Development and validation of a prognostic model predicting the prognosis of surgically treated non-clear cell renal cell carcinoma patients with tumor thrombus

Development and validation of a prognostic model predicting the prognosis of surgically treated non-clear cell renal cell carcinoma patients with tumor thrombus | 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 prognostic model predicting the prognosis of surgically treated non-clear cell renal cell carcinoma patients with tumor thrombus He Miao, Ye Zhou, Hui Chen, Yulin Zhou, Chang Lei, Silun Ge, Yufeng Gu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3976210/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 Accurate prediction of clinical outcomes in non-clear cell renal cell carcinoma with tumor thrombus (nccRCC-TT) patients is crucial for counseling, follow-up planning, and selecting appropriate systemic therapy. We aimed to investigate independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in nccRCC-TT patients after surgical resection and construct a nomogram predicting the 1-, 3-, and 5-year survival for these patients. Methods This was a retrospective analysis of data from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2020) and the China REMEMBER database with nccRCC-TT patients. NccRCC-TT patients from the SEER database were randomly divided into training and internal validation sets. Multivariable nomogram models were built and validated to predict OS and CSS. Scores based on the nomograms were used to conduct risk stratification. The performance of these nomograms was then compared with the American Joint Committee on Cancer (AJCC) TNM staging system. Results A total of 809 patients participated, with a training set ( n = 514), an internal validation set ( n = 216), and an external validation set ( n = 79). Median follow-up times for OS were 51, 47, and 28 months in the three sets, respectively. The nomogram integrated seven risk factors affecting survival (advanced age, left side, histology, positive lymph nodes, distant metastasis, renal sinus/perirenal fat invasion, and sarcomatoid/rhabdoid differentiation) to predict OS and CSS at 1-, 3-, and 5-years. Outperforming the AJCC staging system, the nomogram achieved a C-index of 0.774 (95% CI, 0.727–0.821) for OS and 0.787 (95% CI, 0.736–0.838) for CSS in the internal validation set. Both OS and CSS significantly differed between subgroups with low, moderate, and high risk (all P < 0.001). Conclusions Pathological combined histological features are crucial predictors of prognosis in nccRCC-TT patients. We developed a tool to improve patient counseling and guide decision-making on other therapies in addition to surgery for patients with nccRCC-TT. Risk stratification based on our nomograms provides postoperative consultation and patient selection for treatment strategies. Non-clear cell renal cell carcinoma Tumor thrombus Survival Nomogram Prognostic model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Patients with renal cell carcinoma (RCC) have a propensity to invade the venous system. In approximately 10% of RCC cases, tumors can extend into the renal vein or inferior vena cava (IVC) and even the right atrium(Topaktaş et al. 2019 ). RCC with tumor thrombus (TT) is associated with poor prognosis, higher Fuhrman grade, and larger tumor size(Tilki et al. 2014 ). A meta-analysis indicated that the 5-year cancer-specific survival (CSS) after surgery was only 25%-53% for these cases, emphasizing the necessity for meticulous prognosis assessment(Gu et al. 2018 ). Currently, postoperative outcomes following radical nephrectomy with thrombectomy for TT patients have been reported, revealing variations by specific histologic subtypes(Tilki et al. 2014 ; Ciancio et al. 2010 ; Wang et al. 2022 ). Non-clear cell renal cell carcinoma (nccRCC) constitutes 20–30% of RCC cases(Bukavina et al. 2022 ). Patients with nccRCC and tumor thrombus (nccRCC-TT) experience worse outcomes compared to clear cell RCC (ccRCC)(Tilki et al. 2014 ; Ciancio et al. 2010 ). However, Rabinowitz(Rabinowitz et al. 2022 ) analyzed 103 patients (82 ccRCC and 21 nccRCC) and found no significant survival difference between the two groups. Hence, a study involving larger sample size is imperative to futher assess the prognostic impact of histological subtypes in TT patients. Despite several studies have reported that the tumor size, grade, lymph node status, and distant metastases are risk factors for the poor prognosis of RCC with TT(Gu et al. 2018 ; Zapała et al. 2021 ), these studies most focused on ccRCC. While the postoperative survival prognostic factors for nccRCC-TT, constrained by small sample sizes, remain unclear. The TNM staging system is often used to predict survival outcomes. Nevertheless, it only accounts for the impact of some tumor characteristics such as size, invasion status, and metastasis on the survival of patients, and does not consider individual differences such as sex, age and histology in prognosis. Currently, no prediction model exclusively predicts the survival of nccRCC-TT patients. Therefore, this study utilized a large dataset of nccRCC-TT cases from the Surveillance, Epidemiology and End Results (SEER) database. We aimed to investigate independent prognostic factors for overall survival (OS) and CSS in nccRCC-TT patients after surgical resection and construct a nomogram predicting the 1-, 3-, and 5-year survival for these patients. Materials and methods 2.1 Patient selection Patients diagnosed with nccRCC-TT between 2010 and 2020 were initially identified from the SEER database using SEER*Stat version 8.35. The histological classification was based on the International Classifcation of Diseases Codes for Oncology (ICD-O) proposed in 2000. Inclusion criteria comprised: (i) primary site code C64.9, ICDO-3 histology codes 8260/3 (papillary renal cell carcinoma, pRCC), 8317/3 (chromophobe renal cell carcinoma, chRCC), 8010/3 (carcinoma unclassified), 8319/3 and 8510/3 (Bellini RCC)(Moch et al. 2016 ); (ii) presence of TT (based on the major vein involvement recode); (iii) only unilateral and one primary tumor; (iv) histologically confirmed diagnosis; (v) active follow-up to ensure a reliable patient status. The exclusion criteria were as follows: (i) missing important data; (ii) unknown status of TT involvement recode; (iii) those who have received neoadjuvant therapy; (iv) patient died within 1 month or had a follow-up duration less than 1 month. An external validation set comprising 79 patients complying with the above criteria was collected from our REMEMBER (Research of Multi-institution in East-China on Malignant and Benign Epithelial Renal Tumors) database in China (2015–2022). The histological subtypes of nccRCC were: pRCC ( n = 401), chRCC ( n = 249), Bellini RCC ( n = 52) and Others ( n = 28) in the SEER set; and pRCC ( n = 54), chRCC ( n = 2), Bellini RCC ( n = 12) and Others ( n = 11) in our external validation set. 2.2 Study sets and variables Patients in the SEER set were randomly divided into training and validation sets in a 7:3 ratio using the R package “caret”. The training set was utilized for variable screening and model construction. The validation set was employed to validate the results. We selected 12 possible risk factors, which included age, sex, race, laterality, histology, TNM stage based on the 6th AJCC staging system (2010–2015) and 7th AJCC staging system (2016–2020), postoperative systemic therapy, renal sinus/perirenal fat invasion, sarcomatoid/rhabdoid differentiation, major vein involvement, tumor size and pathological grade. Additionally, the TT was divided into two groups (renal vein thrombus or inferior vena cava thrombus) based on the major vein involvement recode to assess the level of TT and balance the effect of different T stages in the two versions. According to the Fuhrman grade system, we combined GI and GII into Low (I/II), GIII and GIV into High (III/IV) for further analyses. The chRCC subtype and some cases with unknown grading were denoted as not available (NA). Overall survival (OS) was defined as the time from surgery to death of any cause, and the cancer specific survival (CSS) was defined as the time from surgery to the death specifically caused by renal tumor. 2.3 Statistical analysis All statistical analuses were performed using R version 4.3.1 software ( http://www.r-project.org ). Continuous variables were presented as median and interquartile range (IQR), and categorical variables as frequency (percentage). Optimal age and tumor size cut-off values were determined using X-tile version 3.6.1 software (Yale University School of Medicine, New Haven, Conn)(Camp et al. 2004 ). Univariable and multivariable Cox proportional hazards regression analyses were performed for OS and CSS. Variables with a univariate P -value < 0.05 were included in multivariate analyses to identify independent prognostic factors. Hazard ratios (HRs) were presented with 95% confidence intervals (CIs). The final prognostic factors were incorporated into two nomograms predicting the 1-, 3- and 5-year OS and CSS rates, respectively. Discrimination accuracy was measured by Harrell’s concordance index (C-index) and receiver operating characteristic (ROC) curves, with corresponding areas under the curves (AUC) computed. Time-dependent ROC analysis was also conducted. Calibration curves, generated using the bootstrap method (resampling = 1000) in both training and validation sets, assessed the consistency of the nomogram. Decision curve analysis (DCA)(Fitzgerald et al. 2015 ), a method evaluating prediction models by calculating clinical net benefit, was conducted across all sets. The performance of the nomogram was compared with the AJCC TNM staging system. Individual risk scores were calculated for each patient, based on which patients were categorized into low-risk, moderate-risk, or high-risk groups using X-tile software. Kaplan-Meier curves were plotted for these groups to further assess calibration, with differences evaluated using the log-rank test. Finally, two online tools regarding OS and CSS of nccRCC-TT patients were built using the “DynNom” and “Shiny” R packages, accessible via the Shiny website ( https://www.shinyapps.io/ ). All P -values were two-tailed, and P < 0.05 was considered statistically significant. Results 3.1 Clinicopathologic and follow‑up data In total, 730 patients from the SEER database and 79 patients from the China database were eventually enrolled in this study. Patients from SEER were randomly divided into a training set ( n = 514) and an internal validation set ( n = 216). The cut-off value of age and tumor size in the training set was 68 years and 6.8cm, respectively. Predominantly, patients in the SEER set were ≤ 68 years old and male, consistent with our external validation set. Histologically, pRCC accounted for more than half in both the SEER set (54.9%) and the external validation set (68.4%). Notably, chRCC with TT exhibited the highest survival rate, while Bellini RCC demonstrated the poorest (Fig. 1 A-B). The T3b stage was noted for the majority of patients (46% and 40.5%) in both databases. Inferior vena cava TT was observed in 22.1% of the SEER set and 60.8% in the external validation set, possibly due to the latter's more advanced stage (N1: 38% vs. 28.2%; M1: 27.8% vs. 20.3%). Patients who received systemic therapy after surgery were 18.4% in the SEER set and 36.7% in the external validation set. NccRCC-TT tumors generally exhibited size > 6.8 cm and a high Fuhrman grade in both sets. Detailed characteristics are provided in Table 1 . Table 1 Demographic and clinical characteristics of patients with nccRCC-TT. Variables SEER set (N = 730) Training (N = 514) Internal validation (N = 216) P -value External validation set (N = 79) P -value Age (years) ≤ 68 485 (66.4%) 352 (68.5%) 133 (61.6%) 0.086 70 (88.6%) 68 245 (33.6%) 162 (31.5%) 83 (38.4%) 9 (11.4%) Sex