Development and Validation of a Nomogram for Predicting Brain Metastasis in Elderly Renal cell carcinoma Patients with Lymph Node Involvement

Development and Validation of a Nomogram for Predicting Brain Metastasis in Elderly Renal cell carcinoma Patients with Lymph Node Involvement | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Development and Validation of a Nomogram for Predicting Brain Metastasis in Elderly Renal cell carcinoma Patients with Lymph Node Involvement Shuzhan Sun, Yuhui He, Fei Wang, Xiaohong Ren, Ying Zhao, Yisen Deng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7518783/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Renal cell carcinoma (RCC) is the most common malignant kidney tumor in the elderly. Although brain metastasis is relatively rare, it is associated with poor prognosis and diminished quality of life. This study aimed to develop and validate a predictive nomogram to estimate brain metastasis risk in elderly RCC patients with lymph node involvement. Methods Data were obtained from the SEER database for patients aged ≥ 65 years with RCC and lymph node metastasis (2013–2021). Univariate and multivariate logistic regression analyses identified independent risk factors for brain metastasis. A nomogram was constructed and validated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). Results A total of 2,475 patients were included and randomly assigned to a training cohort (n = 1,857) and validation cohort (n = 618). Liver metastasis, bone metastasis, radiotherapy, and surgery were independent predictors of brain metastasis. The nomogram demonstrated strong discrimination with AUCs of 0.858 (training) and 0.833 (validation), and excellent calibration in both cohorts. DCA confirmed superior clinical utility compared to conventional TN staging. Conclusion We developed and validated a reliable nomogram for predicting brain metastasis in elderly RCC patients with lymph node involvement. The model shows high discrimination and clinical applicability, and may help identify high-risk individuals who could benefit from early surveillance and individualized treatment strategies. Renal cell carcinoma Brain metastasis Nomogram SEER Risk prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Renal cell carcinoma (RCC) represents the most prevalent genitourinary malignancy among the elderly, comprising approximately 3% of all adult cancers. Significantly, over 70% of newly diagnosed RCC cases are observed in individuals aged 65 years or older[ 1 ]. At diagnosis, approximately 30% of patients present with distant metastases, and an additional 30% of those initially diagnosed with localized disease and treated via nephrectomy subsequently develop distant metastases during follow-up[ 2 ]. The most frequent sites of metastasis include the lungs, bones, liver, and lymph nodes[ 3 ]. Although brain metastases are relatively uncommon, they are associated with significantly reduced survival rates and severely diminished quality of life. Brain involvement typically indicates a poor prognosis, with a median survival of only 3–6 months in the absence of treatment, and approximately 10 months even with multimodal therapy[ 4 , 5 ]. Population-based studies have reported that the incidence of brain metastases in RCC varies from 2–17%, contingent upon the cohort studied [ 6 , 7 ]. Contemporary clinical guidelines, including those issued by the European Association of Urology (EAU) and the American Urological Association (AUA), advocate for cranial imaging solely in the presence of neurological symptoms or laboratory abnormalities [ 8 , 9 ]. This protocol may result in the delayed identification of asymptomatic brain metastases. In elderly patients with lymph node involvement, there is a critical need for early and accurate risk stratification of brain metastases, particularly in resource-constrained settings. Consequently, the prompt identification of high-risk individuals and the implementation of preemptive interventions are crucial for enhancing survival outcomes and informing personalized management strategies in this vulnerable cohort. In clinical practice, magnetic resonance imaging (MRI) and computed tomography (CT) serve as the principal imaging modalities for diagnosing brain metastases. Nevertheless, these techniques are generally utilized only when there is clinical suspicion or following the onset of neurological symptoms, thereby offering limited utility for early risk prediction[ 10 , 11 ]. Current research predominantly concentrates on predicting the risk of distant metastases within the general RCC population, with relatively few studies specifically examining patients with lymph node involvement. Lymph node metastasis is widely acknowledged as an indicator of aggressive tumor biology and is associated with a significantly heightened risk of further dissemination [ 12 , 13 ]. In light of this, identifying robust predictive factors for brain metastases in RCC patients with lymph node involvement is of critical importance. The development of an accurate, accessible, and clinically applicable predictive model tailored to this high-risk subgroup could facilitate earlier surveillance and inform individualized management strategies. In recent years, there has been a notable increase in the development of statistical model-based clinical prediction tools, which present new opportunities for the application of precision medicine in disease management. Among these tools, nomograms have gained significant popularity due to their visual intuitiveness, user-friendliness, and high predictive accuracy. Nomogram models have been widely utilized for prognosis evaluation and risk stratification across various malignancies[ 14 ]. For instance, Wang et al. developed a nomogram utilizing data from the SEER database to predict the risk of distant metastases in elderly patients with RCC. Their model exhibited excellent discrimination and calibration performance, significantly surpassing the traditional TNM staging system, thereby highlighting the clinical potential of nomogram-based tools in the context of renal cancer[ 15 ]. Similarly, Yu et al. constructed a 30-day early warning model for major adverse kidney events (MAKE30) in critically ill patients with sepsis. Their nomogram demonstrated strong clinical utility and decision-making value, further emphasizing the advantages of nomograms in individualized risk prediction[ 16 ]. Consequently, the objective of this study was to employ the Surveillance, Epidemiology, and End Results (SEER) database to identify independent predictors of brain metastases within this high-risk cohort and to develop a user-friendly, clinically applicable nomogram based on these predictors. We anticipate that this predictive tool will offer valuable support for the individualized management of elderly RCC patients with lymph node metastases, thereby contributing to enhanced clinical outcomes and quality of life. Materials and methods Study design We retrieved data on RCC patients with lymph node involvement, diagnosed between 2013 and 2021, from the SEER database. The SEER program, administered by the U.S. National Cancer Institute, includes 18 population-based cancer registries and encompasses approximately 30% of the U.S. population. This database provides comprehensive clinicopathological characteristics and follow-up information for cancer patients, which are publicly accessible. The data utilized in this study were obtained from the SEER database via http://seer.cancer.gov/ . As the SEER database does not contain identifiable patient information and is publicly accessible, neither institutional review board (IRB) approval nor informed consent was necessary. The study adhered fully to the SEER data use policies and guidelines. Patients The study's inclusion criteria comprised: (1) a pathological diagnosis of primary RCC as indicated by ICD-O-3 codes 8260, 8310, 8312, or 8317; and (2) an age of 65 years or older. The exclusion criteria included: (1) unspecified race; (2) presence of bilateral or unilateral renal tumors; (3) indeterminate TN stage; (4) incomplete follow-up data; (5) unspecified tumor size; (6) unspecified surgical method; and (7) a survival duration of less than one month. Data collection We collected demographic data, including age, sex, race, year of diagnosis, and marital status. Furthermore, clinicopathological data were gathered, encompassing tumor histological type, histological grade, tumor laterality, tumor size, T stage, N stage, surgical approach, radiotherapy, chemotherapy, and the presence of distant metastases in the bone, lung, and liver. The primary outcome measure was the incidence of brain metastasis. Marital status was categorized as either married or unmarried, with the unmarried group comprising single, divorced, and widowed individuals. Race was classified into White, Black, and Other, which included American Indian/Alaska Native and Asian/Pacific Islander. The year of diagnosis spanned from 2013 to 2021. The histological subtypes of RCC comprised clear cell RCC, papillary RCC, chromophobe RCC, and several unclassified subtypes. Tumor grading was documented as follows: Grade I (well-differentiated), Grade II (moderately differentiated), Grade III (poorly differentiated), and Grade IV (undifferentiated). Surgical interventions were classified into the following categories: no surgery (code 0), local tumor excision (codes 10–27), partial nephrectomy (code 30), and radical nephrectomy (codes 40–80). Statistical Analysis Eligible patients were randomly allocated to either the training cohort (70%) or the validation cohort (30%). Continuous variables, such as age and tumor size, were reported as mean ± standard deviation (SD), with group comparisons performed using the chi-square test or the nonparametric Mann–Whitney U test, as appropriate. Categorical variables were presented as counts and percentages, and differences between groups were evaluated using the chi-square test. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of brain metastasis. In the training cohort, a univariate logistic regression analysis was initially conducted to screen potential candidate variables. Subsequently, these variables were incorporated into a multivariate logistic regression model utilizing stepwise selection to ascertain independent predictors of brain metastasis. For each variable, hazard ratios (HRs) and 95% confidence intervals (CIs) were documented. Based on the predictors identified, a nomogram was developed to estimate the risk of brain metastasis in elderly patients with RCC and lymph node involvement. The model's discriminatory capacity was assessed using the receiver operating characteristic (ROC) curve and the associated area under the curve (AUC). Calibration curves were constructed to evaluate the concordance between predicted probabilities and observed outcomes. Additionally, decision curve analysis (DCA) was conducted to assess the clinical utility of the nomogram. In summary, the constructed nomogram exhibited strong discriminatory capabilities, accurate calibration, and advantageous clinical applicability, serving as a quantitative instrument for personalized risk stratification and clinical decision-making. All statistical analyses were conducted utilizing R software and SPSS software. A P -value of less than 0.05 was deemed statistically significant. Results Clinical characteristics of patients The study encompassed a cohort of 2,475 elderly patients (aged ≥ 65 years) diagnosed with renal cell carcinoma and exhibiting lymph node involvement, selected in accordance with predefined inclusion and exclusion criteria. Participants were randomly allocated to either the training cohort (n = 1,857) or the validation cohort (n = 618). The mean age of the entire cohort was 73.0 ± 6.3 years. Among the participants, 2,061 (83.3%) identified as White, 1,577 (63.7%) were male, and 1,579 (64.5%) were married. In terms of histological grading, 70 patients (2.8%) presented with Grade I (well-differentiated) tumors, 525 (21.2%) with Grade II (moderately differentiated) tumors, 641 (25.9%) with Grade III (poorly differentiated) tumors, and 308 (12.4%) with Grade IV (undifferentiated) tumors. The mean tumor size was calculated to be 52.3 ± 38.9 mm. The distribution of T stages was as follows: 757 (30.6%) patients were classified as T1a, 559 (22.6%) as T1b, 988 (39.9%) as T2, 101 (4.1%) as T3, and 70 (2.8%) as T4. Concerning surgical interventions, 1,325 patients (53.5%) did not undergo surgery, whereas 27 (1.1%) underwent local tumor excision, 52 (2.1%) received partial nephrectomy, and 1,071 (43.3%) underwent radical nephrectomy. Additionally, chemotherapy was administered to 1,011 patients (40.8%), and radiotherapy was provided to 400 patients (16.2%), respectively. The comprehensive clinicopathological characteristics of all patients are detailed in Table 1 . No significant differences were observed in the baseline characteristics between the training and validation cohorts. Table 1 Clinicopathological information in elderly patients with RCC and lymph node metastasis. Variables ALL (N = 2475) Training cohort (N = 1857) Validation cohort (N = 618) P -value Age 73.0 ± 6.3 73.0 ± 6.2 73.2 ± 6.6 0.701 Gender 0.129 Male 1577 (63.7%) 1167 (62.8%) 410 (66.3%) Female 898 (36.3%) 690 (37.2%) 208 (33.7%) Race 0.847 White 2061 (83.3%) 1551 (83.5%) 510 (82.5%) Black 214 (8.6%) 158 (8.5%) 56 (9.1%) Other 200 (8.1%) 148 (8%) 52 (8.4%) Marital 0.578 Married 1597 (64.5%) 1192 (64.2%) 405 (65.5%) No 878 (35.5%) 665 (35.8%) 213 (34.5%) Tumor-side 0.772 Right 1272 (51.4%) 958 (51.6%) 314 (50.8%) Left 1203 (48.6%) 899 (48.4%) 304 (49.2%) Pathological 0.526 Papillary 332 (13.4%) 250 (13.5%) 82 (13.3%) Clear cell 1412 (57.1%) 1063 (57.2%) 349 (56.5%) Chromophobe 154 (6.2%) 119 (6.4%) 35 (5.7%) Not classified 577 (23.3%) 425 (22.9%) 152 (24.6%) Grade 0.531 I 70(2.8%) 47(2.5%) 23(3.7%) II 525(21.2%) 394(21.2%) 131(21.2%) III 641(25.9%) 480(25.8%) 161(26.1%) IV 308(12.4%) 238(12.8%) 70(11.3%) Unknown 931(37.6%) 698(37.6%) 233(37.7%) T 0.578 T1a 757 (30.6%) 574 (30.9%) 183 (29.6%) T1b 559 (22.6%) 412 (22.2%) 147 (23.8%) T2 988 (39.9%) 750 (40.4%) 238 (38.5%) T3 101 (4.1%) 71 (3.8%) 30 (4.9%) T4 70 (2.8%) 50 (2.7%) 20 (3.2%) Radiation 0.225 Yes 400 (16.2%) 290 (15.6%) 110 (17.8%) No/Unknown 2075 (83.8%) 1567 (84.4%) 508 (82.2%) Chemotherapy 0.929 Yes 1011 (40.8%) 760 (40.9%) 251 (40.6%) No/Unknown 1464 (59.2%) 1097 (59.1%) 367(59.4%) Bone metastasis 0.899 Yes 642 (25.9%) 480 (25.8%) 162 (26.2%) No 1833 (74.1%) 1377 (74.2%) 456 (73.8%) Brain metastasis 0.637 Yes 133 (5.4%) 97 (5.2%) 36 (5.8%) No 2342 (94.6%) 1760 (94.8%) 582 (94.2%) Liver metastasis 0.366 Yes 359 (14.5%) 262 (14.1%) 97 (15.7%) No 2116 (85.5%) 1595 (85.9%) 521 (84.3%) Lung metastasis 0.116 Yes 216 (8.8%) 152 (8.2%) 64 (10.4%) No 2259 (91.3%) 1705 (91.8%) 554 (89.6%) Surgical 0.634 No 1325 (53.5%) 990 (53.3%) 335 (54.2%) Local tumor excision 27 (1.1%) 20 (1.1%) 7 (1.1%) Partial nephrectomy 52 (2.1%) 43 (2.3%) 9 (1.5%) Radical nephrectomy 1071 (43.3%) 804 (43.3%) 267 (43.2%) Tumor Size 52.3 ± 38.9 51.8 ± 35.7 53.9 ± 47.2 0.864 Univariate and Multivariate Logistic Regression Analysis Within the training cohort, a univariate logistic regression analysis was conducted to evaluate the association between various clinicopathological variables and the incidence of brain metastasis (Table 2 ). The analysis revealed significant associations for T stage (OR = 1.245, P = 0.028), surgical intervention (OR = 0.619, P = 0.001), radiotherapy (OR = 21.628, P < 0.001), bone metastasis (OR = 2.881, P < 0.001), and liver metastasis (OR = 2.102, P = 0.020) with brain metastasis. Variables demonstrating significant associations in the univariate analysis were subsequently incorporated into a multivariate logistic regression model (Table 3 ). The multivariate analysis identified radiotherapy as the most significant independent risk factor for brain metastasis (OR = 30.141, 95% CI: 17.016–53.389, P < 0.001), with liver metastasis also significantly associated with an increased risk (OR = 1.920, 95% CI: 1.113–3.312, P = 0.019). Notably, bone metastasis emerged as a protective factor (OR = 0.392, 95% CI: 0.228–0.672, P = 0.001), whereas surgical intervention was correlated with a decreased risk of brain metastasis (OR = 0.719, 95% CI: 0.592–0.874, P = 0.001). Table 2 Univariate analysis of predictive variables of Elderly patients with lymph node metastasis of renal cell carcinoma in the training cohort. Variables OR 95%CI P -value Age 0.997 0.964–1.030 0.855 gender 1.410 0.904–2.201 0.130 Lung 0.866 0.394–1.904 0.721 Race 1.072 0.767–1.498 0.685 Marital 0.900 0.690–1.371 0.623 Tumor Size 0.993 0.986–1.001 0.068 Tumor Side 1.243 0.823–1.877 0.302 Pathological 0.937 0.760–1.155 0.543 T 1.245 1.024–1.514 0.028 Surgical 0.619 0.517–0.740 0.001 Radiation 21.628 13.349–35.043 0.001 Chemotherapy 1.265 0.839–1.906 0.262 Bone metastasis 2.881 1.907–4.353 0.001 Liver metastasis 2.102 1.300-3.401 0.020 Table 3 Multivariate logistic regression analysis of independent predictors of elderly patients with lymph node metastasis of renal cell carcinoma in the training cohort. Variables β SE Wald P -value OR(95%CI) Liver metastasis 0.652 0.278 5.503 0.019 1.920 (1.113–3.312) Bone metastasis -0.938 0.275 11.605 0.001 0.392 (0.228–0.672) Radiation 3.406 0.292 136.321 <0.001 30.141 (17.016–53.389) Surgical -0.330 0.100 10.923 0.001 0.719 (0.592–0.874) Constant -3.445 0.291 140.376 <0.001 0.032 Construction and Validation of the Nomogram A nomogram was developed to predict the risk of brain metastasis in elderly patients with RCC and lymph node involvement, based on identified independent predictors including liver metastasis, bone metastasis, radiotherapy, and surgery (Fig. 2 ). This nomogram visually represents the relative contribution and weighted score of each risk factor. Clinically, each variable is assigned a score according to the patient's characteristics, and the total score is used to determine an individualized probability of developing brain metastasis. Among the predictors, radiotherapy was the most significant contributor to risk prediction, followed by surgery, bone metastasis, and liver metastasis. The model's strong discriminatory ability was confirmed by the AUC, with values of 0.858 (95% CI: 0.753–0.877) in the training cohort and 0.833 (95% CI: 0.694–0.854) in the validation cohort (Fig. 3 ). Calibration curves indicated excellent agreement between predicted and observed probabilities in both cohorts (Fig. 4 ). DCA indicated that the nomogram offered a superior net benefit over a broad spectrum of threshold probabilities (Fig. 5 ), thereby demonstrating substantial clinical utility. In conclusion, we have developed and validated a nomogram designed to predict the risk of brain metastasis in elderly patients with RCC who have lymph node involvement. The model exhibited excellent discrimination (AUC > 0.83), strong calibration, and significant clinical applicability, as evidenced by the high net benefit observed in DCA. Discussion In this study, we developed and validated a nomogram constructed using liver metastasis, bone metastasis, radiotherapy, and surgery to assess the risk of brain metastasis in elderly patients with RCC and lymph node involvement. Utilizing data from the SEER database, the model exhibited robust discriminatory power, achieving AUC values of 0.858 in the training cohort and 0.833 in the validation cohort. Furthermore, the model demonstrated favorable calibration and clinical utility, as evaluated through DCA. This tool facilitates the early and precise identification of high-risk individuals in resource-limited settings, thereby informing optimized decisions regarding the frequency of cranial imaging and follow-up strategies. Current major clinical guidelines do not advocate for routine brain imaging in patients with localized disease or during standard surveillance[ 17 , 18 ]. Instead, cranial imaging is generally recommended only in the presence of neurological symptoms or specific clinical indicators[ 19 , 20 ]. Nonetheless, a growing body of evidence suggests that a subset of renal cell carcinoma (RCC) patients may exhibit asymptomatic brain metastases either at the commencement of systemic therapy or during the eligibility screening process for clinical trials[ 21 ]. These patients generally experience significantly poorer prognoses, highlighting the practical importance of implementing proactive identification strategies specifically designed for high-risk sub-populations. In recent years, numerous studies have been undertaken to predict distant metastases in patients with renal cell carcinoma (RCC); however, the majority have concentrated either on the general population or on a single metastatic site. Tong et al. developed a nomogram specifically