Kidney Cancer Data Research Articles

A Bayesian hierarchical hidden Markov model for clustering and gene selection: Application to kidney cancer gene expression data.

We introduce a Bayesian approach for biclustering that accounts for the prior functional dependence between genes using hidden Markov models (HMMs). We utilize biological knowledge gathered from gene ontologies and the hidden Markov structure to capture the potential coexpression of neighboring genes. Our interpretable model-based clustering characterized each cluster of samples by three groups of features: overexpressed, underexpressed, and irrelevant features. The proposed methods have been implemented in an R package and are used to analyze both the simulated data and The Cancer Genome Atlas kidney cancerdata.

Valuing vicinity: Memory attention framework for context-based semantic segmentation in histopathology.

The segmentation of histopathological whole slide images into tumourous and non-tumourous types of tissue is a challenging task that requires the consideration of both local and global spatial contexts to classify tumourous regions precisely. The identification of subtypes of tumour tissue complicates the issue as the sharpness of separation decreases and the pathologist's reasoning is even more guided by spatial context. However, the identification of detailed tissue types is crucial for providing personalized cancer therapies. Due to the high resolution of whole slide images, existing semantic segmentation methods, restricted to isolated image sections, are incapable of processing context information beyond. To take a step towards better context comprehension, we propose a patch neighbour attention mechanism to query the neighbouring tissue context from a patch embedding memory bank and infuse context embeddings into bottleneck hidden feature maps. Our memory attention framework (MAF) mimics a pathologist's annotation procedure - zooming out and considering surrounding tissue context. The framework can be integrated into any encoder-decoder segmentation method. We evaluate the MAF on two public breast cancer and liver cancer data sets and an internal kidney cancer data set using famous segmentation models (U-Net, DeeplabV3) and demonstrate the superiority over other context-integrating algorithms - achieving a substantial improvement of up to 17% on Dice score. The code is publicly available at https://github.com/tio-ikim/valuing-vicinity.

On the estimation of hazard rate in mixed populations with its application

Mixture of survival and hazard functions have been widely applied to the analysis of data from heterogeneous populations. From the Bayesian point of view, two different predictive mixture hazard rates can be considered: prior and posterior predictive hazard rates. In this paper, we consider a heterogeneous population consists of two sub-populations with different hazard rates where each one follows a Cox proportional hazards model. A prior predictive mixture hazard model is proposed to estimate the hazard rate of the population through the assessment of some potential regression covariates. Under right-censoring, the estimating equations based on martingale are developed to estimate the parameters of the assumed mixture model. The large sample properties of the proposed estimators are established. The finite sample behavior of the resulting estimators is evaluated through simulation studies, and the approach is also applied to a kidney cancer data set collected from a clinical trial.