Female 243 (33.3%) 168 (32.7%) 75 (34.7%) 0.655 29 (36.7%) 0.627 Male 487 (66.7%) 346 (67.3%) 141 (65.3%) 50 (63.3%) Race Caucasian 530 (72.6%) 375 (73%) 155 (71.8%) 0.893 0 (0%) < .001 African American 151 (20.7%) 104 (20.2%) 47 (21.8%) 0 (0%) Asian and Others 49 (6.7%) 35 (6.8%) 14 (6.5%) 79 (100%) Laterality Left 390 (53.4%) 269 (52.3%) 121 (56%) 0.407 42 (53.2%) 1.000 Right 340 (46.6%) 245 (47.7%) 95 (44%) 37 (46.8%) Histology pRCC 401 (54.9%) 292 (56.8%) 109 (50.5%) 0.479 54 (68.4%) < .001 chRCC 249 (34.1%) 168 (32.7%) 81 (37.5%) 2 (2.5%) Bellini RCC 52 (7.1%) 35 (6.8%) 17 (7.9%) 12 (15.2%) Others 28 (3.8%) 19 (3.7%) 9 (4.2%) 11 (13.9%) T stage 3a 312 (42.7%) 215 (41.8%) 97 (44.9%) 0.045 27 (34.2%) .006 3b 336 (46%) 245 (47.7%) 91 (42.1%) 32 (40.5%) 3c 35 (4.8%) 28 (5.4%) 7 (3.2%) 10 (12.7%) 4 47 (6.4%) 26 (5.1%) 21 (9.7%) 10 (12.7%) Thrombus level Renal vein 569 (77.9%) 401 (78%) 168 (77.8%) 1.000 31 (39.2%) < .001 Inferior vena cava 161 (22.1%) 113 (22%) 48 (22.2%) 48 (60.8%) N stage N0/Nx 524 (71.8%) 368 (71.6%) 156 (72.2%) 0.935 49 (62%) 0.093 N1 206 (28.2%) 146 (28.4%) 60 (27.8%) 30 (38%) M stage M0 582 (79.7%) 409 (79.6%) 173 (80.1%) 0.953 57 (72.2%) 0.154 M1 148 (20.3%) 105 (20.4%) 43 (19.9%) 22 (27.8%) Tumour size (cm) ≤ 6.8 226 (31%) 156 (30.4%) 70 (32.4%) 0.645 27 (34.2%) 0.647 > 6.8 504 (69%) 358 (69.6%) 146 (67.6%) 52 (65.8%) Systemic therapy No 596 (81.6%) 416 (80.9%) 180 (83.3%) 0.509 50 (63.3%) < .001 Yes 134 (18.4%) 98 (19.1%) 36 (16.7%) 29 (36.7%) Renal sinus/Perirenal fat invasion No 203 (27.8%) 136 (26.5%) 67 (31%) 0.244 30 (38%) 0.078 Yes 527 (72.2%) 378 (73.5%) 149 (69%) 49 (62%) Sarcomatoid/Rhabdoid feature No 640 (87.7%) 447 (87%) 193 (89.4%) 0.440 55 (69.6%) < .001 Yes 90 (12.3%) 67 (13%) 23 (10.6%) 24 (30.4%) Fuhrman grade Low (1–2) 51 (7%) 40 (7.8%) 11 (5.1%) 0.406 2 (2.5%) < .001 High (3–4) 351 (48.1%) 247 (48.1%) 104 (48.1%) 75 (94.9%) NA 328 (44.9%) 227 (44.2%) 101 (46.8%) 2 (2.5%) OS month (Median (95%CI)) 46.0 (37.8–54.2) 45.0 (36.1–53.9) 49.0 (28.8–69.2) 0.764 30.0 (18.5–41.5) 0.236 CSS month (Median (95%CI)) 61.0 (37.1–84.9) 59.0 (29.7–88.3) 73.0 (51.3–94.7) 0.739 - Follow-up month OS 51.0 (45.8–56.2) 51.0 (43.9–58.1) 47.0 (37.5–56.5) 0.201 28.0 (21.6–34.4) < .001 Follow-up month CSS 44.0 (39.1–48.9) 44.0 (38.1–49.9) 41.0 (34.2–47.8) 0.236 - NOTE. Data are presented as No. (%) unless indicated otherwise. IQR, interquartile range; OS, Overall Survival; CSS, Cancer Specific Survival; NA, not available. pRCC, papillary renal cell carcinoma; chRCC, chromophobe renal cell carcinoma; Bellini RCC, Bellini renal cell carcinoma/collecting duct carcinoma; Others, Unclassified carcinoma. P values were calculated by Chi-square test for categorical variables and Cochran-Mantel-Haenszel(CMH) Chi-square test for ordinal variables. The overall cohort has been randomly divided into a training (70%) and a validation (30%) cohort. After a median follow-up time of 51 months (95%CI, 45.8–48.9) in the SEER set and 28 months (95%CI, 21.6–34.4) in the external validation set, the median OS time was 46 months (95%CI, 37.8–54.2) and 30 months (95%CI, 18.5–41.5), respectively. The median CSS in the SEER set, after a median follow-up of 44 months (95% CI, 39.1–48.9), was 61 months (95% CI, 37.1–84.9). At the end of follow-up, the 5-year OS rate was 43.1% in the SEER set and 27.4% in the external validation set. The 5-year CSS rate was 50.4% in the SEER set. 3.2 Construction of the nomogram In the training set, independent risk factors for OS were obtained: age > 68 (HR: 1.88, 95%CI, 1.45–2.45, P < 0.001), laterality (HR: 0.74, 95%CI, 0.57–0.96, P = 0.026), histology (Bellini RCC vs. pRCC; HR: 2.10, 95%CI, 1.38–3.19, P = 0.001), N1 stage (HR: 1.76, 95%CI, 1.28–2.41, P < 0.001), M1 stage (HR: 2.44, 95%CI, 1.78–3.34, P < 0.001), renal sinus/perirenal fat invasion (HR: 1.44, 95%CI, 1.04-2.00, P = 0.027), and sarcomatoid/rhabdoid feature (HR: 1.72, 95%CI, 1.23–2.40, P = 0.001) (Table 2 ). Similarly, multivariate analysis results suggested that age > 68 (HR: 1.61, 95%CI, 1.19–2.18, P = 0.002), laterality (HR: 0.69, 95%CI, 0.52–0.93, P = 0.014), histology (Bellini RCC vs. pRCC; HR: 2.39, 95%CI, 1.53–3.72, P < 0.001), N1 stage (HR: 1.92, 95%CI, 1.36–2.72, P < 0.001), M1 stage (HR: 2.73, 95%CI, 1.94–3.86, P < 0.001), renal sinus/perirenal fat invasion (HR: 1.50, 95%CI, 1.02–2.19, P < 0.038), and sarcomatoid/rhabdoid feature (HR: 1.97, 95%CI, 1.39–2.79, P < 0.001) were independent risk factors for CSS (Table 2 ). Nomograms predicting OS and CSS risk for patients at 1-, 3-, or 5-years were then respectively constructed based on identified variables (Fig. 2 A-B). Table 2 Univariate and multivariate Cox analyses on variables for the prediction of OS and CSS of nccRCC-TT patients. Variables Overall survival Cancer-specific survival Univariable Multivariable Univariable Multivariable HR (95% CI) P- value HR (95%CI) P- value HR (95% CI) P- value HR (95%CI) P- value Age (years) ≤ 68 Ref. Ref. Ref. Ref. > 68 1.76(1.36–2.28) < .001 1.88(1.45–2.45) < .001 1.46(1.09–1.95) 0.001 1.61(1.19–2.18) 0.002 Sex Female Ref. Ref. Male 1.19(0.90–1.56) 0.219 1.29(0.95–1.74) 0.106 Race Caucasian Ref. Ref. Ref. African American 1.23(0.91–1.66) 0.183 1.39(1.01–1.93) 0.045 1.17(0.82–1.65) 0.381 Asian and Others 1.11(0.67–1.82) 0.694 1.32(0.79–2.22) 0.288 1.17(0.69-2.00) 0.560 Laterality Left Ref. Ref. Ref. Ref. Right 0.75(0.58–0.97) 0.030 0.74(0.57–0.96) 0.026 0.70(0.53–0.93) 0.015 0.69(0.52–0.93) 0.014 Histology pRCC Ref. Ref. Ref. Ref. chRCC 0.41(0.29–0.57) < .001 0.71(0.43–1.18) 0.185 0.35(0.24–0.51) < .001 0.86(0.46–1.61) 0.644 Bellini RCC 2.17(1.44–3.26) < .001 2.10(1.38–3.19) 0.001 2.39(1.56–3.67) < .001 2.39(1.53–3.72) < .001 Others 0.835 0.97(0.44–2.12) 0.932 1.10(0.51–2.35) 0.813 1.27(0.57–2.83) 0.563 Thrombus level Renal vein Ref. Ref. Ref. Ref. Inferior vena cava 1.62(1.22–2.16) < .001 1.03(0.76–1.39) 0.864 1.73(1.27–2.36) < .001 1.02(0.73–1.42) 0.925 N stage N0/Nx Ref. Ref. Ref. Ref. N1 2.83(2.18–3.67) < .001 1.76(1.28–2.41) < .001 3.39(2.56–4.50) < .001 1.92(1.36–2.72) < .001 M stage M0 Ref. Ref. Ref. Ref. M1 3.12(2.37–4.11) < .001 2.44(1.78–3.34) < .001 3.68(2.74–4.93) < .001 2.73(1.94–3.86) 6.8 1.51(1.13–2.03) 0.006 1.32(0.97–1.78) 0.076 1.53(1.10–2.12) 0.011 1.23(0.87–1.73) 0.243 Systemic therapy No Ref. Ref. Ref. Ref. Yes 1.93(1.45–2.57) < .001 0.77(0.54–1.09) 0.141 2.23(1.64–3.02) < .001 0.78(0.53–1.14) 0.196 Renal sinus/Perirenal fat invasion No Ref. Ref. Ref. Ref. Yes 1.91(1.40–2.61) < .001 1.44(1.04-2.00) 0.027 2.17(1.51–3.11) < .001 1.50(1.02–2.19) 0.038 Sarcomatoid/Rhabdoid feature No Ref. Ref. Ref. Ref. Yes 2.44(1.78–3.35) < .001 1.72(1.23–2.40) 0.001 3.06(2.21–4.26) < .001 1.97(1.39–2.79) < .001 Fuhrman grade Low (1–2) Ref. Ref. Ref. Ref. High (3–4) 1.99(1.24–3.21) 0.005 1.41(0.87–2.30) 0.167 1.95(1.18–3.24) 0.010 1.31(0.78–2.22) 0.306 NA 0.79(0.48–1.32) 0.370 0.96(0.52–1.78) 0.908 0.62(0.35–1.07) 0.085 0.66(0.33–1.33) 0.244 Abbreviations: HR, hazard ratio; CI, confidential interval; NA, not available; Ref, Reference. pRCC, papillary renal cell carcinoma; chRCC, chromophobe renal cell carcinoma; Bellini RCC, Bellini renal cell carcinoma/collecting duct carcinoma; Others, Unclassified carcinoma. 3.3 Validation of the nomogram The calibration plots in the training, internal validation, and external validation sets for 1-, 3-, and 5-year OS and CSS were depicted in Fig. 3 A-E. The nomogram exhibited a C-index for OS prediction of 0.734 (95%CI, 0.703–0.765) in the training set, 0.774 (95%CI, 0.727–0.821) in the internal validation set, and 0.705 (95%CI, 0.600-0.811) in the external validation set. The AUCs for 1-, 3- and 5-year OS prediction were 0.766, 0.787, and 0.818 in the training set, 0.814, 0.866, and 0.815 in the internal validation set, 0.774, 0.668 and 0.601 in the external validation set, respectively (Fig. 4 A-C). While the TNM staging system for OS prediction exhibited a lower C-index: 0.675 (95%CI, 0.640–0.710) in the training set, 0.713 (95%CI, 0.660–0.766) in the internal validation set, and 0.649 (95%CI, 0.541–0.757) in the external validation set. Furthermore, the time-dependent AUC curves for OS, including nomogram and stage, demonstrated the nomogram's superior predictive value compared to the commonly used risk factor TNM stage ( Supplementary Fig. 1. A-C ). Simultaneously, the C-index of the nomogram for CSS prediction surpassed that of the TNM staging system, with 0.759 (95%CI, 0.726–0.792) vs. 0.709 (95%CI, 0.672–0.746) in the training set and 0.787 (95%CI, 0.736–0.838) vs. 0.730 (95%CI, 0.673–0.787) in the internal validation set. The AUCs for 1-, 3- and 5-year CSS prediction were 0.793, 0.807, and 0.838 in the training set and 0.825, 0.870, and 0.824 in the internal validation set (Fig. 4 D-E). Additionally, the time-dependent AUC curves for the CSS nomogram were displayed in Supplementary Fig. 1. D-E , futher indicated the nomogram's stability and discriminability compared to the TNM staging system. The DCA plots for 1-, 3-, and 5-year rates of OS and CSS in the training and validation sets illustrated that our nomogram for nccRCC-TT achieved positive net clinical benefits across a broad range of threshold probabilities, emphasizing its high clinical utility ( Supplementary Fig. 2 ). 3.4 Risk stratification Following the acquisition of risk scores for each of the 730 SEER dataset patients through the nomogram, patients with nccRCC-TT were categorized into low-, moderate-, and high-risk groups for OS prediction, with cut-off points at 96 and 193. The Kaplan-Meier OS curves demonstrated significant discrimination among these groups ( P < 0.001) (Fig. 5 A). The median OS for high-risk, moderate-risk, and low-risk groups were 8 months (95%CI, 0.44–0.65), 24 months (95%CI, 0.46–0.59), and 117 months (95%CI, 0.39–0.62), respectively. The 3-year OS rates for these groups were 6.0% (95%CI, 0.02–0.15), 39.5% (95%CI, 0.33–0.47), and 79.6% (95%CI, 0.75–0.84), respectively. Similarly, the cut-off points of the nomogram for CSS prediction were 99 and 189. As shown in Fig. 5 B, the risk of specific death from nccRCC-TT significantly increased with rising risk levels ( P < 0.001). Median CSS for high-risk, moderate-risk, and low-risk groups were 9 months (95%CI, 0.40–0.63), 29 months (95%CI, 0.44–0.58), and not reached (95%CI, NA-NA), respectively. The 3-year CSS rates for these groups were 5.1% (95%CI, 0.02–0.15), 44.5% (95%CI, 0.38–0.52), and 84.1% (95%CI, 0.80–0.89), respectively. The median time, 1-, 3-, and 5-year rates for the three risk groups of OS and CSS were summarized in Table 3 . Table 3 Risk stratification for the nomogram of OS and CSS. Risk group Median time 1-year rate 3-year rate 5-year rate High risk 8 months (95%CI 0.44–0.65) 38.3% (95%CI 0.30–0.50) 6.0% (95%CI 0.02–0.15) not reached (95%CI NA-NA) OS Moderate risk 24 months (95%CI 0.46–0.59) 71.2% (95%CI 0.66–0.77) 39.5% (95%CI 0.33–0.47) 25.0% (95%CI 0.19–0.33) Low risk 117 months (95%CI 0.39–0.62) 92.2% (95%CI 0.89–0.95) 79.6% (95%CI 0.75–0.84) 67.9% (95%CI 0.62–0.74) High risk 9 months (95%CI 0.40–0.63) 37.0% (95%CI 0.28–0.49) 5.1% (95%CI 0.02–0.15) not reached (95%CI NA-NA) CSS Moderate risk 29 months (95%CI 0.44–0.58) 74.0% (95%CI 0.69–0.80) 44.5% (95%CI 0.38–0.52) 30.5% (95%CI 0.24–0.39) Low risk not reached (95%CI NA-NA) 94.5% (95%CI 0.92–0.97) 84.1% (95%CI 0.80–0.89) 76.4% (95%CI 0.71–0.82) OS, overall survival; CSS, cancer-specific survival; NA, not available. Additionally, two dedicated online tools for OS ( https://njmumh1997.shinyapps.io/os-prediction/ ) and CSS ( https://njmumh1997.shinyapps.io/css-prediction/ ) were built to conveniently calculate nomogram scores, determine risk groups, and predict survival for each patient. Discussion Renal cell carcinoma (RCC) often infiltrates the renal venous system and causes tumor thrombus, impacting 4–13% of newly diagnosed RCC patients and associating with an unfavorable prognosis(Wang et al. 2022 ). The incidence of