for predicting brain metastasis in RCC patients, achieving a concordance index (C-index) of 0.924. They validated its clinical utility through calibration curves and DCA, thereby providing foundational evidence that the risk of brain metastasis can be quantified using conventional clinical variables[ 22 ]. Our findings align with their principal conclusion that patterns of distant metastasis are significant indicators of brain metastasis risk. Nevertheless, our study targets a distinct clinical subgroup—elderly RCC patients with lymph node involvement—thereby offering enhanced relevance to practical clinical decisions, such as the consideration of earlier or more frequent cranial imaging in this high-risk population. This focus augments the clinical utility of our model during postoperative follow-up and prior to the initiation of systemic therapy. Furthermore, numerous multicenter studies have investigated machine learning-based models for predicting brain metastasis. Kim et al. assessed six algorithms and determined that AdaBoost achieved an AUC of approximately 0.716[ 23 ]. In contrast, our nomogram demonstrated superior discriminatory power utilizing fewer, routinely accessible clinical variables, while maintaining robust interpretability and practical applicability at the bedside—attributes that are especially beneficial in resource-constrained environments and in the treatment of elderly patients. Additionally, cross-tumor generalized risk models leveraging electronic health records (EHR) have been proposed to estimate the risk of brain metastasis at the initial cancer diagnosis stage, achieving an AUC of approximately 0.91 in RCC)cohorts[ 24 ]. Despite their promising statistical performance across various cancer types, these models were not specifically designed for elderly RCC patients with lymph node involvement. Their clinical implementation is further hindered by issues related to variable interpretability, portability, and local applicability. Collectively, these factors underscore the distinctive importance and compelling justification for the development of a specialized predictive tool for brain metastasis in elderly patients with RCC who have established lymph node metastasis. This study identified a significant association between liver metastasis and an increased risk of brain metastasis, corroborating findings from previous research. Mechanistically, the elevated expression of CXCR4 in liver metastases, in conjunction with its ligand CXCL12 secreted by astrocytes and endothelial cells within the brain, establishes a chemotactic axis that facilitates the directional migration, adhesion, and survival of tumor cells within the brain microenvironment[ 25 – 27 ]. Previous studies have reported heightened CXCR4 positivity in both liver metastases of renal cell carcinoma (RCC) lesions and corresponding brain metastases, reinforcing its role in the organotropic dissemination of RCC to the brain. Furthermore, integrins αvβ3 and αvβ8 have been observed to be upregulated in brain metastatic lesions compared to liver metastatic lesions across various cancers, including RCC. These integrins play a crucial role in mediating adhesion, invasion, and traversal of the blood–brain barrier (BBB) [ 28 ]. Additionally, tumor-derived exosomes, which transport microRNAs and proteins, have been demonstrated to induce the formation of a pre-metastatic niche (PMN) at distant sites by enhancing vascular permeability, modulating the immune response, and remodeling the extracellular matrix[ 29 ]. These molecular characteristics collectively contribute to a highly invasive metastatic phenotype, which may explain the clinical association observed between liver metastasis and brain metastasis. Consequently, liver metastasis could serve as a clinically observable surrogate marker for increased metastatic aggressiveness, biologically linked to an elevated risk of brain metastasis. In elderly patients with RCC who present with lymph node involvement and concurrent liver metastasis, clinicians might consider employing a lower nomogram threshold as an early indicator for initiating cranial MRI screening. Our study intriguingly identified a negative correlation between bone metastasis and brain metastasis, suggesting a potential site-specific "competitive mechanism" among metastatic niches. The bone marrow microenvironment is characterized by high levels of CXCL12, which effectively retains CXCR4-expressing RCC cells, thereby promoting preferential dissemination or expansion towards the bone [ 30 ]. Conversely, successful colonization of the brain necessitates that tumor cells overcome additional biological barriers, such as adhesion to and transmigration across the BBB. This process involves integrins, basement membrane degradation, and endothelial penetration [ 31 ]. These distinct trajectories may compete at the population level, with certain RCC cell subsets favoring bone homing while others exhibit a predisposition towards brain tropism, resulting in an inverse correlation between the two metastatic sites. It is noteworthy that bone metastasis was positively associated with brain metastasis in univariate analysis; however, this effect was reversed in multivariate models. This shift likely represents a statistical net effect rather than an actual biological protective factor. In this study, radiotherapy emerged as the most significant risk factor for brain metastasis. Nonetheless, this association should not be construed as evidence that radiotherapy directly causes brain metastases. Instead, it exemplifies a classic case of confounding by indication. In the practical management of RCC, radiotherapy-including stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT)-is commonly employed for the local control of existing symptomatic, solitary, or oligometastatic lesions, frequently involving the brain or bone. Consequently, patients undergoing radiotherapy are more likely to belong to a subpopulation characterized by a greater metastatic burden or pre-existing central nervous system involvement, rather than radiotherapy being the causal factor for brain metastasis[ 32 ]. From a methodological perspective, radiotherapy should be considered more as a proxy indicator of disease severity rather than an independent etiological factor. It is important to acknowledge that radiotherapy can influence the tumor microenvironment through its effects on endothelial cells and astrocyte activation. However, current evidence remains insufficient to substantiate a causal pathway by which radiotherapy directly increases the risk of brain metastasis. Nonetheless, the current body of evidence does not sufficiently substantiate a causal relationship in which radiotherapy directly elevates the incidence of brain metastasis. In contrast, surgical intervention has been significantly correlated with a decreased risk of brain metastasis. Prior research has indicated that the primary tumor acts as a principal source of circulating tumor cells (CTCs) and tumor-derived exosomes, which facilitate the establishment of distant pre-metastatic niches (PMNs)[ 33 , 34 ]. The surgical resection of the primary tumor may reduce the continuous release of pro-metastatic signals, including angiogenic factors, immunosuppressive vesicles, and extracellular matrix–remodeling enzymes, thereby indirectly decreasing the probability of brain colonization and enhancing survival outcomes in patients with metastatic RCC[ 35 ]. This effect may be particularly pertinent in elderly patients, for whom surgical intervention remains a crucial strategy for local disease management and delaying the progression of distant metastases. Our findings advocate for the consideration of surgical intervention when clinically feasible. It is crucial to acknowledge the impact of selection bias: patients who undergo surgical intervention are typically those with resectable tumors, superior overall health status, and a lower initial metastatic burden. Consequently, the observed protective effect may be partially confounded by these favorable baseline characteristics. Current guidelines, such as those from the European Association of Urology (EAU) and the American Urological Association (AUA), generally advise against routine cranial imaging for asymptomatic RCC patients during standard follow-up. Nonetheless, prompt brain MRI or CT is strongly recommended in the presence of neurological symptoms or high clinical suspicion [ 9 , 19 ]. The present study introduces a risk stratification tool specifically tailored for elderly RCC patients with lymph node involvement, facilitating the identification of those most likely to benefit from early cranial imaging—within a framework that does not endorse universal screening. This nomogram may function as a quantitative trigger for guideline-recommended, individualized imaging escalation in select patients. In the context of synchronous or multi-organ metastases, previous multicenter studies have indicated that asymptomatic brain metastases are relatively common and are correlated with significantly poorer outcomes[ 7 , 36 ]. These findings advocate for the implementation of more proactive baseline or early surveillance imaging strategies in high-risk subgroups, such as the cohort examined in our study. Nevertheless, this study has several limitations. Firstly, as a retrospective analysis, it is inherently vulnerable to potential confounding factors, despite efforts to mitigate selection bias through stringent inclusion criteria and robust statistical methods. Secondly, the SEER database lacks crucial molecular and genetic information-such as the mutation status of VHL, PBRM1, and SETD2-and does not provide treatment-specific data on targeted therapies or immunotherapies, both of which are known to significantly affect the metastatic behavior and prognosis of RCC. Furthermore, potential collinearity and competing risks among different patterns of distant metastasis may have influenced the direction or strength of association for certain variables. In summary, the present predictive model underwent only internal validation, necessitating further external validation with prospective and multicenter datasets to ascertain its accuracy and generalizability. Conclusion We successfully developed and validated a nomogram utilizing data from the SEER database to predict the risk of brain metastasis in elderly patients with RCC and lymph