Developing and validating an innovative risk stratification model for non-metastatic clear cell renal cell carcinoma patients with venous tumor thrombus

Developing and validating an innovative risk stratification model for non-metastatic clear cell renal cell carcinoma patients with venous 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 Developing and validating an innovative risk stratification model for non-metastatic clear cell renal cell carcinoma patients with venous tumor thrombus Baohua Zhu, Ziyang Mo, Na Ta, Linhui Wang, Wei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5636265/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 Purpose: Exploring the survival influencing factors in patients with non-metastatic clear cell renal cell carcinoma (ccRCC) and venous tumor thrombus (VTT) is vital for tailored therapies. Our objective was to develop and validate a novel risk scoring system for the patients to predict the survival time and probability. Methods: Data were gathered from non-metastatic ccRCC patients with VTT treated between 2011 and 2024. Participants were retrospectively assigned in a 7:3 ratio to training and testing cohorts. We evaluated and quantified clinicopathological characteristics of the primary tumor (PT) and VTT, constructing multivariable models to predict overall survival (OS). Results: The study included 124 patients, with a median follow-up of 35 months. We developed a risk score system based on PT Sarcomatoid differentiation (p = 0.034), PT perirenal fat invasion (p = 0.046), VTT grade (p = 0.045) and Neutrophil to Lymphocyte Ratio(NLR) (p = 0.007). This system accurately identified a high-risk cohort exhibiting adverse outcomes among non-metastatic ccRCC patients with VTT, findings consistent in the testing group. Conclusion: Our study presents a nomogram integrating clinicopathological features—PT Sarcomatoid differentiation, PT perirenal fat invasion, VTT grade and NLR—facilitating risk stratification and enhancing the precision in managing non-metastatic ccRCC patients with VTT. Clear cell renal cell carcinoma Venous tumor thrombus Prognosis Overall Survival Nomogram Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Clear cell Renal cell carcinoma (ccRCC) is one of the most common malignant tumors in the urinary system[ 1 ]. As the detection rate of early-stage ccRCC rises, the 5-year survival rate for these patients has reached 93%[ 2 ]. However, locally advanced or metastatic renal cell carcinoma (RCC) is frequently associated with venous tumor thrombus (VTT), affecting approximately 4–10% of RCC patients[ 3 ]. Historically, venous invasion by ccRCC has been considered a marker of poor prognosis[ 4 ]. Surgical intervention is the main treatment for these patients, and some of them have achieved long-term survival after radical nephrectomy with thrombectomy[ 2 , 5 ]. Regarding the prognosis of patients with ccRCC and VTT and its related factors, the descriptions of previous studies vary greatly due to the limited sample size, and the overall survival time of patients varies from a few months to more than 10 years[ 6 – 8 ]. Traditionally, thrombus has been considered merely an extension of the primary tumor (PT) into the vascular space, with the assumption of similar molecular and histological characteristics[ 9 ]. However, Wang and Shi et al. independently conducted transcriptome sequencing and single-cell RNA sequencing analysis on the normal-tumor-cancer thrombosis triad associated with ccRCC. Their findings revealed that patients with VTT exhibit distinct molecular characteristics compared to those without VTT and the tumor microenvironment between these two patient groups was notably disparate[ 10 , 11 ]. Similarly, The TRACERx Renal project has shed light on the potential role of VTT as a source of metastatic dissemination in patients with ccRCC[ 12 , 