nccRCC-TT is approximately 10% and pRCC accounts for the largest proportion of nccRCC(Bukavina et al. 2022 ; Rigaud et al. 2006 ). Despite the radical nephrectomy with thrombectomy was performed, the long-term survival of RCC with TT (RCC-TT) remains unsatisfactory compared to localized RCC. Several studies have evaluated prognostic factors in RCC-TT patients. However, due to limited sample sizes, most previous studies categorized RCC-TT patients into ccRCC and nccRCC groups without further subclassifying the histology of nccRCC, which has limited the significance of pathological stratification(Wang et al. 2022 ). Additionally, pathological types other than ccRCC and pRCC with TT were often excluded by previous studies because of their rarity. Hence, the postoperative survival prognostic factors of nccRCC-TT group remain unclear. To address this gap, we utilized a large database to develop prognostic nomograms for individual predictions of OS and CSS in nccRCC-TT patients, subsequently validating the models externally. Ultimately, seven independent risk factors for OS and CSS were identified: advanced age, left side, histology, positive lymph nodes, distant metastasis, renal sinus/perirenal fat invasion, and the presence of sarcomatoid/rhabdoid features. Owing to the relatively short follow-up time and histological subtype proportion difference, the C-index of the external validation set (0.705) for OS was inferior to the internal validation set but still superior to the TNM staging system (0.649). In the exploration of prognostic factors for RCC-TT, most studies analyzed the non-metastatic and metastatic patients together. Adverse predictors for both CSS and OS, such as large tumor size, high Fuhrman grade, tumor necrosis, positive lymph nodes, and metastasis at surgery, have been widely indicated. Besides, inferior CSS has been associated with IVC tumor thrombus, sarcomatoid differentiation, perinephretic fat invasion, and adrenal gland invasion(Gu et al. 2018 ; Zapała et al. 2021 ; Gayed et al. 2016 ; Niedworok et al. 2015 ; Chen et al. 2021 ). In our focused study on ccRCC-TT patients, lymph node positivity, preoperative distant metastasis, sarcomatoid/rhabdoid differentiation, and renal sinus/perinephric fat invasion independently affect both OS and CSS. This underscores the profound impact of these factors on the survival outcomes of patients with TT involvement, regardless of ccRCC or nccRCC. Additionally, we observed that advanced age at diagnosis and left-sided TT were also associated with inferior survival outcomes. Similar findings were reported by David et al.(Thiel et al. 2012 ), who observed a higher risk of RCC-related death for left-sided patients compared to their right-sided counterparts (HR: 2.02) among those with a thrombus reaching the IVC. Due to physiological differences in structure, the left renal vein surpasses the right in anatomical length by more than threefold. Under equivalent TT lengths, the tumor is more likely to extend into the IVC on the right side, often exhibiting a more pronounced advancement compared to the contralateral side. The prognostic significance of the level of TT has been controversial, with some studies reporting it as an independent prognostic factor for survival(Haferkamp et al. 2007 ; Martínez-Salamanca et al. 2011 ), while others did not find it significant(Ciancio et al. 2010 ; Zapała et al. 2021 ; Chen et al. 2021 ). In our analysis, we found that although IVC thrombus was a significant predictor of survival in univariate analysis, it did not retain its independence on multivariate analysis. While the existence of TT generally contributes to a worse prognosis in RCC patients, notable variations in survival persist among different histological subtypes. Most studies agree that ccRCC generally has a more favorable prognosis. A study by Dharam et al. (Kaushik et al. 2013 ) revealed that nccRCC-TT patients exhibit a larger tumor size, higher nuclear grade, more frequent sarcomatoid differentiation, and increased lymph node invasion, leading to a worse prognosis. Similarly, studies by the International Renal Cell Carcinoma venous Thrombus Consortium(Martínez-Salamanca et al. 2014 ), Gaetano et al.(Ciancio et al. 2010 ), and Derya et al.(Tilki et al. 2014 ) also suggested significantly worse CSS for nccRCC histological types. In contrast, studies by Nocera et al.(Nocera et al. 2022 ) and Matthew et al.(Rabinowitz et al. 2022 ) indicated that both clear cell and non-clear cell demonstrated similarly poor OS in patients with TT, with no clinically meaningful differences between the groups. However, Nocera’s subgroup excluded patients with stage T3a and M1, and Matthew’s study had a small sample size, potentially contributing to the heterogeneous results. Our study further underscores the pivotal role of non-clear cell histological subtypes on OS and CSS outcomes in nccRCC-TT patients, with Bellini RCC associated with the least favorable prognosis, followed by pRCC, and chRCC demonstrating the best prognosis. The advent of novel immune checkpoint inhibitors has led to unprecedented benefits for patients undergoing systemic immunotherapy following radical nephrectomy, particularly reported in ccRCC(Motzer et al. 2020 ). However, the benefits of immune checkpoint inhibition in nccRCC have yet to be defined further. A retrospective real-life cohort of advanced nccRCC patients shows that immunotherapy-based combinations could improve OS compared to TKI monotherapy(Bimbatti et al. 2023 ). While results from CheckMate920 indicated that Nivolumab plus ipilimumab for previously untreated advanced nccRCC showed no new safety signals and encouraging antitumor activity(Tykodi et al. 2022 ). In our multivariate analysis, 18.4% of patients received postoperative systemic therapy (HR: 0.77). Prospective trials for nccRCC patients utilizing novel therapies are ongoing and their results eagerly awaited. Hence, determining the suitability of nccRCC-TT patients for systemic therapy is crucial(Zoumpourlis et al. 2021 ). Our study has several limitations that should be considered while interpreting our results. Firstly, the study is limited by its retrospective nature. Secondly, details regarding the use and duration of systemic therapy within the SEER database are unclear, and essential perioperative variables were not evaluated. This lack of data on therapy may affect the reported survival outcomes. Thirdly, the size of the external validation set was relatively scant, and the medium follow-up time was only 28 months, so we were unable to achieve good performance of 3- and 5-year OS prediction in the external validation set. Therefore, more multicenter clinical validation is imperative to evaluate the external utility of our nomogram. Conclusion In conclusion, our study pioneers survival prediction models in nccRCC-TT patients after surgery, highlighting the efficacy of our nomograms in predicting OS and CSS time. The nomogram’s C-index demonstrated its superiority to the AJCC-TNM categorisation regarding predictive capacity. Calculating scores and stratifying risks based on the nomogram model can facilitate personalized follow-up and subsequent treatment for these patients. Declarations Funding This work was supported by the National Natural Science Foundation of China Funding (82373154 to Le Qu and 82072836 to Wenquan Zhou). Competing Interests The authors have no relevant financial or non-financial interests to disclose. Author contributions All authors contributed to the study conception and design. Material preparation and data collection were performed by He Miao, Ye Zhou, Hui Chen, Silun Ge, Yufeng Gu, Xin Pan, Xing Zeng, Cheng Zhao and Shaogang Wang, Jingping Ge and Linhui Wang. The data analysis was performed by He Miao, Chang Lei and Yulin Zhou. The study was sponsored by Le Qu and Wenquan Zhou. The first draft of the manuscript was written by He Miao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data availability The data for this study were obtained from publicly available databases (https://seer.cancer.gov/). The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Ethics approval This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the institutional review board of initiating center Jinling Hospital (ID Number: 2021NZKY-004-01). Consent to participate Written informed consent was obtained from all patients. Consent to publish Each author agreed to publish the paper. References Topaktaş R, Ürkmez A, Tokuç E, et al. Surgical management of renal cell carcinoma with associated tumor thrombus extending into the inferior vena cava: A 10-year single-center experience [J]. Turk J Urol, 2019, 45(5), 345-50. https://doi.org/10.5152/tud.2019.95826 Tilki D, Nguyen H G, Dall'era M A, et al. Impact of histologic subtype on cancer-specific survival in patients with renal cell carcinoma and tumor thrombus [J]. Eur Urol, 2014, 66(3), 577-83. https://doi.org/10.1016/j.eururo.2013.06.048 Gu L, Li H, Wang Z, et al. A systematic review and meta-analysis of clinicopathologic factors linked to oncologic outcomes for renal cell carcinoma with tumor thrombus treated by radical nephrectomy with thrombectomy [J]. Cancer Treat Rev, 2018, 69(112-20. https://doi.org/10.1016/j.ctrv.2018.06.014 Ciancio G, Manoharan M, Katkoori D, et al. 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Urology, 2013, 82(1), 136-41. https://doi.org/10.1016/j.urology.2013.02.034 Martínez-Salamanca J I, Linares E, González J, et al. Lessons learned from the International Renal Cell Carcinoma-Venous Thrombus Consortium (IRCC-VTC) [J]. Curr Urol Rep, 2014, 15(5), 404. https://doi.org/10.1007/s11934-014-0404-7 Nocera L, Collà Ruvolo C, Stolzenbach L F, et al. Tumor Stage and Substage Predict Cancer-specific Mortality After Nephrectomy for Nonmetastatic Renal Cancer: Histological Subtype-specific Validation [J]. Eur Urol Focus, 2022, 8(1), 182-90. https://doi.org/10.1016/j.euf.2021.02.009 Motzer R J, Banchereau R, Hamidi H, et al. Molecular Subsets in Renal Cancer Determine Outcome to Checkpoint and Angiogenesis Blockade [J]. Cancer Cell, 2020, 38(6), 803-17.e4. https://doi.org/10.1016/j.ccell.2020.10.011 Bimbatti D, Pierantoni F, Lai E, et al. Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience [J]. Cancers (Basel), 2023, 15(17), https://doi.org/10.3390/cancers15174353 Tykodi S S, Gordan L N, Alter R S, et al. Safety and efficacy of nivolumab plus ipilimumab in patients with advanced non-clear cell renal cell carcinoma: results from the phase 3b/4 CheckMate 920 trial [J]. J Immunother Cancer, 2022, 10(2), https://doi.org/10.1136/jitc-2021-003844 Zoumpourlis P, Genovese G, Tannir N M, et al. Systemic Therapies for the Management of Non-Clear Cell Renal Cell Carcinoma: What Works, What Doesn't, and What the Future Holds [J]. Clin Genitourin Cancer, 2021, 19(2), 103-16. https://doi.org/10.1016/j.clgc.2020.11.005 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx 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. 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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-3976210","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274459324,"identity":"2a8a5aa1-15ad-4a5c-8a7e-6e4326d4e7d1","order_by":0,"name":"He Miao","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Miao","suffix":""},{"id":274459325,"identity":"bb1616c6-9ce2-43a5-9043-7dea5086d605","order_by":1,"name":"Ye Zhou","email":"","orcid":"","institution":"Changhai Hospital, Naval Medical 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16:06:42","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3976210/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3976210/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51717003,"identity":"a548f3e2-8861-493d-a7fe-27261b79fa00","added_by":"auto","created_at":"2024-02-27 21:11:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":513963,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curve for OS and CSS prediction according to histology subtypes in SEER set \u003cstrong\u003e(A)\u003c/strong\u003e. and External validation set \u003cstrong\u003e(B)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3976210/v1/f61fa516f76aa57e859c85e8.png"},{"id":51717007,"identity":"9f245452-6d94-4bf7-aa04-dce6ed4935ed","added_by":"auto","created_at":"2024-02-27 21:11:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":441570,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for prediction of 1-, 3- and 5-year OS rates \u003cstrong\u003e(A)\u003c/strong\u003e and CSS rates\u003cstrong\u003e (B)\u003c/strong\u003e of patients with nccRCC-TT after surgery.