node involvement. The analysis identified liver metastasis, bone metastasis, radiotherapy, and surgery as independent predictive factors. The model exhibited strong discrimination, calibration, and clinical utility in both the training and validation cohorts. This nomogram holds potential as a tool for clinicians to visually assess an individual patient's risk of brain metastasis, thereby providing evidence-based guidance for customizing follow-up frequency, imaging surveillance, and personalized treatment strategies. Declarations Conflict of interests The authors declare that they have no conflict of interest. Data availability statement Publicly available datasets were analyzed in this study. This data can be found here: https://seer.Cancer.gov/. Ethics statement The data of this study is obtained from the SEER database. The patients’ data is public and anonymous, so this study does not require ethical approval and informed consent. Author contributions Shuzhan Sun: resources; methodology; formal analysis; investigation; visualization; writing—original draft. Yuhui He: resources; methodology; formal analysis; investigation; validation; writing—original draft. Fei Wang: resources; writing—original draft. Xiaohong Ren: resources; formal analysis; writing— original draft. Ying Zhao: validation; writing—original draft; Yisen Deng: conception and design; writing—review and editing; supervision; funding acquisition. Jianfeng Wang: conception and design; writing— review and editing; supervision; data curation; project administration; funding acquisition. Funding None Data availability statement The data generated and analyzed in this study are available upon reasonable request from the corresponding author. References Siegel RL, Giaquinto AN, Jemal A: Cancer statistics, 2024. CA Cancer J Clin 2024, 74(1):12-49. Escudier B, Porta C, Schmidinger M, Rioux-Leclercq N, Bex A, Khoo V, Gruenvald V, Horwich A: Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol 2016, 27(suppl 5):v58-v68. 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Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7518783","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":519836327,"identity":"bae63bea-0b4f-40af-a397-039e36e9934f","order_by":0,"name":"Shuzhan 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2","display":"","copyAsset":false,"role":"figure","size":68481,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for brain metastasis in elderly RCC patients with lymph node involvement\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7518783/v1/77ef771ad96d68ce6dc3e805.png"},{"id":93009209,"identity":"35ade959-b633-4063-aded-7d76d984567f","added_by":"auto","created_at":"2025-10-08 07:08:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109679,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC of the nomogram of training cohort \u003cstrong\u003e(A) \u003c/strong\u003eand validation cohort \u003cstrong\u003e(B)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7518783/v1/d6491a01023a5e4ab3e196ae.png"},{"id":93009207,"identity":"0af42158-a46d-4a22-b29e-5173f805ee20","added_by":"auto","created_at":"2025-10-08 07:08:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104892,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram’s calibration curve in the training cohort \u003cstrong\u003e(A)\u003c/strong\u003e and the validation cohort \u003cstrong\u003e(B)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7518783/v1/62ae5d922e27228ffa1316c7.png"},{"id":93013071,"identity":"1c5fc9a4-89e3-41e7-81c4-239ebb95597e","added_by":"auto","created_at":"2025-10-08 07:24:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":138658,"visible":true,"origin":"","legend":"\u003cp\u003eThe DCA of the nomogram of training cohort \u003cstrong\u003e(A) \u003c/strong\u003eand validation cohort \u003cstrong\u003e(B)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7518783/v1/d1a264b4829b878c0b4b03b0.png"},{"id":95802246,"identity":"3ca78ad0-b225-42d9-a6da-536aa2bdc135","added_by":"auto","created_at":"2025-11-13 08:27:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1350482,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7518783/v1/67a0cdc5-81e9-447a-96de-52b341413369.pdf"}],"financialInterests":"","formattedTitle":"Development and Validation of a Nomogram for Predicting Brain Metastasis in Elderly Renal cell carcinoma Patients with Lymph Node Involvement","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) represents the most prevalent genitourinary malignancy among the elderly, comprising approximately 3% of all adult cancers. Significantly, over 70% of newly diagnosed RCC cases are observed in individuals aged 65 years or older[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. At diagnosis, approximately 30% of patients present with distant metastases, and an additional 30% of those initially diagnosed with localized disease and treated via nephrectomy subsequently develop distant metastases during follow-up[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The most frequent sites of metastasis include the lungs, bones, liver, and lymph nodes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although brain metastases are relatively uncommon, they are associated with significantly reduced survival rates and severely diminished quality of life. Brain involvement typically indicates a poor prognosis, with a median survival of only 3\u0026ndash;6 months in the absence of treatment, and approximately 10 months even with multimodal therapy[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Population-based studies have reported that the incidence of brain metastases in RCC varies from 2\u0026ndash;17%, contingent upon the cohort studied [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Contemporary clinical guidelines, including those issued by the European Association of Urology (EAU) and the American Urological Association (AUA), advocate for cranial imaging solely in the presence of neurological symptoms or laboratory abnormalities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This protocol may result in the delayed identification of asymptomatic brain metastases. In elderly patients with lymph node involvement, there is a critical need for early and accurate risk stratification of brain metastases, particularly in resource-constrained settings. Consequently, the prompt identification of high-risk individuals and the implementation of preemptive interventions are crucial for enhancing survival outcomes and informing personalized management strategies in this vulnerable cohort.\u003c/p\u003e\u003cp\u003eIn clinical practice, magnetic resonance imaging (MRI) and computed tomography (CT) serve as the principal imaging modalities for diagnosing brain metastases. Nevertheless, these techniques are generally utilized only when there is clinical suspicion or following the onset of neurological symptoms, thereby offering limited utility for early risk prediction[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Current research predominantly concentrates on predicting the risk of distant metastases within the general RCC population, with relatively few studies specifically examining patients with lymph node involvement. Lymph node metastasis is widely acknowledged as an indicator of aggressive tumor biology and is associated with a significantly heightened risk of further dissemination [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In light of this, identifying robust predictive factors for brain metastases in RCC patients with lymph node involvement is of critical importance. The development of an accurate, accessible, and clinically applicable predictive model tailored to this high-risk subgroup could facilitate earlier surveillance and inform individualized management strategies.\u003c/p\u003e\u003cp\u003eIn recent years, there has been a notable increase in the development of statistical model-based clinical prediction tools, which present new opportunities for the application of precision medicine in disease management. Among these tools, nomograms have gained significant popularity due to their visual intuitiveness, user-friendliness, and high predictive accuracy. Nomogram models have been widely utilized for prognosis evaluation and risk stratification across various malignancies[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For instance, Wang et al. developed a nomogram utilizing data from the SEER database to predict the risk of distant metastases in elderly patients with RCC. Their model exhibited excellent discrimination and calibration performance, significantly surpassing the traditional TNM staging system, thereby highlighting the clinical potential of nomogram-based tools in the context of renal cancer[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Similarly, Yu et al. constructed a 30-day early warning model for major adverse kidney events (MAKE30) in critically ill patients with sepsis. Their nomogram demonstrated strong clinical utility and decision-making value, further emphasizing the advantages of nomograms in individualized risk prediction[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eConsequently, the objective of this study was to employ the Surveillance, Epidemiology, and End Results (SEER) database to identify independent predictors of brain metastases within this high-risk cohort and to develop a user-friendly, clinically applicable nomogram based on these predictors. We anticipate that this predictive tool will offer valuable support for the individualized management of elderly RCC patients with lymph node metastases, thereby contributing to enhanced clinical outcomes and quality of life.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy design\u003c/h2\u003e\u003cp\u003eWe retrieved data on RCC patients with lymph node involvement, diagnosed between 2013 and 2021, from the SEER database. The SEER program, administered by the U.S. National Cancer Institute, includes 18 population-based cancer registries and encompasses approximately 30% of the U.S. population. This database provides comprehensive clinicopathological characteristics and follow-up information for cancer patients, which are publicly accessible. The data utilized in this study were obtained from the SEER database via \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://seer.cancer.gov/\u003c/span\u003e\u003cspan address=\"http://seer.