13 ]. Furthermore, our prior research also indicates that VTT acts as a reservoir for metastases in the majority of ccRCC patients[ 14 ]. This study aimed to identify independent prognostic factors for ccRCC with VTT by collecting a series of clinical sample data, including VTT grade, and to develop a nomogram for predicting the survival time of clinical patients. Methods Study design and Patients selection From January 2011 to October 2024, a cohort of patients diagnosed as ccRCC with VTT was assembled at Changhai Hospital. The inclusion criteria were as follows: (a) patients with non-metastatic ccRCC featuring VTT graded 0 to IV; (b) patients who underwent radical nephrectomy (RN) with concurrent VTT extraction, without VTT detachment or perioperative mortality; (c) histologically confirmed ccRCC post-surgery; and (d) patients who could be followed up during the study duration. Ultimately, 124 patients with ccRCC and VTT were enrolled. Subsequently, these patients were randomly stratified into a training cohort (n = 86) and a testing cohort (n = 38) in a 7:3 ratio. Data collection The following perioperative clinical data were obtained from the electronic medical records of our institution, including age at surgery, gender, symptoms, body mass index, several preoperative blood routine examination results (Creatinine, Hemoglobin, Blood platelet and Neutrophil to Lymphocyte Ratio (NLR)), VTT Mayo classification, PT tumor size and grade, perirenal fat invasion of PT, sarcomatoid differentiation and necrosis in PT etc. All patients underwent comprehensive preoperative evaluations, encompassing clinical assessments, computed tomography (CT) scans, and/or magnetic resonance imaging (MRI) studies, to establish a definitive diagnosis. The principal outcome was overall survival (OS), measured as the duration from the date of radical surgery to the date of death due to any cause or the censoring date at the conclusion of follow-up, which was finalized in October 2024. VTT Pathological variables We additionally assessed the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system in VTT specimens. The VTT grade was assigned based on the highest grade identified on any slide, regardless of whether it was focal, consistent with the approach used for PT grade. In detail, ISUP grade I was characterized by the absence or subtle presence of nucleoli at ×400 magnification. ISUP grade II was defined by nucleoli that were clearly visible at ×400 magnification but not discernible at ×100 magnification. For ISUP grade III, nucleoli were distinctly apparent at ×100 magnification. ISUP grade IV was distinguished by the presence of marked nuclear pleomorphism, tumor giant cells, and/or the presence of rhabdoid and/or sarcomatoid differentiation. A schematic of the above mentioned VTT grade is shown in Fig. 1 Statistical analysis Continuous variables are reported as means and standard deviations, while categorical variables are presented as percentages. Differences among groups for continuous data were examined using ANOVA or the Kruskal–Walli’s test, depending on the data distribution. Chi-square and Cochran–Mantel–Haenszel tests assessed differences in categorical and ordinal data, respectively. The optimal cut-off value for NLR was determined using Youden’s index. Univariate and multivariate analyses in the training corht were conducted using Cox proportional hazards models to identify independent prognostic factors. A nomogram for predicting patient OS was constructed using the rms package in R. Kaplan-Meier survival curves were generated to compare OS between different groups. Model accuracy was quantified using time-dependent receiver operating characteristic (ROC) curves and areas under the curve (AUC), with model discrimination assessed through the concordance index (C-index) and AUC values. Decision curve analysis (DCA) was