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3976210/v1/aeda99b60d354a9933ea0e58.png"},{"id":51717000,"identity":"bc8121d4-eb1d-488e-9e08-c2852c6835e6","added_by":"auto","created_at":"2024-02-27 21:11:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":583743,"visible":true,"origin":"","legend":"\u003cp\u003eCalibration plots for predicting OS in the training \u003cstrong\u003e(A)\u003c/strong\u003e, internal validation \u003cstrong\u003e(B)\u003c/strong\u003e, and external validation sets \u003cstrong\u003e(C)\u003c/strong\u003e. Calibration plots for predicting CSS in the training\u003cstrong\u003e (D)\u003c/strong\u003e, internal validation \u003cstrong\u003e(E)\u003c/strong\u003e. The dashed line represents a perfect match between actual survival outcome (Y-axis) and nomogram prediction (X-axis).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3976210/v1/be51a709a4c4110a4041bb46.png"},{"id":51717006,"identity":"5ea36ed1-1b03-4408-b583-e8bcd45a6b83","added_by":"auto","created_at":"2024-02-27 21:11:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":157941,"visible":true,"origin":"","legend":"\u003cp\u003eROC curves for OS rates in the training set \u003cstrong\u003e(A)\u003c/strong\u003e, the internal validation set \u003cstrong\u003e(B)\u003c/strong\u003e, and the external validation set \u003cstrong\u003e(C)\u003c/strong\u003e. ROC curves for CSS rates in the training set \u003cstrong\u003e(D)\u003c/strong\u003e, the internal validation set \u003cstrong\u003e(E)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3976210/v1/745b7a8a0e64c08e2a50074e.png"},{"id":51716970,"identity":"b9375ada-74a7-4d58-ba8d-4887b52c48c3","added_by":"auto","created_at":"2024-02-27 21:11:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":93032,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves of OS for different risk groups in the SEER set\u003cstrong\u003e(A)\u003c/strong\u003e; Kaplan-Meier curves of CSS for different risk groups in the SEER set \u003cstrong\u003e(B)\u003c/strong\u003e.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3976210/v1/1db332429c667c060acc0f92.png"},{"id":51734557,"identity":"3309b7c5-0681-466a-80b1-7500bbc5869e","added_by":"auto","created_at":"2024-02-28 06:17:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1536152,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3976210/v1/09d0e867-c019-42e0-84c1-fd442d5270b2.pdf"},{"id":51717025,"identity":"3ffb78f3-e331-401c-8647-f104e9853260","added_by":"auto","created_at":"2024-02-27 21:11:46","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":396267,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-3976210/v1/a2f0ec17dfb41b3ceffd9cb0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Development and validation of a prognostic model predicting the prognosis of surgically treated non-clear cell renal cell carcinoma patients with tumor thrombus","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatients with renal cell carcinoma (RCC) have a propensity to invade the venous system. In approximately 10% of RCC cases, tumors can extend into the renal vein or inferior vena cava (IVC) and even the right atrium(Topaktaş et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). RCC with tumor thrombus (TT) is associated with poor prognosis, higher Fuhrman grade, and larger tumor size(Tilki et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A meta-analysis indicated that the 5-year cancer-specific survival (CSS) after surgery was only 25%-53% for these cases, emphasizing the necessity for meticulous prognosis assessment(Gu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Currently, postoperative outcomes following radical nephrectomy with thrombectomy for TT patients have been reported, revealing variations by specific histologic subtypes(Tilki et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ciancio et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Non-clear cell renal cell carcinoma (nccRCC) constitutes 20\u0026ndash;30% of RCC cases(Bukavina et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Patients with nccRCC and tumor thrombus (nccRCC-TT) experience worse outcomes compared to clear cell RCC (ccRCC)(Tilki et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Ciancio et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). However, Rabinowitz(Rabinowitz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) analyzed 103 patients (82 ccRCC and 21 nccRCC) and found no significant survival difference between the two groups. Hence, a study involving larger sample size is imperative to futher assess the prognostic impact of histological subtypes in TT patients.\u003c/p\u003e \u003cp\u003eDespite several studies have reported that the tumor size, grade, lymph node status, and distant metastases are risk factors for the poor prognosis of RCC with TT(Gu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zapała et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), these studies most focused on ccRCC. While the postoperative survival prognostic factors for nccRCC-TT, constrained by small sample sizes, remain unclear. The TNM staging system is often used to predict survival outcomes. Nevertheless, it only accounts for the impact of some tumor characteristics such as size, invasion status, and metastasis on the survival of patients, and does not consider individual differences such as sex, age and histology in prognosis. Currently, no prediction model exclusively predicts the survival of nccRCC-TT patients.\u003c/p\u003e \u003cp\u003eTherefore, this study utilized a large dataset of nccRCC-TT cases from the Surveillance, Epidemiology and End Results (SEER) database. We aimed to investigate independent prognostic factors for overall survival (OS) and CSS in nccRCC-TT patients after surgical resection and construct a nomogram predicting the 1-, 3-, and 5-year survival for these patients.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patient selection\u003c/h2\u003e \u003cp\u003ePatients diagnosed with nccRCC-TT between 2010 and 2020 were initially identified from the SEER database using SEER*Stat version 8.35. The histological classification was based on the International Classifcation of Diseases Codes for Oncology (ICD-O) proposed in 2000. Inclusion criteria comprised: (i) primary site code C64.9, ICDO-3 histology codes 8260/3 (papillary renal cell carcinoma, pRCC), 8317/3 (chromophobe renal cell carcinoma, chRCC), 8010/3 (carcinoma unclassified), 8319/3 and 8510/3 (Bellini RCC)(Moch et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e); (ii) presence of TT (based on the major vein involvement recode); (iii) only unilateral and one primary tumor; (iv) histologically confirmed diagnosis; (v) active follow-up to ensure a reliable patient status. The exclusion criteria were as follows: (i) missing important data; (ii) unknown status of TT involvement recode; (iii) those who have received neoadjuvant therapy; (iv) patient died within 1 month or had a follow-up duration less than 1 month. An external validation set comprising 79 patients complying with the above criteria was collected from our REMEMBER (Research of Multi-institution in East-China on Malignant and Benign Epithelial Renal Tumors) database in China (2015\u0026ndash;2022). The histological subtypes of nccRCC were: pRCC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;401), chRCC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;249), Bellini RCC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;52) and Others (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;28) in the SEER set; and pRCC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54), chRCC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), Bellini RCC (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12) and Others (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;11) in our external validation set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study sets and variables\u003c/h2\u003e \u003cp\u003e Patients in the SEER set were randomly divided into training and validation sets in a 7:3 ratio using the R package \u0026ldquo;caret\u0026rdquo;. The training set was utilized for variable screening and model construction. The validation set was employed to validate the results. We selected 12 possible risk factors, which included age, sex, race, laterality, histology, TNM stage based on the 6th AJCC staging system (2010\u0026ndash;2015) and 7th AJCC staging system (2016\u0026ndash;2020), postoperative systemic therapy, renal sinus/perirenal fat invasion, sarcomatoid/rhabdoid differentiation, major vein involvement, tumor size and pathological grade. Additionally, the TT was divided into two groups (renal vein thrombus or inferior vena cava thrombus) based on the major vein involvement recode to assess the level of TT and balance the effect of different T stages in the two versions. According to the Fuhrman grade system, we combined GI and GII into Low (I/II), GIII and GIV into High (III/IV) for further analyses. The chRCC subtype and some cases with unknown grading were denoted as not available (NA). Overall survival (OS) was defined as the time from surgery to death of any cause, and the cancer specific survival (CSS) was defined as the time from surgery to the death specifically caused by renal tumor.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analuses were performed using R version 4.3.1 software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003cspan address=\"http://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Continuous variables were presented as median and interquartile range (IQR), and categorical variables as frequency (percentage). Optimal age and tumor size cut-off values were determined using X-tile version 3.6.1 software (Yale University School of Medicine, New Haven, Conn)(Camp et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Univariable and multivariable Cox proportional hazards regression analyses were performed for OS and CSS. Variables with a univariate \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were included in multivariate analyses to identify independent prognostic factors. Hazard ratios (HRs) were presented with 95% confidence intervals (CIs). The final prognostic factors were incorporated into two nomograms predicting the 1-, 3- and 5-year OS and CSS rates, respectively.\u003c/p\u003e \u003cp\u003eDiscrimination accuracy was measured by Harrell\u0026rsquo;s concordance index (C-index) and receiver operating characteristic (ROC) curves, with corresponding areas under the curves (AUC) computed. Time-dependent ROC analysis was also conducted. Calibration curves, generated using the bootstrap method (resampling\u0026thinsp;=\u0026thinsp;1000) in both training and validation sets, assessed the consistency of the nomogram. Decision curve analysis (DCA)(Fitzgerald et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), a method evaluating prediction models by calculating clinical net benefit, was conducted across all sets. The performance of the nomogram was compared with the AJCC TNM staging system.