V11-05 MONOCENTRIC EXPERIENCE OF ROBOT-ASSISTED RADICAL NEPHRECTOMY WITH VENA CAVA THROMBECTOMY: TECHNIQUE AND RESULTS

You have accessJournal of UrologyRenal Oncology II (V11)1 Sep 2021V11-05 MONOCENTRIC EXPERIENCE OF ROBOT-ASSISTED RADICAL NEPHRECTOMY WITH VENA CAVA THROMBECTOMY: TECHNIQUE AND RESULTS Louis Gobinet, Lorenzo Tesi, Nam Son Vuong, Melanie Percot, Laure Dupitout, Helene Simeon, Eric Alezra, Franck Bladou, Gregoire Robert, Marine Gross Goupil, and Jean Christophe Bernhard Louis GobinetLouis Gobinet More articles by this author , Lorenzo TesiLorenzo Tesi More articles by this author , Nam Son VuongNam Son Vuong More articles by this author , Melanie PercotMelanie Percot More articles by this author , Laure DupitoutLaure Dupitout More articles by this author , Helene SimeonHelene Simeon More articles by this author , Eric AlezraEric Alezra More articles by this author , Franck BladouFranck Bladou More articles by this author , Gregoire RobertGregoire Robert More articles by this author , Marine Gross GoupilMarine Gross Goupil More articles by this author , and Jean Christophe BernhardJean Christophe Bernhard More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002073.05AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Open surgery remains the gold standard treatment for renal tumours with thrombus extension of the inferior vena cava (IVC), whereas the robotic approach has been developed by some expert teams. Two comparative studies are available in the literature. This video’s objective was to present the technique and results of robot-assisted radical nephrectomy with IVC thrombectomy. METHODS: We described the surgical technique of the robotic approach, carried out with the Da-Vinci SI (Intuitive Surgical) surgical robot and intra-operative ultrasound (Hitachi). We present the results of the comparative monocentric retrospective study, which included patients undergoing a radical nephrectomy with IVC thrombus from level 1 to 3 (Mayo clinic classification), between December 2015 and December 2019. Clinical data, collected after consent, come from the prospective French kidney cancer data base UroCCR (CNIL DR 2013-206, NCT03293563). Patients were divided into 2 groups, according to the surgical approach, open or robotic. We present univariate and multivariate analysis of perioperative morbidity and oncological findings. RESULTS: The patient, an 81-year-old man, presented an 8cm tumour of the upper and mid-pole of the right kidney, associated with a thrombus of the IVC. The CT-scan for extension work-up was negative. Operative time (OT) was 320 minutes, including 24 minutes of IVC clamping, with 300cc of blood loss. The length of hospital stay (LOS) was 4 days. Pathology report showed a Clear cell renal carcinoma, Fuhrman grade 4, pT3cN0M0.The open-approach group included 30 patients and the robotic group 10 patients. OT was significantly longer in the robotic group (350 vs 208 min (p<0.01) while blood loss (500 vs 1250 mL (p=0.02)) and LOS (7 Vs 10 days (p<0.01) were reduced. There was no significant difference in terms of postoperative complications or oncological results. CONCLUSIONS: The robotic approach for radical nephrectomy with IVC thrombectomy appears to be feasible and safe, for selected patients in expert centers. It leads to longer operative times, but reduced blood loss and shorter length of stay. Source of Funding: none © 2021 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 206Issue Supplement 3September 2021Page: e859-e859 Advertisement Copyright & Permissions© 2021 by American Urological Association Education and Research, Inc.MetricsAuthor Information Louis Gobinet More articles by this author Lorenzo Tesi More articles by this author Nam Son Vuong More articles by this author Melanie Percot More articles by this author Laure Dupitout More articles by this author Helene Simeon More articles by this author Eric Alezra More articles by this author Franck Bladou More articles by this author Gregoire Robert More articles by this author Marine Gross Goupil More articles by this author Jean Christophe Bernhard More articles by this author Expand All Advertisement Loading ...

특징 추출 기법에 따른 신장암 데이터의 분류

특징 추출 기법에 따른 신장암 데이터의 분류

Age-period-cohort analysis of kidney cancer deaths attributable to high body-mass index in China and U.S. adults

BackgroundStatistical data on burden of kidney cancer and the relavant risk factors are valuable for policy-making. This study aims to estimate kidney cancer deaths and high body-mass index (BMI) attributable to the deaths by gender and age group in China adults, compared with U.S.MethodsWe extracted kidney cancer data (1990–2017) about the age-standardized rates using the comparative risk assessment framework of the 2017 Global Burden of Disease study. We performed an age-period-cohort (APC) analysis to estimate trends of kidney cancer mortality attributable to high BMI.ResultsDuring 1990–2017, age-standardized mortality rate of kidney cancer was increasing in China but decreasing in U.S. The mortality attributable to high BMI in China showed a general increasing trend, while that in U.S. men was increasing and tended to be stable in women since 1995. APC analysis showed a similar pattern of age effect between China and U.S. adults, which substantially increased from 20 to 24 to 90–94 age group. Differently, the period effect rapidly increased in China than U.S. adults during 1990–2017. The cohort effect peaked in the earlier cohort born in 1902–1906 in China, and it declined consistently in U.S. with exception of 1902–1906 and 1907–1911 birth cohort.ConclusionsThe kidney cancer deaths attributable to high BMI, and period effect have been generally increasing in China adults, compared with U.S. adults in which the trend tends to be stable in recent years. The rapid aging may also intensify the increasing trend of kidney cancer death in China. Effective measures should be conducted on body weight control and care for kidney cancer prevention.