performed to explore the clinical utility of the predictive model, identifying net benefits across various probability thresholds. Calibration plots were created to evaluate the accuracy of predicted outcomes against observed events. All p-values were two-tailed, with p < 0.05 considered statistically significant. Statistical analyses were conducted using R software (version 4.1.3). Results Baseline Characteristics The clinical features of the patients comprising both the training and testing cohorts are concisely summarized in Table 1 . Notably, no statistically significant disparities were observed in the clinical and pathological attributes between the training and testing cohorts. Among the 124 study participants, 33 (26.61%) were female, whereas 91 (73.39%) were male. Those who presented with no symptoms, local symptoms and systemic symptoms were 37 (29.84%), 69 (55.65%) and 18 (55.65%) patients. Regarding pathological T staging, 46 cases (37.1%) were classified as T3a, 57 (45.97%) as T3b, 7 (5.65%) as T3c, and 14 (11.29%) as T4. In terms of the Mayo classification, 43 patients (34.68%) were categorized as stage 0, 18 (14.52%) as stage I, 32 (25.81%) as stage II, 24 (19.35%) as stage III, and 7 (5.65%) as stage IV. Nearly half (48.39%) of all patients had PT perirenal fat invasion. The maximum diameter of the tumor ranged from 2 to 20 cm with a mean size of 85 mm. VTT grade (WHO/ISUP grade) was 2 in 39 (31.5%), 3 in 52 (42%), and 4 in 33 (26.5%). PT grade (WHO/ISUP grade) was 2 in 39 (31.45%) cases, 3 in 54 (43.55%), 4 in 31 (25%). Based on the optimal cut-off value derived using the maximum Youden index, a threshold of 3.84 was established for NLR. The median follow-up was 35 months. Table 1 Baseline data of 124 included patients Level Overall Test Train p N 124 38 86 Gender (%) Female 33 (26.61) 10 (26.32) 23 (26.74) 1 Male 91 (73.39) 28 (73.68) 63 (73.26) Age (mean (SD)) 58 (12.60) 57 (12.66) 58 (12.62) 0.5073 BMI (mean (SD)) 23.32 (3.23) 23.13 (3.30) 23.40 (3.21) 0.666 Symptoms (%) None 37 (29.84) 11 (28.95) 26 (30.23) 0.9621 Local symptoms 69 (55.65) 21 (55.26) 48 (55.81) Systemic symptoms 18 (14.52) 6 (15.79) 12 (13.95) ASA_score (%) 1 6 (4.84) 2 (5.26) 4 (4.65) 0.1844 2 89 (71.77) 27 (71.05) 62 (72.09) 3 27 (21.77) 7 (18.42) 20 (23.26) 4 2 (1.61) 2 (5.26) 0 (0.00) Mayor (%) 0 43 (34.68) 15 (39.47) 28 (32.56) 0.9183 Ⅰ 18 (14.52) 6 (15.79) 12 (13.95) Ⅱ 32 (25.81) 8 (21.05) 24 (27.91) Ⅲ 24 (19.35) 7 (18.42) 17 (19.77) Ⅳ 7 (5.65) 2 (5.26) 5 (5.81) Renal_tumour (%) Left 56 (45.16) 19 (50.00) 37 (43.02) 0.6003 Right 68 (54.84) 19 (50.00) 49 (56.98) Preoperative LDH (U/L) (mean (SD)) 209.69 (117.47) 200.95 (87.80) 213.56 (128.70) 0.5836 Preoperative Creatinine (µmol/L) (mean (SD)) 88.48 (24.32) 82.92 (19.85) 90.93 (25.78) 0.091 Preoperative Neutrophils 10 9 /L (mean (SD)) 4.94 (2.31) 4.75 (1.89) 5.03 (2.48) 0.5417 Preoperative lymphocyte 10 9 /L (mean (SD)) 1.36 (0.44) 1.36 (0.48) 1.36 (0.42) 0.9242 Preoperative Blood platelet (mean (SD)) 249.24 (110.22) 252.24 (88.66) 247.92 (118.96) 0.8415 NLR (mean (SD)) 4.43 (4.72) 5.04 (6.95) 4.16 (3.31) 0.3436 Preoperative Hemoglobin (mean (SD)) 115.23 (24.89) 118.03 (24.06) 113.99 (25.30) 0.4072 Operation Time (hours) (mean (SD)) 3.35 (1.28) 3.16 (1.23) 3.434 (1.31) 0.265 Blood_loss (mean (SD)) 1404(1309) 1246.05 (1166) 1474(1368) 0.3736 PT_grade (%) 2 39 (31.45) 13 (34.21) 26 (30.23) 0.5945 3 54 (43.55) 14 (36.84) 40 (46.51) 4 31 (25.00) 11 (28.95) 20 (23.26) VTT_GRADE (%) 2 39 (31.50) 12 (31.58) 27 (31.39) 0.3740 3 52 (42.00) 13 (34.21) 39 (45.34) 4 33 (26.50) 13 (34.21) 20 (23.27) Clavien Grading, n (%) Ⅰ 53 (42.74) 18 (47.37) 35 (40.70) 0.7791 Ⅱ 67 (54.03) 19 (50.00) 48 (55.81) Ⅲ 4 (3.23) 1 (2.63) 3 (3.49) PT Perirenal fat invasion (%) No 64 (51.61) 20 (52.63) 44 (51.16) 1 Yes 60 (48.39) 18 (47.37) 42 (48.84) PT Sarcomatoid differentiation (%) No 105 (84.68) 32 (84.21) 73 (84.88) 1 Yes 19 (15.32) 6 (15.79) 13 (15.12) PT necrosis (%) No 52 (41.94) 15 (39.47) 37 (43.02) 0.8635 Yes 72 (58.06) 23 (60.53) 49 (56.98) pT stage (%) T3a 46 (37.10) 16 (42.11) 30 (34.88) 0.2015 T3b 57 (45.97) 16 (42.11) 41 (47.67) T3c 7 (5.65) 4 (10.53) 3 (3.49) T4 14 (11.29) 2 (5.26) 12 (13.95) pN (%) No 110 (88.71) 36 (94.74) 74 (86.05) 0.2705 Yes 14 (11.29) 2 (5.26) 12 (13.95) Volume (mm) (mean (SD)) 85.815 (36.460) 83.842 (40.496) 86.686 (34.746) 0.6905 OS (%) Alive 56 (45.16) 15 (39.47) 41 (47.67) 0.5155 Death 68 (54.84) 23 (60.53) 45 (52.33) Model development In the training cohort, the above clinical indicators were performed in the univariate analysis. Subsequent multivariate analysis pinpointed PT Sarcomatoid differentiation (p = 0.034), PT perirenal fat invasion (p = 0.046), VTT grade (p = 0.045) and NLR (p = 0.007) as an independent predictor of OS, as detailed in Table 2 . Leveraging these four candidate predictors, a nomogram (Fig. 2 ) was constructed to estimate the likelihood of predicting the survival time of clinical patients. Table 2 Univariate and multivariate Cox regression analysis in the Training cohort Characteristics Total(N) Univariate analysis Multivariate analysis Hazard ratio (95% CI) P value Hazard ratio (95% CI) P value Gender 86 Female 23 Reference Male 63 0.721 (0.387–1.342) 0.302 Age 86 0.999 (0.976–1.022) 0.931 BMI 86 0.958 (0.873–1.050) 0.358 Symptomatic 86 None 26 Reference Local symptoms 48 0.984 (0.513–1.888) 0.962 Systemic symptoms 12 0.733 (0.263–2.039) 0.552 ASA_score 86 1 4 Reference 2 62 0.988 (0.235–4.164) 0.987 3 20 1.383 (0.305–6.261) 0.674 Mayor 86 0 28 Reference Ⅰ 12 1.626 (0.666–3.968) 0.286 Ⅱ 24 1.401 (0.665–2.955) 0.375 Ⅲ 17 0.922 (0.378–2.247) 0.858 Ⅳ 5 0.898 (0.206–3.921) 0.887 Renal tumour 86 Left 37 Reference Right 49 0.739 (0.411–1.330) 0.313 Preoperative LDH 86 1.000 (0.998–1.002) 0.702 Preoperative Creatinine 86 1.002 (0.990–1.014) 0.743 Preoperative blood platelet 86 1.001 (0.999–1.003) 0.272 NLR 86 < 3.84 48 Reference Reference ≥ 3.84 38 4.068 (2.149–7.700) < 0.001 2.703 (1.307–5.590) 0.007 Preoperative Hemoglobin 86 0.998 (0.985–1.011) 0.723 Operation time 86 1.061 (0.850–1.325) 0.601 Blood loss 86 1.000 (1.000–1.000) 0.557 PT grade 86 2 26 Reference 3 40 1.391 (0.648–2.982) 0.397 4 20 2.333 (1.046–5.204) 0.039 VTT_GRADE 86 2 27 Reference Reference 3 39 2.018 (0.900–4.525) 0.088 1.232 (0.514–2.951) 0.640 4 20 4.993 (2.129–11.712) < 0.001 2.517 (1.020–6.213) 0.045 Clavien Grading 86 Ⅰ 35 Reference Ⅱ 48 0.725 (0.395–1.330) 0.298 Ⅲ 3 2.417 (0.713–8.190) 0.156 PT Perirenal fat invasion 86 No 44 Reference Reference Yes 42 4.152 (2.090–8.250) < 0.001 2.162 (1.015–4.606) 0.046 PT Sarcomatoid differentiation 86 No 73 Reference Reference Yes 13 3.421 (1.700–6.883) < 0.001 2.230 (1.063–4.676) 0.034 PT necrosis 86 No 37 Reference Yes 49 0.856 (0.476–1.538) 0.602 pT stage 86 T3a 30 Reference T3b 41 1.328 (0.693–2.546) 0.392 T3c 3 1.159 (0.153–8.803) 0.886 T4 12 1.039 (0.408–2.642) 0.937 pN 86 No 74 Reference Yes 12 1.700 (0.753–3.834) 0.201 Volume 86 1.004 (0.997–1.012) 0.276 Model performance and validation To evaluate the precision of the aforementioned risk score system, we performed cross-validation, yielding Kaplan-Meier curves and ROC curves for both the training and testing cohorts. The results demonstrate that our risk score system effectively stratifies patients and predicts 1-, 3-, 5-year, and beyond survival rates with considerable precision. Furthermore, in the both training and testing cohorts, the Kaplan-Meier curves indicated that patients assigned to the high-risk score group exhibited a poorer prognosis (Fig. 3 A, 4 A) and the average AUC for 1-, 3-, and 5-year OS reached 0.75 (Fig. 3 B, 4 B). The apparent calibration curve closely approximated the ideal 45° line, suggesting that the observed probabilities aligned well with the