\u003c/p\u003e \u003cp\u003eIndividual risk scores were calculated for each patient, based on which patients were categorized into low-risk, moderate-risk, or high-risk groups using X-tile software. Kaplan-Meier curves were plotted for these groups to further assess calibration, with differences evaluated using the log-rank test. Finally, two online tools regarding OS and CSS of nccRCC-TT patients were built using the \u0026ldquo;DynNom\u0026rdquo; and \u0026ldquo;Shiny\u0026rdquo; R packages, accessible via the Shiny website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.shinyapps.io/\u003c/span\u003e\u003cspan address=\"https://www.shinyapps.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll \u003cem\u003eP\u003c/em\u003e-values were two-tailed, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Clinicopathologic and follow‑up data\u003c/h2\u003e \u003cp\u003eIn total, 730 patients from the SEER database and 79 patients from the China database were eventually enrolled in this study. Patients from SEER were randomly divided into a training set (\u003cem\u003en\u003c/em\u003e = 514) and an internal validation set (\u003cem\u003en\u003c/em\u003e = 216). The cut-off value of age and tumor size in the training set was 68 years and 6.8cm, respectively. Predominantly, patients in the SEER set were ≤ 68 years old and male, consistent with our external validation set. Histologically, pRCC accounted for more than half in both the SEER set (54.9%) and the external validation set (68.4%). Notably, chRCC with TT exhibited the highest survival rate, while Bellini RCC demonstrated the poorest (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-B). The T3b stage was noted for the majority of patients (46% and 40.5%) in both databases. Inferior vena cava TT was observed in 22.1% of the SEER set and 60.8% in the external validation set, possibly due to the latter's more advanced stage (N1: 38% vs. 28.2%; M1: 27.8% vs. 20.3%). Patients who received systemic therapy after surgery were 18.4% in the SEER set and 36.7% in the external validation set. NccRCC-TT tumors generally exhibited size \u0026gt; 6.8 cm and a high Fuhrman grade in both sets. Detailed characteristics are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\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\u003eDemographic and clinical characteristics of patients with nccRCC-TT.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\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\u003eSEER set (N = 730)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTraining\u003c/p\u003e \u003cp\u003e(N = 514)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInternal validation (N = 216)\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\u003eExternal validation set (N = 79)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\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 (years)\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≤ 68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e485 (66.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e352 (68.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e133 (61.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e70 (88.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e245 (33.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e162 (31.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83 (38.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9 (11.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e243 (33.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (32.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75 (34.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (36.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.627\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\u003e487 (66.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e346 (67.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e141 (65.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (63.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e530 (72.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e375 (73%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e155 (71.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.893\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican American\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e151 (20.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104 (20.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47 (21.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0 (0%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian and Others\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49 (6.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (6.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14 (6.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e79 (100%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e390 (53.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e269 (52.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121 (56%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e42 (53.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.000\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\u003e340 (46.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245 (47.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95 (44%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37 (46.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epRCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e401 (54.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e292 (56.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e109 (50.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54 (68.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echRCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e249 (34.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168 (32.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (37.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (2.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBellini RCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52 (7.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35 (6.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17 (7.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12 (15.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (3.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19 (3.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9 (4.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11 (13.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT stage\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3a\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (42.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e215 (41.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e97 (44.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (34.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3b\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e336 (46%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e245 (47.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91 (42.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32 (40.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3c\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35 (4.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (5.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7 (3.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (12.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (6.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (5.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21 (9.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10 (12.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombus level\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal vein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e569 (77.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e401 (78%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168 (77.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31 (39.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior vena cava\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161 (22.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e113 (22%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48 (22.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e48 (60.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0/Nx\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e524 (71.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e368 (71.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (72.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (62%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e206 (28.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e146 (28.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60 (27.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (38%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM stage\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e582 (79.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e409 (79.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173 (80.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.953\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57 (72.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e148 (20.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e105 (20.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (19.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22 (27.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumour size (cm)\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≤ 6.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e226 (31%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e156 (30.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (32.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27 (34.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 6.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e504 (69%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e358 (69.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e146 (67.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52 (65.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic therapy\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e596 (81.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e416 (80.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e180 (83.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e50 (63.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\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\u003e134 (18.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e98 (19.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (16.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29 (36.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal sinus/Perirenal fat invasion\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e203 (27.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (26.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67 (31%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30 (38%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.078\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\u003e527 (72.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e378 (73.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e149 (69%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e49 (62%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcomatoid/Rhabdoid feature\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\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\u003e640 (87.7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e447 (87%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e193 (89.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55 (69.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\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\u003e90 (12.3%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67 (13%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (10.6%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24 (30.4%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuhrman grade\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (1–2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (7%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40 (7.