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. As the SEER database does not contain identifiable patient information and is publicly accessible, neither institutional review board (IRB) approval nor informed consent was necessary. The study adhered fully to the SEER data use policies and guidelines.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePatients\u003c/h3\u003e\n\u003cp\u003eThe study's inclusion criteria comprised: (1) a pathological diagnosis of primary RCC as indicated by ICD-O-3 codes 8260, 8310, 8312, or 8317; and (2) an age of 65 years or older. The exclusion criteria included: (1) unspecified race; (2) presence of bilateral or unilateral renal tumors; (3) indeterminate TN stage; (4) incomplete follow-up data; (5) unspecified tumor size; (6) unspecified surgical method; and (7) a survival duration of less than one month.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eWe collected demographic data, including age, sex, race, year of diagnosis, and marital status. Furthermore, clinicopathological data were gathered, encompassing tumor histological type, histological grade, tumor laterality, tumor size, T stage, N stage, surgical approach, radiotherapy, chemotherapy, and the presence of distant metastases in the bone, lung, and liver. The primary outcome measure was the incidence of brain metastasis. Marital status was categorized as either married or unmarried, with the unmarried group comprising single, divorced, and widowed individuals. Race was classified into White, Black, and Other, which included American Indian/Alaska Native and Asian/Pacific Islander. The year of diagnosis spanned from 2013 to 2021. The histological subtypes of RCC comprised clear cell RCC, papillary RCC, chromophobe RCC, and several unclassified subtypes. Tumor grading was documented as follows: Grade I (well-differentiated), Grade II (moderately differentiated), Grade III (poorly differentiated), and Grade IV (undifferentiated). Surgical interventions were classified into the following categories: no surgery (code 0), local tumor excision (codes 10\u0026ndash;27), partial nephrectomy (code 30), and radical nephrectomy (codes 40\u0026ndash;80).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eEligible patients were randomly allocated to either the training cohort (70%) or the validation cohort (30%). Continuous variables, such as age and tumor size, were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), with group comparisons performed using the chi-square test or the nonparametric Mann\u0026ndash;Whitney U test, as appropriate. Categorical variables were presented as counts and percentages, and differences between groups were evaluated using the chi-square test. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of brain metastasis. In the training cohort, a univariate logistic regression analysis was initially conducted to screen potential candidate variables. Subsequently, these variables were incorporated into a multivariate logistic regression model utilizing stepwise selection to ascertain independent predictors of brain metastasis. For each variable, hazard ratios (HRs) and 95% confidence intervals (CIs) were documented. Based on the predictors identified, a nomogram was developed to estimate the risk of brain metastasis in elderly patients with RCC and lymph node involvement. The model's discriminatory capacity was assessed using the receiver operating characteristic (ROC) curve and the associated area under the curve (AUC). Calibration curves were constructed to evaluate the concordance between predicted probabilities and observed outcomes. Additionally, decision curve analysis (DCA) was conducted to assess the clinical utility of the nomogram. In summary, the constructed nomogram exhibited strong discriminatory capabilities, accurate calibration, and advantageous clinical applicability, serving as a quantitative instrument for personalized risk stratification and clinical decision-making.\u003c/p\u003e\u003cp\u003eAll statistical analyses were conducted utilizing R software and SPSS software. A \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 was deemed statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eClinical characteristics of patients\u003c/h2\u003e\u003cp\u003eThe study encompassed a cohort of 2,475 elderly patients (aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years) diagnosed with renal cell carcinoma and exhibiting lymph node involvement, selected in accordance with predefined inclusion and exclusion criteria. Participants were randomly allocated to either the training cohort (n\u0026thinsp;=\u0026thinsp;1,857) or the validation cohort (n\u0026thinsp;=\u0026thinsp;618). The mean age of the entire cohort was 73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3 years. Among the participants, 2,061 (83.3%) identified as White, 1,577 (63.7%) were male, and 1,579 (64.5%) were married. In terms of histological grading, 70 patients (2.8%) presented with Grade I (well-differentiated) tumors, 525 (21.2%) with Grade II (moderately differentiated) tumors, 641 (25.9%) with Grade III (poorly differentiated) tumors, and 308 (12.4%) with Grade IV (undifferentiated) tumors. The mean tumor size was calculated to be 52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;38.9 mm. The distribution of T stages was as follows: 757 (30.6%) patients were classified as T1a, 559 (22.6%) as T1b, 988 (39.9%) as T2, 101 (4.1%) as T3, and 70 (2.8%) as T4. Concerning surgical interventions, 1,325 patients (53.5%) did not undergo surgery, whereas 27 (1.1%) underwent local tumor excision, 52 (2.1%) received partial nephrectomy, and 1,071 (43.3%) underwent radical nephrectomy. Additionally, chemotherapy was administered to 1,011 patients (40.8%), and radiotherapy was provided to 400 patients (16.2%), respectively. The comprehensive clinicopathological characteristics of all patients are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. No significant differences were observed in the baseline characteristics between the training and validation cohorts.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicopathological information in elderly patients with RCC and lymph node metastasis.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eALL (N\u0026thinsp;=\u0026thinsp;2475)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTraining cohort (N\u0026thinsp;=\u0026thinsp;1857)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eValidation cohort (N\u0026thinsp;=\u0026thinsp;618)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e73.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.701\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1577 (63.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1167 (62.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e410 (66.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e898 (36.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e690 (37.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e208 (33.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.847\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWhite\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2061 (83.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1551 (83.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e510 (82.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBlack\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e214 (8.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e158 (8.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56 (9.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e200 (8.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e148 (8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52 (8.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarried\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1597 (64.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1192 (64.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e405 (65.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e878 (35.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e665 (35.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e213 (34.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor-side\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.772\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1272 (51.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e958 (51.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e314 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLeft\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1203 (48.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e899 (48.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e304 (49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.526\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePapillary\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e332 (13.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e250 (13.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e82 (13.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClear cell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1412 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1063 (57.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e349 (56.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChromophobe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e154 (6.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e119 (6.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e35 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNot classified\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e577 (23.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e425 (22.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e152 (24.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.531\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70(2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47(2.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e23(3.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e525(21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e394(21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e131(21.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIII\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e641(25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e480(25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161(26.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e308(12.