Classification of Kidney Cancer Data Using Cost-Sensitive Hybrid Deep Learning Approach

Recently, large-scale bioinformatics and genomic data have been generated using advanced biotechnology methods, thus increasing the importance of analyzing such data. Numerous data mining methods have been developed to process genomic data in the field of bioinformatics. We extracted significant genes for the prognosis prediction of 1157 patients using gene expression data from patients with kidney cancer. We then proposed an end-to-end, cost-sensitive hybrid deep learning (COST-HDL) approach with a cost-sensitive loss function for classification tasks on imbalanced kidney cancer data. Here, we combined the deep symmetric auto encoder; the decoder is symmetric to the encoder in terms of layer structure, with reconstruction loss for non-linear feature extraction and neural network with balanced classification loss for prognosis prediction to address data imbalance problems. Combined clinical data from patients with kidney cancer and gene data were used to determine the optimal classification model and estimate classification accuracy by sample type, primary diagnosis, tumor stage, and vital status as risk factors representing the state of patients. Experimental results showed that the COST-HDL approach was more efficient with gene expression data for kidney cancer prognosis than other conventional machine learning and data mining techniques. These results could be applied to extract features from gene biomarkers for prognosis prediction of kidney cancer and prevention and early diagnosis.

TRNA Fragments Show Intertwining with mRNAs of Specific Repeat Content and Have Links to Disparities

tRNA-derived fragments (tRF) are a class of potent regulatory RNAs. We mined the datasets from The Cancer Genome Atlas (TCGA) representing 32 cancer types with a deterministic and exhaustive pipeline for tRNA fragments. We found that mitochondrial tRNAs contribute disproportionally more tRFs than nuclear tRNAs. Through integrative analyses, we uncovered a multitude of statistically significant and context-dependent associations between the identified tRFs and mRNAs. In many of the 32 cancer types, these associations involve mRNAs from developmental processes, receptor tyrosine kinase signaling, the proteasome, and metabolic pathways that include glycolysis, oxidative phosphorylation, and ATP synthesis. Even though the pathways are common to multiple cancers, the association of specific mRNAs with tRFs depends on and differs from cancer to cancer. The associations between tRFs and mRNAs extend to genomic properties as well; specifically, tRFs are positively correlated with shorter genes that have a higher density in repeats, such as ALUs, MIRs, and ERVLs. Conversely, tRFs are negatively correlated with longer genes that have a lower repeat density, suggesting a possible dichotomy between cell proliferation and differentiation. Analyses of bladder, lung, and kidney cancer data indicate that the tRF-mRNA wiring can also depend on a patient's sex. Sex-dependent associations involve cyclin-dependent kinases in bladder cancer, the MAPK signaling pathway in lung cancer, and purine metabolism in kidney cancer. Taken together, these findings suggest diverse and wide-ranging roles for tRFs and highlight the extensive interconnections of tRFs with key cellular processes and human genomic architecture. SIGNIFICANCE: Across 32 TCGA cancer contexts, nuclear and mitochondrial tRNA fragments exhibit associations with mRNAs that belong to concrete pathways, encode proteins with particular destinations, have a biased repeat content, and are sex dependent.

Causal Mediation Analysis for Standardized Mortality Ratios.

Indirectly standardized mortality ratios (SMR) are often used to compare patient outcomes between health care providers as indicators of quality of care. Observed differences in the outcomes raise the question of whether these could be causally attributable to earlier processes or outcomes in the pathway of care that the patients received. Such pathways can be naturally addressed in a causal mediation analysis framework. Adopting causal mediation models allows the total provider effect on outcome to be decomposed into direct and indirect (mediated) effects. This in turn enables quantification of the improvement in patient outcomes due to a hypothetical intervention on the mediator. We formulate the effect decomposition for the indirectly standardized SMR when comparing to a health care system-wide average performance, propose novel model-based and semiparametric estimators for the decomposition, study the properties of these through simulations, and demonstrate their use through application to Ontario kidney cancer data.