predicted probabilities within the training cohort (Fig. 3 C). Following the establishment of our model, we conducted a comparative analysis with several well-documented prognostic models, namely SSIGN, Leibovich, GRANT, Karakiewicz Nomogram, Abel Nomogram, and the Mayo score system. Also, The time-dependent C-index for OS of our scoring system in the training cohorts demonstrated a statistically significant superiority compared to other established prognostic models (Fig. 3 D). Additionally, DCA of our model surpassed that of other prognostic models, particularly in distinguishing between patients with and without 5-year survival (Fig. 3 E). The time-dependent C-index for OS (Fig. 4 D), DCA (Fig. 4 E), and calibration curves (Fig. 4 C) further substantiated the robust superiority of our model in terms of prognostic accuracy and clinical utility within the testing cohorts. Discussion Non-metastatic ccRCC with VTT has been a challenge in urology. Untreated patients with renal cancer and VTT experience a brief natural history, with a median survival time of only 5 months and a 1-year CSS rate of 29%[ 14 ]. Moreover, the effectiveness of drug therapy is also very limited, a thorough examination of a significant patient cohort who underwent systemic targeted therapy for in situ RCC tumor thrombi revealed a restricted clinical effectiveness in diminishing the level of tumor thrombus[ 15 , 16 ]. Fortunately, Successful radical nephrectomy and thrombectomy offer significant palliative benefits to a subset of non-metastatic ccRCC patients with VTT, occasionally resulting in improved long-term survival rates. For non-metastatic RCC patients with VTT who underwent surgery, the median survival time was between 41 and 44 months, and the 5-year overall survival rate ranged between 39% and 60%, which were significantly higher than those observed in non-operated patients[ 17 – 20 ]. However, even non-metastatic renal cancer patients with VTT before surgery may still have disease progression due to the occurrence of distant metastases, and the three-year tumor recurrence rate is as high as 50%[ 21 ]. Therefore, numerous researchers have endeavored to analyze the prognostic factors in this subset of patients. However, there is a notable lack of consistency in the predictive modeling of non-metastatic ccRCC patients with VTT. And the majority of prognostic models have been derived from a broad RCC patient population, rather than tailored specifically for non-metastatic ccRCC patients with VTT[ 22 – 25 ]. Meanwhile, predictive models are typically developed using patient data and intended for practical application, may still have limitations such as suboptimal calibration and small sample sizes within specific subgroups of predictive variables[ 26 , 27 ]. Therefore, more accurate and robust models are needed. Simultaneously, a prognostic survival model was developed specifically for this patient cohort. The predictive model was evaluated using tools such as ROC curve, DCA curve, Time c-index, and Calibration curve, and it was found that the predictive model was able to accurately differentiate between individual patients in terms of disease prognosis. Furthermore, our model demonstrated superior accuracy in predicting survival outcomes for non-metastatic ccRCC patients with VTT compared to the common prognostic models for RCC patients[ 22 – 25 , 27 ]. This study identified: NLR, PT sarcomatoid differentiation, PT perirenal fat invasion, VTT grade as prognostic risk factors. NLR, acknowledged as a cost-effective systemic inflammatory marker, is readily derived from peripheral blood counts and has been reported to serve as a valuable prognostic indicator for a spectrum of solid cancers[ 28 ]. From a biological perspective, the neutrophil enumeration is purported to mirror the inflammatory microenvironment, which subsequently exhibits