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e238(12.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e70(11.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e931(37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e698(37.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e233(37.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e757 (30.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e574 (30.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e183 (29.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e559 (22.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e412 (22.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e147 (23.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e988 (39.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e750 (40.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e238 (38.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71 (3.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30 (4.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e70 (2.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e50 (2.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20 (3.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e400 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e290 (15.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e110 (17.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo/Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2075 (83.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1567 (84.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e508 (82.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.929\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1011 (40.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e760 (40.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e251 (40.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo/Unknown\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1464 (59.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1097 (59.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e367(59.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.899\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e642 (25.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e480 (25.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e162 (26.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1833 (74.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1377 (74.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e456 (73.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrain metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.637\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e133 (5.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e97 (5.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (5.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2342 (94.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1760 (94.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e582 (94.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e359 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e262 (14.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e97 (15.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2116 (85.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1595 (85.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e521 (84.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.116\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e216 (8.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e152 (8.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e64 (10.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2259 (91.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1705 (91.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e554 (89.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.634\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1325 (53.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e990 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e335 (54.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLocal tumor excision\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e27 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7 (1.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePartial nephrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52 (2.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (2.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (1.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadical nephrectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1071 (43.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e804 (43.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e267 (43.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.3\u0026thinsp;\u0026plusmn;\u0026thinsp;38.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.8\u0026thinsp;\u0026plusmn;\u0026thinsp;35.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.9\u0026thinsp;\u0026plusmn;\u0026thinsp;47.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.864\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eUnivariate and Multivariate Logistic Regression Analysis\u003c/h3\u003e\n\u003cp\u003eWithin the training cohort, a univariate logistic regression analysis was conducted to evaluate the association between various clinicopathological variables and the incidence of brain metastasis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analysis revealed significant associations for T stage (OR\u0026thinsp;=\u0026thinsp;1.245, P\u0026thinsp;=\u0026thinsp;0.028), surgical intervention (OR\u0026thinsp;=\u0026thinsp;0.619, P\u0026thinsp;=\u0026thinsp;0.001), radiotherapy (OR\u0026thinsp;=\u0026thinsp;21.628, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), bone metastasis (OR\u0026thinsp;=\u0026thinsp;2.881, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and liver metastasis (OR\u0026thinsp;=\u0026thinsp;2.102, P\u0026thinsp;=\u0026thinsp;0.020) with brain metastasis. Variables demonstrating significant associations in the univariate analysis were subsequently incorporated into a multivariate logistic regression model (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The multivariate analysis identified radiotherapy as the most significant independent risk factor for brain metastasis (OR\u0026thinsp;=\u0026thinsp;30.141, 95% CI: 17.016\u0026ndash;53.389, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with liver metastasis also significantly associated with an increased risk (OR\u0026thinsp;=\u0026thinsp;1.920, 95% CI: 1.113\u0026ndash;3.312, P\u0026thinsp;=\u0026thinsp;0.019). Notably, bone metastasis emerged as a protective factor (OR\u0026thinsp;=\u0026thinsp;0.392, 95% CI: 0.228\u0026ndash;0.672, P\u0026thinsp;=\u0026thinsp;0.001), whereas surgical intervention was correlated with a decreased risk of brain metastasis (OR\u0026thinsp;=\u0026thinsp;0.719, 95% CI: 0.592\u0026ndash;0.874, P\u0026thinsp;=\u0026thinsp;0.001).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate analysis of predictive variables of Elderly patients with lymph node metastasis of renal cell carcinoma in the training cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.997\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.964\u0026ndash;1.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.855\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003egender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.904\u0026ndash;2.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.130\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLung\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.394\u0026ndash;1.904\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.767\u0026ndash;1.498\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.685\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMarital\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.900\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.690\u0026ndash;1.371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.623\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Size\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.986\u0026ndash;1.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.068\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor Side\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.823\u0026ndash;1.877\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.302\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePathological\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.760\u0026ndash;1.155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.543\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.024\u0026ndash;1.514\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.028\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.517\u0026ndash;0.740\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.349\u0026ndash;35.043\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChemotherapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.265\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.839\u0026ndash;1.906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.907\u0026ndash;4.353\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.300-3.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMultivariate logistic regression analysis of independent predictors of elderly patients with lymph node metastasis of renal cell carcinoma in the training cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eβ\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWald\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOR(95%CI)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiver metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.652\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.503\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.920 (1.113\u0026ndash;3.312)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBone metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.938\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.605\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.392 (0.228\u0026ndash;0.672)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRadiation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.292\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e30.141 (17.016\u0026ndash;53.389)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSurgical\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.330\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.923\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.719 (0.592\u0026ndash;0.874)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConstant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-3.445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e140.