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (5.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.406\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (2.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (3–4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e351 (48.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e247 (48.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e104 (48.1%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75 (94.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e328 (44.9%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e227 (44.2%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101 (46.8%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2 (2.5%)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOS month (Median (95%CI))\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.0 (37.8–54.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.0 (36.1–53.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e49.0 (28.8–69.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.764\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.0 (18.5–41.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCSS month (Median (95%CI))\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61.0 (37.1–84.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59.0 (29.7–88.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.0 (51.3–94.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up month OS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.0 (45.8–56.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.0 (43.9–58.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.0 (37.5–56.5)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.0 (21.6–34.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; .001\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up month CSS\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.0 (39.1–48.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0 (38.1–49.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.0 (34.2–47.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNOTE. Data are presented as No. (%) unless indicated otherwise.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eIQR, interquartile range; OS, Overall Survival; CSS, Cancer Specific Survival; NA, not available.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003epRCC, papillary renal cell carcinoma; chRCC, chromophobe renal cell carcinoma; Bellini RCC, Bellini renal cell carcinoma/collecting duct carcinoma; Others, Unclassified carcinoma.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eP\u003c/em\u003e values were calculated by Chi-square test for categorical variables and Cochran-Mantel-Haenszel(CMH) Chi-square test for ordinal variables.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eThe overall cohort has been randomly divided into a training (70%) and a validation (30%) cohort.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter a median follow-up time of 51 months (95%CI, 45.8–48.9) in the SEER set and 28 months (95%CI, 21.6–34.4) in the external validation set, the median OS time was 46 months (95%CI, 37.8–54.2) and 30 months (95%CI, 18.5–41.5), respectively. The median CSS in the SEER set, after a median follow-up of 44 months (95% CI, 39.1–48.9), was 61 months (95% CI, 37.1–84.9). At the end of follow-up, the 5-year OS rate was 43.1% in the SEER set and 27.4% in the external validation set. The 5-year CSS rate was 50.4% in the SEER set.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Construction of the nomogram\u003c/h2\u003e \u003cp\u003eIn the training set, independent risk factors for OS were obtained: age \u0026gt; 68 (HR: 1.88, 95%CI, 1.45–2.45, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), laterality (HR: 0.74, 95%CI, 0.57–0.96, \u003cem\u003eP\u003c/em\u003e = 0.026), histology (Bellini RCC vs. pRCC; HR: 2.10, 95%CI, 1.38–3.19, \u003cem\u003eP\u003c/em\u003e = 0.001), N1 stage (HR: 1.76, 95%CI, 1.28–2.41, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), M1 stage (HR: 2.44, 95%CI, 1.78–3.34, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), renal sinus/perirenal fat invasion (HR: 1.44, 95%CI, 1.04-2.00, \u003cem\u003eP\u003c/em\u003e = 0.027), and sarcomatoid/rhabdoid feature (HR: 1.72, 95%CI, 1.23–2.40, \u003cem\u003eP\u003c/em\u003e = 0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Similarly, multivariate analysis results suggested that age \u0026gt; 68 (HR: 1.61, 95%CI, 1.19–2.18, \u003cem\u003eP\u003c/em\u003e = 0.002), laterality (HR: 0.69, 95%CI, 0.52–0.93, \u003cem\u003eP\u003c/em\u003e = 0.014), histology (Bellini RCC vs. pRCC; HR: 2.39, 95%CI, 1.53–3.72, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), N1 stage (HR: 1.92, 95%CI, 1.36–2.72, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), M1 stage (HR: 2.73, 95%CI, 1.94–3.86, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), renal sinus/perirenal fat invasion (HR: 1.50, 95%CI, 1.02–2.19, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.038), and sarcomatoid/rhabdoid feature (HR: 1.97, 95%CI, 1.39–2.79, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) were independent risk factors for CSS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Nomograms predicting OS and CSS risk for patients at 1-, 3-, or 5-years were then respectively constructed based on identified variables (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\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 and multivariate Cox analyses on variables for the prediction of OS and CSS of nccRCC-TT patients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eOverall survival\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eCancer-specific survival\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eUnivariable\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eMultivariable\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95%CI)\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≤ 68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.76(1.36–2.28)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.88(1.45–2.45)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.46(1.09–1.95)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.61(1.19–2.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eRef.\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e1.19(0.90–1.56)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.29(0.95–1.74)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCaucasian\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAfrican American\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.23(0.91–1.66)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.39(1.01–1.93)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.045\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17(0.82–1.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian and Others\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11(0.67–1.82)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.694\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32(0.79–2.22)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.17(0.69-2.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaterality\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e0.75(0.58–0.97)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.030\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.74(0.57–0.96)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.70(0.53–0.93)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.69(0.52–0.93)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epRCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echRCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.41(0.29–0.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.71(0.43–1.18)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35(0.24–0.51)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.86(0.46–1.61)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.644\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBellini RCC\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.17(1.44–3.26)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.10(1.38–3.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.39(1.56–3.67)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.39(1.53–3.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.97(0.44–2.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10(0.51–2.35)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.813\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.27(0.57–2.83)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThrombus level\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal vein\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInferior vena cava\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62(1.22–2.16)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03(0.76–1.39)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.73(1.27–2.36)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.02(0.73–1.42)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.925\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN stage\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN0/Nx\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.83(2.18–3.67)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.76(1.28–2.41)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.39(2.56–4.50)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.92(1.36–2.72)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM stage\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eM1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.12(2.37–4.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.44(1.78–3.34)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.68(2.74–4.93)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.73(1.94–3.86)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumour size (cm)\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e≤ 6.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 6.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.51(1.13–2.03)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.006\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32(0.97–1.78)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.076\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53(1.10–2.12)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.011\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.23(0.87–1.73)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystemic therapy\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e1.93(1.45–2.57)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77(0.54–1.09)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.23(1.64–3.02)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.78(0.53–1.14)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenal sinus/Perirenal fat invasion\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e1.91(1.40–2.61)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.44(1.04-2.00)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.17(1.51–3.11)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.50(1.02–2.19)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e0.038\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcomatoid/Rhabdoid feature\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\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\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e2.44(1.78–3.35)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.72(1.23–2.40)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.06(2.21–4.26)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.97(1.39–2.79)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt; .001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFuhrman grade\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=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (1–2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRef.\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (3–4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.99(1.24–3.21)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.005\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.41(0.87–2.30)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95(1.18–3.24)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.31(0.78–2.22)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.79(0.48–1.32)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.96(0.52–1.78)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.908\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.62(0.35–1.07)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.66(0.33–1.33)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eAbbreviations: HR, hazard ratio; CI, confidential interval; NA, not available; Ref, Reference.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003epRCC, papillary renal cell carcinoma; chRCC, chromophobe renal cell carcinoma; Bellini RCC, Bellini renal cell carcinoma/collecting duct carcinoma; Others, Unclassified carcinoma.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Validation of the nomogram\u003c/h2\u003e \u003cp\u003eThe calibration plots in the training, internal validation, and external validation sets for 1-, 3-, and 5-year OS and CSS were depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-E. The nomogram exhibited a C-index for OS prediction of 0.734 (95%CI, 0.703–0.765) in the training set, 0.774 (95%CI, 0.727–0.821) in the internal validation set, and 0.705 (95%CI, 0.600-0.811) in the external validation set. The AUCs for 1-, 3- and 5-year OS prediction were 0.766, 0.787, and 0.818 in the training set, 0.814, 0.866, and 0.815 in the internal validation set, 0.774, 0.668 and 0.601 in the external validation set, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-C). While the TNM staging system for OS prediction exhibited a lower C-index: 0.675 (95%CI, 0.640–0.710) in the training set, 0.713 (95%CI, 0.660–0.766) in the internal validation set, and 0.649 (95%CI, 0.541–0.757) in the external validation set. Furthermore, the time-dependent AUC curves for OS, including nomogram and stage, demonstrated the nomogram's superior predictive value compared to the commonly used risk factor TNM stage (\u003cb\u003eSupplementary Fig.\u0026nbsp;1. A-C\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimultaneously, the C-index of the nomogram for CSS prediction surpassed that of the TNM staging system, with 0.759 (95%CI, 0.726–0.792) vs. 0.709 (95%CI, 0.672–0.746) in the training set and 0.787 (95%CI, 0.736–0.838) vs. 0.730 (95%CI, 0.673–0.787) in the internal validation set. The AUCs for 1-, 3- and 5-year CSS prediction were 0.793, 0.807, and 0.838 in the training set and 0.825, 0.870, and 0.824 in the internal validation set (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e4\u003c/span\u003eD-E). Additionally, the time-dependent AUC curves for the CSS nomogram were displayed in \u003cb\u003eSupplementary Fig.\u0026nbsp;1. D-E\u003c/b\u003e, futher indicated the nomogram's stability and discriminability compared to the TNM staging system.\u003c/p\u003e \u003cp\u003eThe DCA plots for 1-, 3-, and 5-year rates of OS and CSS in the training and validation sets illustrated that our nomogram for nccRCC-TT achieved positive net clinical benefits across a broad range of threshold probabilities, emphasizing its high clinical utility (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Risk stratification\u003c/h2\u003e \u003cp\u003eFollowing the acquisition of risk scores for each of the 730 SEER dataset patients through the nomogram, patients with nccRCC-TT were categorized into low-, moderate-, and high-risk groups for OS prediction, with cut-off points at 96 and 193. The Kaplan-Meier OS curves demonstrated significant discrimination among these groups (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). The median OS for high-risk, moderate-risk, and low-risk groups were 8 months (95%CI, 0.44–0.65), 24 months (95%CI, 0.46–0.59), and 117 months (95%CI, 0.39–0.62), respectively. The 3-year OS rates for these groups were 6.0% (95%CI, 0.02–0.15), 39.5% (95%CI, 0.33–0.47), and 79.6% (95%CI, 0.75–0.84), respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSimilarly, the cut-off points of the nomogram for CSS prediction were 99 and 189. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, the risk of specific death from nccRCC-TT significantly increased with rising risk levels (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Median CSS for high-risk, moderate-risk, and low-risk groups were 9 months (95%CI, 0.40–0.63), 29 months (95%CI, 0.44–0.58), and not reached (95%CI, NA-NA), respectively. The 3-year CSS rates for these groups were 5.1% (95%CI, 0.02–0.15), 44.5% (95%CI, 0.38–0.52), and 84.1% (95%CI, 0.80–0.89), respectively. The median time, 1-, 3-, and 5-year rates for the three risk groups of OS and CSS were summarized in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\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\u003eRisk stratification for the nomogram of OS and CSS.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRisk group\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian time\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1-year rate\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3-year rate\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5-year rate\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 months (95%CI 0.44–0.65)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.3% (95%CI 0.30–0.50)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.0% (95%CI 0.02–0.15)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enot reached (95%CI NA-NA)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModerate risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 months (95%CI 0.46–0.59)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.2% (95%CI 0.66–0.77)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e39.5% (95%CI 0.33–0.47)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.0% (95%CI 0.19–0.33)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLow risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e117 months (95%CI 0.39–0.62)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e92.2% (95%CI 0.89–0.95)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.6% (95%CI 0.75–0.84)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e67.9% (95%CI 0.62–0.74)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eHigh risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 months (95%CI 0.40–0.63)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37.0% (95%CI 0.28–0.49)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.1% (95%CI 0.02–0.15)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003enot reached (95%CI NA-NA)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eModerate risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29 months (95%CI 0.44–0.58)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.0% (95%CI 0.69–0.80)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.5% (95%CI 0.38–0.52)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e30.5% (95%CI 0.24–0.39)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLow risk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003enot reached (95%CI NA-NA)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e94.5% (95%CI 0.92–0.97)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.1% (95%CI 0.80–0.89)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e76.4% (95%CI 0.71–0.82)\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eOS, overall survival; CSS, cancer-specific survival; NA, not available.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003eAdditionally, two dedicated online tools for OS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://njmumh1997.shinyapps.io/os-prediction/\u003c/span\u003e\u003cspan address=\"https://njmumh1997.shinyapps.io/os-prediction/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CSS (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://njmumh1997.shinyapps.io/css-prediction/\u003c/span\u003e\u003cspan address=\"https://njmumh1997.shinyapps.io/css-prediction/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were built to conveniently calculate nomogram scores, determine risk groups, and predict survival for each patient.\u003c/p\u003e \u003c/div\u003e "},{"header":"Discussion","content":"\u003cp\u003eRenal cell carcinoma (RCC) often infiltrates the renal venous system and causes tumor thrombus, impacting 4–13% of newly diagnosed RCC patients and associating with an unfavorable prognosis(Wang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The incidence of nccRCC-TT is approximately 10% and pRCC accounts for the largest proportion of nccRCC(Bukavina et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rigaud et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Despite the radical nephrectomy with thrombectomy was performed, the long-term survival of RCC with TT (RCC-TT) remains unsatisfactory compared to localized RCC. Several studies have evaluated prognostic factors in RCC-TT patients. However, due to limited sample sizes, most previous studies categorized RCC-TT patients into ccRCC and nccRCC groups without further subclassifying the histology of nccRCC, which has limited the significance of pathological stratification(Wang et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, pathological types other than ccRCC and pRCC with TT were often excluded by previous studies because of their rarity. Hence, the postoperative survival prognostic factors of nccRCC-TT group remain unclear. To address this gap, we utilized a large database to develop prognostic nomograms for individual predictions of OS and CSS in nccRCC-TT patients, subsequently validating the models externally. Ultimately, seven independent risk factors for OS and CSS were identified: advanced age, left side, histology, positive lymph nodes, distant metastasis, renal sinus/perirenal fat invasion, and the presence of sarcomatoid/rhabdoid features. Owing to the relatively short follow-up time and histological subtype proportion difference, the C-index of the external validation set (0.705) for OS was inferior to the internal validation set but still superior to the TNM staging system (0.649).\u003c/p\u003e\u003cp\u003eIn the exploration of prognostic factors for RCC-TT, most studies analyzed the non-metastatic and metastatic patients together. Adverse predictors for both CSS and OS, such as large tumor size, high Fuhrman grade, tumor necrosis, positive lymph nodes, and metastasis at surgery, have been widely indicated. Besides, inferior CSS has been associated with IVC tumor thrombus, sarcomatoid differentiation, perinephretic fat invasion, and adrenal gland invasion(Gu et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Zapała et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gayed et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Niedworok et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our focused study on ccRCC-TT patients, lymph node positivity, preoperative distant metastasis, sarcomatoid/rhabdoid differentiation, and renal sinus/perinephric fat invasion independently affect both OS and CSS. This underscores the profound impact of these factors on the survival outcomes of patients with TT involvement, regardless of ccRCC or nccRCC. Additionally, we observed that advanced age at diagnosis and left-sided TT were also associated with inferior survival outcomes. Similar findings were reported by David et al.