Efficient proximal gradient algorithm for inference of differential gene networks

BackgroundGene networks in living cells can change depending on various conditions such as caused by different environments, tissue types, disease states, and development stages. Identifying the differential changes in gene networks is very important to understand molecular basis of various biological process. While existing algorithms can be used to infer two gene networks separately from gene expression data under two different conditions, and then to identify network changes, such an approach does not exploit the similarity between two gene networks, and it is thus suboptimal. A desirable approach would be clearly to infer two gene networks jointly, which can yield improved estimates of network changes.ResultsIn this paper, we developed a proximal gradient algorithm for differential network (ProGAdNet) inference, that jointly infers two gene networks under different conditions and then identifies changes in the network structure. Computer simulations demonstrated that our ProGAdNet outperformed existing algorithms in terms of inference accuracy, and was much faster than a similar approach for joint inference of gene networks. Gene expression data of breast tumors and normal tissues in the TCGA database were analyzed with our ProGAdNet, and revealed that 268 genes were involved in the changed network edges. Gene set enrichment analysis identified a significant number of gene sets related to breast cancer or other types of cancer that are enriched in this set of 268 genes. Network analysis of the kidney cancer data in the TCGA database with ProGAdNet also identified a set of genes involved in network changes, and the majority of the top genes identified have been reported in the literature to be implicated in kidney cancer. These results corroborated that the gene sets identified by ProGAdNet were very informative about the cancer disease status. A software package implementing the ProGAdNet, computer simulations, and real data analysis is available as Additional file 1.ConclusionWith its superior performance over existing algorithms, ProGAdNet provides a valuable tool for finding changes in gene networks, which may aid the discovery of gene-gene interactions changed under different conditions.

Multiswarm heterogeneous binary PSO using win-win approach for improved feature selection in liver and kidney disease diagnosis

Feature selection is a significant preprocessing method in the classification part of an expert system. We propose a new Multiswarm Heterogeneous Binary Particle Swarm Optimization algorithm (MHBPSO) using a Win-Win approach to improve the performance of Binary Particle Swarm Optimization algorithm (BPSO) for feature selection. MHBPSO is a cooperation algorithm, which includes BPSO and its three variants such as Boolean PSO (BoPSO), Self Adjusted Hierarchical Boolean PSO (SAHBoPSO), and Catfish Self Adjusted Hierarchical Boolean PSO (CSAHBoPSO). It performs heterogeneous search on the entire solution space using four different algorithms. Each algorithm shares global best information to another algorithm to select the preeminent global best position. A variant of BoPSO, SAHBoPSO is proposed, in which leaders are identified based on the fitness values for guiding remaining particles and thus forms the hierarchical structure of leaders and its followers. Meanwhile leaders and followers are changed dynamically and consequently changed the hierarchical structure. CSAHBoPSO, which is the version of SAHBoPSO, is also proposed to avoid stagnation in the subsequent iterations. To assess the performance of the proposed algorithms CEC 2013 benchmark functions are employed. In order to validate the proposed algorithms, comparative study with BPSO, BoPSO, VPSO (Mirjalili and Lewis, 2013), HBPSOGA (Wang et al., 2018) and CatfishBPSO (Chuang et al., 2011a) is provided. Experimental results show that SAHBoPSO and CSAHBoPSO algorithm based on BoPSO are promising and significantly better than BPSO, BoPSO, and VPSO. MHBPSO shows the superior improvement in the search ability. In addition, proposed algorithms are tested in the feature selection phase of intelligent liver and kidney cancer diagnostic systems to select elite features from the liver and kidney cancer data. Findings show that the proposed system is proficient in selecting the elite features to classify the tumor as benign or malignant with minimum error rates.

Velocity Bounded Boolean Particle Swarm Optimization for improved feature selection in liver and kidney disease diagnosis

Cancer is one of the foremost causes of death and can be reduced by early diagnosis. Computer Aided Diagnostic system plays an important role in the detection of cancer. Feature selection is an important preprocessing step in the classification phase of the diagnostic system. The feature selection is a NP – hard challenging problem that have many applications in the area relevant to expert and intelligent system. In this study, two new modified Boolean Particle Swarm Optimization algorithms are proposed namely Velocity Bounded BoPSO (VbBoPSO) and Improved Velocity Bounded BoPSO (IVbBoPSO) to solve feature selection problem. Compared to the basic Boolean PSO, these improved algorithms introduce Vmin parameter that makes it more effective in solving feature selection problem. The performance of VbBoPSO and IVbBoPSO are tested over 28 benchmark functions provided by CEC 2013 session. A comparative study of proposed algorithms with the recent modification of Binary Particle Swarm Optimization and Boolean PSO (BoPSO) is provided. The results prove that the proposed algorithms improve the performance of BoPSO significantly. In addition, the proposed algorithms are tested in the feature selection phase of intelligent disease diagnostic system. Experiments are carried out to select elite features from the liver and kidney cancer data. Empirical results illustrate that the proposed system is superior in selecting elite features to achieve highest classification accuracy.

