symptoms but at high risk of having brain metastases according to the risk model. However, the proposed model certainly needs further testing and validation in the clinical setting. Future studies on brain metastases survival and treatment options are also needed.
Authors: Shaan Dudani; Guillermo de Velasco; J Connor Wells; Chun Loo Gan; Frede Donskov; Camillo Porta; Anna Fraccon; Felice Pasini; Jae Lyun Lee; Aaron Hansen; Georg A Bjarnason; Benoit Beuselinck; Sumanta K Pal; Takeshi Yuasa; Nils Kroeger; Ravindran Kanesvaran; M Neil Reaume; Christina Canil; Toni K Choueiri; Daniel Y C Heng Journal: JAMA Netw Open Date: 2021-01-04
Authors: Laurens V Beerepoot; Patrick E J Hanssens; Niels J van Ruitenbeek; Vincent K Y Ho; Hans M Westgeest Journal: J Neurooncol Date: 2021-06-25 Impact factor: 4.130