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.032\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eConstruction and Validation of the Nomogram\u003c/h3\u003e\n\u003cp\u003eA nomogram was developed to predict the risk of brain metastasis in elderly patients with RCC and lymph node involvement, based on identified independent predictors including liver metastasis, bone metastasis, radiotherapy, and surgery (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). This nomogram visually represents the relative contribution and weighted score of each risk factor. Clinically, each variable is assigned a score according to the patient's characteristics, and the total score is used to determine an individualized probability of developing brain metastasis. Among the predictors, radiotherapy was the most significant contributor to risk prediction, followed by surgery, bone metastasis, and liver metastasis. The model's strong discriminatory ability was confirmed by the AUC, with values of 0.858 (95% CI: 0.753\u0026ndash;0.877) in the training cohort and 0.833 (95% CI: 0.694\u0026ndash;0.854) in the validation cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Calibration curves indicated excellent agreement between predicted and observed probabilities in both cohorts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). DCA indicated that the nomogram offered a superior net benefit over a broad spectrum of threshold probabilities (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), thereby demonstrating substantial clinical utility. In conclusion, we have developed and validated a nomogram designed to predict the risk of brain metastasis in elderly patients with RCC who have lymph node involvement. The model exhibited excellent discrimination (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.83), strong calibration, and significant clinical applicability, as evidenced by the high net benefit observed in DCA.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and validated a nomogram constructed using liver metastasis, bone metastasis, radiotherapy, and surgery to assess the risk of brain metastasis in elderly patients with RCC and lymph node involvement. Utilizing data from the SEER database, the model exhibited robust discriminatory power, achieving AUC values of 0.858 in the training cohort and 0.833 in the validation cohort. Furthermore, the model demonstrated favorable calibration and clinical utility, as evaluated through DCA. This tool facilitates the early and precise identification of high-risk individuals in resource-limited settings, thereby informing optimized decisions regarding the frequency of cranial imaging and follow-up strategies. Current major clinical guidelines do not advocate for routine brain imaging in patients with localized disease or during standard surveillance[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Instead, cranial imaging is generally recommended only in the presence of neurological symptoms or specific clinical indicators[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Nonetheless, a growing body of evidence suggests that a subset of renal cell carcinoma (RCC) patients may exhibit asymptomatic brain metastases either at the commencement of systemic therapy or during the eligibility screening process for clinical trials[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These patients generally experience significantly poorer prognoses, highlighting the practical importance of implementing proactive identification strategies specifically designed for high-risk sub-populations.\u003c/p\u003e\u003cp\u003eIn recent years, numerous studies have been undertaken to predict distant metastases in patients with renal cell carcinoma (RCC); however, the majority have concentrated either on the general population or on a single metastatic site. Tong et al. developed a nomogram specifically for predicting brain metastasis in RCC patients, achieving a concordance index (C-index) of 0.924. They validated its clinical utility through calibration curves and DCA, thereby providing foundational evidence that the risk of brain metastasis can be quantified using conventional clinical variables[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our findings align with their principal conclusion that patterns of distant metastasis are significant indicators of brain metastasis risk. Nevertheless, our study targets a distinct clinical subgroup\u0026mdash;elderly RCC patients with lymph node involvement\u0026mdash;thereby offering enhanced relevance to practical clinical decisions, such as the consideration of earlier or more frequent cranial imaging in this high-risk population. This focus augments the clinical utility of our model during postoperative follow-up and prior to the initiation of systemic therapy. Furthermore, numerous multicenter studies have investigated machine learning-based models for predicting brain metastasis. Kim et al. assessed six algorithms and determined that AdaBoost achieved an AUC of approximately 0.716[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In contrast, our nomogram demonstrated superior discriminatory power utilizing fewer, routinely accessible clinical variables, while maintaining robust interpretability and practical applicability at the bedside\u0026mdash;attributes that are especially beneficial in resource-constrained environments and in the treatment of elderly patients. Additionally, cross-tumor generalized risk models leveraging electronic health records (EHR) have been proposed to estimate the risk of brain metastasis at the initial cancer diagnosis stage, achieving an AUC of approximately 0.91 in RCC)cohorts[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Despite their promising statistical performance across various cancer types, these models were not specifically designed for elderly RCC patients with lymph node involvement. Their clinical implementation is further hindered by issues related to variable interpretability, portability, and local applicability. Collectively, these factors underscore the distinctive importance and compelling justification for the development of a specialized predictive tool for brain metastasis in elderly patients with RCC who have established lymph node metastasis.\u003c/p\u003e\u003cp\u003eThis study identified a significant association between liver metastasis and an increased risk of brain metastasis, corroborating findings from previous research. Mechanistically, the elevated expression of CXCR4 in liver metastases, in conjunction with its ligand CXCL12 secreted by astrocytes and endothelial cells within the brain, establishes a chemotactic axis that facilitates the directional migration, adhesion, and survival of tumor cells within the brain microenvironment[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Previous studies have reported heightened CXCR4 positivity in both liver metastases of renal cell carcinoma (RCC) lesions and corresponding brain metastases, reinforcing its role in the organotropic dissemination of RCC to the brain. Furthermore, integrins αvβ3 and αvβ8 have been observed to be upregulated in brain metastatic lesions compared to liver metastatic lesions across various cancers, including RCC. These integrins play a crucial role in mediating adhesion, invasion, and traversal of the blood\u0026ndash;brain barrier (BBB) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Additionally, tumor-derived exosomes, which transport microRNAs and proteins, have been demonstrated to induce the formation of a pre-metastatic niche (PMN) at distant sites by enhancing vascular permeability, modulating the immune response, and remodeling the extracellular matrix[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. These molecular characteristics collectively contribute to a highly invasive metastatic phenotype, which may explain the clinical association observed between liver metastasis and brain metastasis. Consequently, liver metastasis could serve as a clinically observable surrogate marker for increased metastatic aggressiveness, biologically linked to an elevated risk of brain metastasis. In elderly patients with RCC who present with lymph node involvement and concurrent liver metastasis, clinicians might consider employing a lower nomogram threshold as an early indicator for initiating cranial MRI screening.\u003c/p\u003e\u003cp\u003eOur study intriguingly identified a negative correlation between bone metastasis and brain metastasis, suggesting a potential site-specific \"competitive mechanism\" among metastatic niches. The bone marrow microenvironment is characterized by high levels of CXCL12, which effectively retains CXCR4-expressing RCC cells, thereby promoting preferential dissemination or expansion towards the bone [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Conversely, successful colonization of the brain necessitates that tumor cells overcome additional biological barriers, such as adhesion to and transmigration across the BBB. This process involves integrins, basement membrane degradation, and endothelial penetration [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These distinct trajectories may compete at the population level, with certain RCC cell subsets favoring bone homing while others exhibit a predisposition towards brain tropism, resulting in an inverse correlation between the two metastatic sites. It is noteworthy that bone metastasis was positively associated with brain metastasis in univariate analysis; however, this effect was reversed in multivariate models. This shift likely represents a statistical net effect rather than an actual biological protective factor.