An Additive–Multiplicative Restricted Mean Residual Life Model

AbstractThe mean residual life measures the expected remaining life of a subject who has survived up to a particular time. When survival time distribution is highly skewed or heavy tailed, the restricted mean residual life must be considered. In this paper, we propose an additive–multiplicative restricted mean residual life model to study the association between the restricted mean residual life function and potential regression covariates in the presence of right censoring. This model extends the proportional mean residual life model using an additive model as its covariate dependent baseline. For the suggested model, some covariate effects are allowed to be time‐varying. To estimate the model parameters, martingale estimating equations are developed, and the large sample properties of the resulting estimators are established. In addition, to assess the adequacy of the model, we investigate a goodness of fit test that is asymptotically justified. The proposed methodology is evaluated via simulation studies and further applied to a kidney cancer data set collected from a clinical trial.

Construction of a gene-gene interaction network with a combined score across multiple approaches.

Recent progress in computational methods for inves-tigating physical and functional gene interactions has provided new insights into the complexity of biological processes. An essential part of these methods is presented visually in the form of gene interaction networks that can be valuable in exploring the mechanisms of disease. Here, a combined network based on gene pairs with an extra layer of re-liability was constructed after converting and combining the gene pair scores using a novel algorithm across multiple approaches. Four groups of kidney cancer data sets from ArrayExpress were downloaded and analyzed to identify differentially expressed genes using a rank prod-ucts analysis tool. Gene co-expression network, protein-protein interac-tion, co-occurrence network and a combined network were constructed using empirical Bayesian meta-analysis approach, Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, an odds ratio formula of the cBioPortal for Cancer Genomics and a novel rank algorithm with combined score, respectively. The topological features of these networks were then compared to evaluate their performances. The results indicated that the gene pairs and their relationship rank-ings were not uniform. The values of topological parameters, such as clustering coefficient and the fitting coefficient R(2) of interaction net-work constructed using our ranked based combination score, were much greater than the other networks. The combined network had a classic small world property which transferred information quickly and displayed great resilience to the dysfunction of low-degree hubs with high-clustering and short average path length. It also followed distinct-ly a scale-free network with a higher reliability.

PD10-07 ASYMPTOMATIC BRAIN METASTASIS FROM RENAL CELL CARCINOMA - ARE WE MISSING OUR WINDOW TO CURE?