(Thiel et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who observed a higher risk of RCC-related death for left-sided patients compared to their right-sided counterparts (HR: 2.02) among those with a thrombus reaching the IVC. Due to physiological differences in structure, the left renal vein surpasses the right in anatomical length by more than threefold. Under equivalent TT lengths, the tumor is more likely to extend into the IVC on the right side, often exhibiting a more pronounced advancement compared to the contralateral side. The prognostic significance of the level of TT has been controversial, with some studies reporting it as an independent prognostic factor for survival(Haferkamp et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Martínez-Salamanca et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), while others did not find it significant(Ciancio et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Zapała et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In our analysis, we found that although IVC thrombus was a significant predictor of survival in univariate analysis, it did not retain its independence on multivariate analysis.\u003c/p\u003e\u003cp\u003eWhile the existence of TT generally contributes to a worse prognosis in RCC patients, notable variations in survival persist among different histological subtypes. Most studies agree that ccRCC generally has a more favorable prognosis. A study by Dharam et al. (Kaushik et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) revealed that nccRCC-TT patients exhibit a larger tumor size, higher nuclear grade, more frequent sarcomatoid differentiation, and increased lymph node invasion, leading to a worse prognosis. Similarly, studies by the International Renal Cell Carcinoma venous Thrombus Consortium(Martínez-Salamanca et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Gaetano et al.(Ciancio et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and Derya et al.(Tilki et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) also suggested significantly worse CSS for nccRCC histological types. In contrast, studies by Nocera et al.(Nocera et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and Matthew et al.(Rabinowitz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) indicated that both clear cell and non-clear cell demonstrated similarly poor OS in patients with TT, with no clinically meaningful differences between the groups. However, Nocera’s subgroup excluded patients with stage T3a and M1, and Matthew’s study had a small sample size, potentially contributing to the heterogeneous results. Our study further underscores the pivotal role of non-clear cell histological subtypes on OS and CSS outcomes in nccRCC-TT patients, with Bellini RCC associated with the least favorable prognosis, followed by pRCC, and chRCC demonstrating the best prognosis.\u003c/p\u003e\u003cp\u003eThe advent of novel immune checkpoint inhibitors has led to unprecedented benefits for patients undergoing systemic immunotherapy following radical nephrectomy, particularly reported in ccRCC(Motzer et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). However, the benefits of immune checkpoint inhibition in nccRCC have yet to be defined further. A retrospective real-life cohort of advanced nccRCC patients shows that immunotherapy-based combinations could improve OS compared to TKI monotherapy(Bimbatti et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). While results from CheckMate920 indicated that Nivolumab plus ipilimumab for previously untreated advanced nccRCC showed no new safety signals and encouraging antitumor activity(Tykodi et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In our multivariate analysis, 18.4% of patients received postoperative systemic therapy (HR: 0.77). Prospective trials for nccRCC patients utilizing novel therapies are ongoing and their results eagerly awaited. Hence, determining the suitability of nccRCC-TT patients for systemic therapy is crucial(Zoumpourlis et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study has several limitations that should be considered while interpreting our results. Firstly, the study is limited by its retrospective nature. Secondly, details regarding the use and duration of systemic therapy within the SEER database are unclear, and essential perioperative variables were not evaluated. This lack of data on therapy may affect the reported survival outcomes. Thirdly, the size of the external validation set was relatively scant, and the medium follow-up time was only 28 months, so we were unable to achieve good performance of 3- and 5-year OS prediction in the external validation set. Therefore, more multicenter clinical validation is imperative to evaluate the external utility of our nomogram.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study pioneers survival prediction models in nccRCC-TT patients after surgery, highlighting the efficacy of our nomograms in predicting OS and CSS time. The nomogram’s C-index demonstrated its superiority to the AJCC-TNM categorisation regarding predictive capacity. Calculating scores and stratifying risks based on the nomogram model can facilitate personalized follow-up and subsequent treatment for these patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Natural Science Foundation of China Funding (82373154 to Le Qu and 82072836 to Wenquan Zhou).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Material preparation and data collection were performed by He Miao, Ye Zhou, Hui Chen, Silun Ge, Yufeng Gu, Xin Pan, Xing Zeng, Cheng Zhao and Shaogang Wang, Jingping Ge and Linhui Wang. The data analysis was performed by He Miao, Chang Lei and Yulin Zhou. The study was sponsored by Le Qu and Wenquan Zhou. The first draft of the manuscript was written by He Miao and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data for this study were obtained from publicly available databases (https://seer.cancer.gov/). The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the institutional review board of initiating center Jinling Hospital (ID Number: 2021NZKY-004-01).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEach author agreed to publish the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTopaktaş R, \u0026Uuml;rkmez A, Toku\u0026ccedil; E, et al. 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Eur Urol, 2011, 59(1), 120-7. https://doi.org/10.1016/j.eururo.2010.10.001\u003c/li\u003e\n\u003cli\u003eKaushik D, Linder B J, Thompson R H, et al. The impact of histology on clinicopathologic outcomes for patients with renal cell carcinoma and venous tumor thrombus: a matched cohort analysis [J]. Urology, 2013, 82(1), 136-41. https://doi.org/10.1016/j.urology.2013.02.034\u003c/li\u003e\n\u003cli\u003eMart\u0026iacute;nez-Salamanca J I, Linares E, Gonz\u0026aacute;lez J, et al. Lessons learned from the International Renal Cell Carcinoma-Venous Thrombus Consortium (IRCC-VTC) [J]. Curr Urol Rep, 2014, 15(5), 404. https://doi.org/10.1007/s11934-014-0404-7\u003c/li\u003e\n\u003cli\u003eNocera L, Coll\u0026agrave; Ruvolo C, Stolzenbach L F, et al. Tumor Stage and Substage Predict Cancer-specific Mortality After Nephrectomy for Nonmetastatic Renal Cancer: Histological Subtype-specific Validation [J]. Eur Urol Focus, 2022, 8(1), 182-90. https://doi.org/10.1016/j.euf.2021.02.009\u003c/li\u003e\n\u003cli\u003eMotzer R J, Banchereau R, Hamidi H, et al. Molecular Subsets in Renal Cancer Determine Outcome to Checkpoint and Angiogenesis Blockade [J]. Cancer Cell, 2020, 38(6), 803-17.e4. https://doi.org/10.1016/j.ccell.2020.10.011\u003c/li\u003e\n\u003cli\u003eBimbatti D, Pierantoni F, Lai E, et al. Advanced Non-Clear Cell Renal Cell Carcinoma Treatments and Survival: A Real-World Single-Centre Experience [J]. Cancers (Basel), 2023, 15(17), https://doi.org/10.3390/cancers15174353\u003c/li\u003e\n\u003cli\u003eTykodi S S, Gordan L N, Alter R S, et al. Safety and efficacy of nivolumab plus ipilimumab in patients with advanced non-clear cell renal cell carcinoma: results from the phase 3b/4 CheckMate 920 trial [J]. J Immunother Cancer, 2022, 10(2), https://doi.org/10.1136/jitc-2021-003844\u003c/li\u003e\n\u003cli\u003eZoumpourlis P, Genovese G, Tannir N M, et al. Systemic Therapies for the Management of Non-Clear Cell Renal Cell Carcinoma: What Works, What Doesn\u0026apos;t, and What the Future Holds [J]. Clin Genitourin Cancer, 2021, 19(2), 103-16. https://doi.org/10.1016/j.clgc.2020.11.005\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":"Non-clear cell renal cell carcinoma, Tumor thrombus, Survival, Nomogram, Prognostic model","lastPublishedDoi":"10.21203/rs.3.rs-3976210/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3976210/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAccurate prediction of clinical outcomes in non-clear cell renal cell carcinoma with tumor thrombus (nccRCC-TT) patients is crucial for counseling, follow-up planning, and selecting appropriate systemic therapy. We aimed to investigate independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) in nccRCC-TT patients after surgical resection and construct a nomogram predicting the 1-, 3-, and 5-year survival for these patients.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis was a retrospective analysis of data from the Surveillance, Epidemiology, and End Results (SEER) database (2010\u0026ndash;2020) and the China REMEMBER database with nccRCC-TT patients. NccRCC-TT patients from the SEER database were randomly divided into training and internal validation sets. Multivariable nomogram models were built and validated to predict OS and CSS. Scores based on the nomograms were used to conduct risk stratification. The performance of these nomograms was then compared with the American Joint Committee on Cancer (AJCC) TNM staging system.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 809 patients participated, with a training set (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;514), an internal validation set (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;216), and an external validation set (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;79). Median follow-up times for OS were 51, 47, and 28 months in the three sets, respectively. The nomogram integrated seven risk factors affecting survival (advanced age, left side, histology, positive lymph nodes, distant metastasis, renal sinus/perirenal fat invasion, and sarcomatoid/rhabdoid differentiation) to predict OS and CSS at 1-, 3-, and 5-years. Outperforming the AJCC staging system, the nomogram achieved a C-index of 0.774 (95% CI, 0.727\u0026ndash;0.821) for OS and 0.787 (95% CI, 0.736\u0026ndash;0.838) for CSS in the internal validation set. Both OS and CSS significantly differed between subgroups with low, moderate, and high risk (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePathological combined histological features are crucial predictors of prognosis in nccRCC-TT patients. We developed a tool to improve patient counseling and guide decision-making on other therapies in addition to surgery for patients with nccRCC-TT. Risk stratification based on our nomograms provides postoperative consultation and patient selection for treatment strategies.\u003c/p\u003e","manuscriptTitle":"Development and validation of a prognostic model predicting the prognosis of surgically treated non-clear cell renal cell carcinoma patients with tumor thrombus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-27 21:10:27","doi":"10.21203/rs.3.rs-3976210/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":"c52f3c67-12e9-43f8-9de9-03d6e00f7339","owner":[],"postedDate":"February 27th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-23T04:14:13+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-27 21:10:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3976210","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3976210","identity":"rs-3976210","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}