\u003c/p\u003e\u003cp\u003eIn this study, radiotherapy emerged as the most significant risk factor for brain metastasis. Nonetheless, this association should not be construed as evidence that radiotherapy directly causes brain metastases. Instead, it exemplifies a classic case of confounding by indication. In the practical management of RCC, radiotherapy-including stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT)-is commonly employed for the local control of existing symptomatic, solitary, or oligometastatic lesions, frequently involving the brain or bone. Consequently, patients undergoing radiotherapy are more likely to belong to a subpopulation characterized by a greater metastatic burden or pre-existing central nervous system involvement, rather than radiotherapy being the causal factor for brain metastasis[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. From a methodological perspective, radiotherapy should be considered more as a proxy indicator of disease severity rather than an independent etiological factor. It is important to acknowledge that radiotherapy can influence the tumor microenvironment through its effects on endothelial cells and astrocyte activation. However, current evidence remains insufficient to substantiate a causal pathway by which radiotherapy directly increases the risk of brain metastasis. Nonetheless, the current body of evidence does not sufficiently substantiate a causal relationship in which radiotherapy directly elevates the incidence of brain metastasis.\u003c/p\u003e\u003cp\u003eIn contrast, surgical intervention has been significantly correlated with a decreased risk of brain metastasis. Prior research has indicated that the primary tumor acts as a principal source of circulating tumor cells (CTCs) and tumor-derived exosomes, which facilitate the establishment of distant pre-metastatic niches (PMNs)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The surgical resection of the primary tumor may reduce the continuous release of pro-metastatic signals, including angiogenic factors, immunosuppressive vesicles, and extracellular matrix\u0026ndash;remodeling enzymes, thereby indirectly decreasing the probability of brain colonization and enhancing survival outcomes in patients with metastatic RCC[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This effect may be particularly pertinent in elderly patients, for whom surgical intervention remains a crucial strategy for local disease management and delaying the progression of distant metastases. Our findings advocate for the consideration of surgical intervention when clinically feasible. It is crucial to acknowledge the impact of selection bias: patients who undergo surgical intervention are typically those with resectable tumors, superior overall health status, and a lower initial metastatic burden. Consequently, the observed protective effect may be partially confounded by these favorable baseline characteristics.\u003c/p\u003e\u003cp\u003e Current guidelines, such as those from the European Association of Urology (EAU) and the American Urological Association (AUA), generally advise against routine cranial imaging for asymptomatic RCC patients during standard follow-up. Nonetheless, prompt brain MRI or CT is strongly recommended in the presence of neurological symptoms or high clinical suspicion [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The present study introduces a risk stratification tool specifically tailored for elderly RCC patients with lymph node involvement, facilitating the identification of those most likely to benefit from early cranial imaging\u0026mdash;within a framework that does not endorse universal screening. This nomogram may function as a quantitative trigger for guideline-recommended, individualized imaging escalation in select patients. In the context of synchronous or multi-organ metastases, previous multicenter studies have indicated that asymptomatic brain metastases are relatively common and are correlated with significantly poorer outcomes[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These findings advocate for the implementation of more proactive baseline or early surveillance imaging strategies in high-risk subgroups, such as the cohort examined in our study.\u003c/p\u003e\u003cp\u003eNevertheless, this study has several limitations. Firstly, as a retrospective analysis, it is inherently vulnerable to potential confounding factors, despite efforts to mitigate selection bias through stringent inclusion criteria and robust statistical methods. Secondly, the SEER database lacks crucial molecular and genetic information-such as the mutation status of VHL, PBRM1, and SETD2-and does not provide treatment-specific data on targeted therapies or immunotherapies, both of which are known to significantly affect the metastatic behavior and prognosis of RCC. Furthermore, potential collinearity and competing risks among different patterns of distant metastasis may have influenced the direction or strength of association for certain variables. In summary, the present predictive model underwent only internal validation, necessitating further external validation with prospective and multicenter datasets to ascertain its accuracy and generalizability.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe successfully developed and validated a nomogram utilizing data from the SEER database to predict the risk of brain metastasis in elderly patients with RCC and lymph node involvement. The analysis identified liver metastasis, bone metastasis, radiotherapy, and surgery as independent predictive factors. The model exhibited strong discrimination, calibration, and clinical utility in both the training and validation cohorts. This nomogram holds potential as a tool for clinicians to visually assess an individual patient's risk of brain metastasis, thereby providing evidence-based guidance for customizing follow-up frequency, imaging surveillance, and personalized treatment strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eConflict of interests The authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this study. This data can be found here: https://seer.Cancer.gov/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of this study is obtained from the SEER database. The patients\u0026rsquo; data is public and anonymous, so this study does not require ethical approval and informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuzhan Sun: resources; methodology; formal analysis; investigation; visualization; writing\u0026mdash;original draft. Yuhui He: resources; methodology; formal analysis; investigation; validation; writing\u0026mdash;original draft. Fei Wang: resources; writing\u0026mdash;original draft. Xiaohong Ren: resources; formal analysis; writing\u0026mdash; original draft. Ying Zhao: validation; writing\u0026mdash;original draft; Yisen Deng: conception and design; writing\u0026mdash;review and editing; supervision; funding acquisition. Jianfeng Wang: conception and design; writing\u0026mdash; review and editing; supervision; data curation; project administration; funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated and analyzed in this study are available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Giaquinto AN, Jemal A: Cancer statistics, 2024. CA Cancer J Clin 2024, 74(1):12-49.\u003c/li\u003e\n\u003cli\u003eEscudier B, Porta C, Schmidinger M, Rioux-Leclercq N, Bex A, Khoo V, Gruenvald V, Horwich A: Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. 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Signal transduction and targeted therapy 2021, 6(1):404.\u003c/li\u003e\n\u003cli\u003eHeng DY, Wells JC, Rini BI, Beuselinck B, Lee J-L, Knox JJ, Bjarnason GA, Pal SK, Kollmannsberger CK, Yuasa T: Cytoreductive nephrectomy in patients with synchronous metastases from renal cell carcinoma: results from the International Metastatic Renal Cell Carcinoma Database Consortium. European urology 2014, 66(4):704-710.\u003c/li\u003e\n\u003cli\u003eParmar A, Ghosh S, Sahgal A, Lalani AA, Hansen AR, Reaume MN, Wood L, Basappa NS, Heng DYC, Graham J et al: Evaluating the impact of early identification of asymptomatic brain metastases in metastatic renal cell carcinoma. Cancer Rep (Hoboken) 2023, 6(3):e1763. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Renal cell carcinoma, Brain metastasis, Nomogram, SEER, Risk prediction","lastPublishedDoi":"10.21203/rs.3.rs-7518783/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7518783/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eRenal cell carcinoma (RCC) is the most common malignant kidney tumor in the elderly. Although brain metastasis is relatively rare, it is associated with poor prognosis and diminished quality of life. This study aimed to develop and validate a predictive nomogram to estimate brain metastasis risk in elderly RCC patients with lymph node involvement.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eData were obtained from the SEER database for patients aged\u0026thinsp;\u0026ge;\u0026thinsp;65 years with RCC and lymph node metastasis (2013\u0026ndash;2021). Univariate and multivariate logistic regression analyses identified independent risk factors for brain metastasis. A nomogram was constructed and validated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 2,475 patients were included and randomly assigned to a training cohort (n\u0026thinsp;=\u0026thinsp;1,857) and validation cohort (n\u0026thinsp;=\u0026thinsp;618). Liver metastasis, bone metastasis, radiotherapy, and surgery were independent predictors of brain metastasis. The nomogram demonstrated strong discrimination with AUCs of 0.858 (training) and 0.833 (validation), and excellent calibration in both cohorts. DCA confirmed superior clinical utility compared to conventional TN staging.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eWe developed and validated a reliable nomogram for predicting brain metastasis in elderly RCC patients with lymph node involvement. The model shows high discrimination and clinical applicability, and may help identify high-risk individuals who could benefit from early surveillance and individualized treatment strategies.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a Nomogram for Predicting Brain Metastasis in Elderly Renal cell carcinoma Patients with Lymph Node Involvement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 07:07:56","doi":"10.21203/rs.3.rs-7518783/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5e747dda-c5d8-4a6f-8476-2177020abb51","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T04:25:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 07:07:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7518783","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7518783","identity":"rs-7518783","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}