You have accessJournal of UrologyKidney Cancer: Evaluation/Staging I1 Apr 2014PD10-07 ASYMPTOMATIC BRAIN METASTASIS FROM RENAL CELL CARCINOMA - ARE WE MISSING OUR WINDOW TO CURE? Michael Hanzly, Ahmed Aboumohamed, Terry Creighton, Christopher Foote, Diana Mehedint, Kristopher Attwood, Eric Kauffman, and Thomas Schwaab Michael HanzlyMichael Hanzly More articles by this author , Ahmed AboumohamedAhmed Aboumohamed More articles by this author , Terry CreightonTerry Creighton More articles by this author , Christopher FooteChristopher Foote More articles by this author , Diana MehedintDiana Mehedint More articles by this author , Kristopher AttwoodKristopher Attwood More articles by this author , Eric KauffmanEric Kauffman More articles by this author , and Thomas SchwaabThomas Schwaab More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2014.02.483AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Follow up guidelines for renal cell carcinoma (RCC) recommend brain imaging only in patients with central nervous system (CNS) symptoms. We hypothesized that using symptomatology as an indicator for brain imaging in patients with localized RCC would miss a substantial proportion of patients with asymptomatic brain metastases. METHODS The IRB-approved, prospectively-maintained kidney cancer data base at Roswell Park Cancer Institute was queried for all RCC patients between 1995 and 2011 who developed brain metastases on imaging (CT or MRI). Patients were divided into a cohort of patients with CNS leading CNS imaging and a cohort of patients without CNS symptoms at the time of brain metastases diagnosis. CNS symptoms were defined as any change in the patient’s baseline neurologic exam. Patient demographics, primary treatment for RCC, elapsed time between primary diagnosis and brain metastasis, presence or absence of associated CNS symptomatology, treatment of metastatic site and overall survival following diagnosis of brain metastasis were examined. Student T-Test was used for statistical analysis. RESULTS Out of 898 patients, 52 patients met these criteria. CNS symptoms were absent in 37% of patients with brain metastases. The mean number of metastases was 1.8 per patient with a mean size of 1.0 cm in the asymptomatic group, and 2.7 metastases with a mean size of 1.9 cm in patients with CNS symptoms (p<0.05). One-year overall survival (OS) of patients with CNS symptoms was 81% and 69% at three years, while the asymptomatic group had an OS of 81% 31%, respectively. Interestingly, two patients in the CNS symptom group and one in the asymptomatic group presented with no other metastatic sites. Among both groups, pulmonary metastases were present in 75% of patients at the time of brain metastasis diagnosis (p<0.005). Clinical trial enrolment was the most common reason for brain imaging in asymptomatic patients. CONCLUSIONS This is the largest series examining the presence or absence of CNS symptoms at the time of diagnosis of brain metastases following treatment for RCC. Our findings suggest that patients without CNS symptoms had smaller brain metastases and a worse overall survival. The current urologic guidelines recommending CNS imaging only in patients with symptoms may be missing a subset of the metastatic RCC population with small, potentially treatable brain metastases. © 2014FiguresReferencesRelatedDetails Volume 191Issue 4SApril 2014Page: e284 Peer Review Report Advertisement Copyright & Permissions© 2014MetricsAuthor Information Michael Hanzly More articles by this author Ahmed Aboumohamed More articles by this author Terry Creighton More articles by this author Christopher Foote More articles by this author Diana Mehedint More articles by this author Kristopher Attwood More articles by this author Eric Kauffman More articles by this author Thomas Schwaab More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Brain metastasis from renal cell carcinoma: Are we missing our chance to treat?

485 Background: Brain metastasis from renal cell carcinoma (RCC) occurs approximately 8% of patients with metastatic RCC. The guidelines from the NCCN, EAU, and AUA recommendcerebral imaging only in patients who have symptomatology. The purpose of our study was to review the presenting symptomatology that led to their diagnosis of brain metastases followed by their overall treatment and survival. Methods: Our IRB-approved prospectively-maintained kidney cancer data base was reviewed between from 1995 to 2012. Brain metastases from a primary RCC was identified in 52 patients. Patient demographics including family and social history, primary treatment for RCC, elapsed time between primary diagnosis and brain metastasis, associated symptomatology, treatment of metastatic site and overall survival following diagnosis of brain metastasis were examined. Results: Primary RCC was diagnosed in 986 patients between 1995 and 2012. Brain metastases were identified in 52 patients, 79% were male and 100% Caucasian, 87% had no family history of renal malignancy. Primary treatment for RCC included 75% radical nephrectomy, 10% observation, 7.6% cytoreductive nephrectomy, 5.4% sunitinib, 2% interleukin-2 treatment. Central nervous system (CNS) symptoms were absent in 37% of patients diagnosed with brain metastases, while 73% presented with symptoms. The average number of metastases was 1.78 per patient in the asymptomatic group, and 2.7 metastases in patients with CNS symptoms (p=.18). Elapsed time from primary RCC treatment to diagnosis of brain metastases was an average of 730 days in the asymptomatic group and 1170 days for the symptomatic group (p=0.02). The symptomatic group has overall survival (OS) at one year of 81% and 69% at three years, while the asymptomatic group had an OS of 81% at one year and 31% at 3 years. Pulmonary involvement existed in 75% of both cohorts of patients (p=0.003). Conclusions: Our findings suggest that patients without CNS symptoms with brain metastases had a worse overall survival. The urologic guidelines recommending CNS imaging only in patients with symptoms may be missing a subset of the metastatic RCC population who could potentially benefit from early intervention, thus prolonging overall survival.