tumor-propagating effects, encompassing the survival and proliferation of cancer cells, angiogenesis, metastasis, and the suppression of adaptive immune responses[ 29 ]. Lymphocytes serve as potent inhibitors of cancer progression, and their presence, notably within the tumor microenvironment, is considered indicative of host immune responsiveness[ 30 ]. For RCC and RCC with VTT, NLR has been reported to function as an informative biomarker for prognosis[ 31 , 32 ]. A recent investigation has revealed that, among patients with non-metastatic RCC, those possessing NLR ≥ 1.7 demonstrated a statistically significantly higher recurrence-free survival rate compared to those with an NLR < 1.7 (p < 0.001)[ 31 ]. Shoichi Nagamoto et al. found that NLR is significant prognostic factors for CSS and OS in RCC with VTT[ 32 ]. Interestingly, another study has found that utilizing internal cut-point analysis, a preoperative NLR threshold of 2.27 was determined. Subsequent analysis revealed that the application of this specific cut-off value had a negligible impact on the prognosis[ 33 ]. The present study defined the cutoff value of the NLR to be 3.84 by optimal cut-off value determined through the application of the maximum Youden index and subsequent multivariate analysis pinpointed NLR as an independent predictor of the OS. Previous studies suggest that renal cancer patients with sarcomatoid differentiation have a poorer prognosis[ 34 , 35 ]. The majority of patients with sarcomatoid differentiation have been reported to develop tumor metastasis and die within 1 year compared to the common renal cancer pathologic type[ 34 ], and approximately 20% of patients have metastases at the time of initial diagnosis[ 36 , 37 ].Even for localized renal cell carcinoma, if combined with sarcomatoid differentiation within 5–26 months after surgery, nearly 80% of patients still experience tumor recurrence[ 35 ]. Therefore, regular review is intensified for non-metastatic ccRCC with VTT patients who have postoperative pathology suggestive of concomitant sarcomatoid differentiation. Perirenal fat invasion usually indicates a higher degree of tumor invasiveness. Although some studies have failed to establish perinephric fat invasion as an independent prognostic predictor[ 38 , 39 ], two multicenter, large-scale studies have demonstrated a significant correlation between perirenal fat invasion and a poor prognosis, making it an independent prognostic predictor for CSS in all patients[ 40 , 41 ]. A study has shown that perirenal fat invasion is an independent predictor of prognosis for non-metastatic RCC with VTT patients[ 6 ]. In our study, this viewpoint was confirmed and used as one of the prognostic indicators for patients. In this regard, a reclassification of the current pT3a stage to distinguish between perirenal fat invasion and renal VTT should be given consideration. In addition to PT, the clinicopathological features and prognostic potential of VTT were the focus of this study. The predictive role of VTT height levels in patients with RCC has been the subject of many studies, but remains controversial. Some researchers have suggested that patients with tumor thrombus extending into the inferior vena cava have a lower survival rate than those with renal vein thrombus alone[ 42 ]. However, the results of the study by Wagner et al. showed that the prognosis of patients with different grades of inferior vena cava thrombosis was not different. In addition to the height of VTT, the study deeply explores pathological characteristics of VTT[ 43 ]. In the cox regression analysis, VTT grade were identified as autonomous prognostic markers for OS. These variables demonstrated consistency across testing cohorts. Research has found that at the molecular level, prior investigations have elucidated both correlations and disparities between PTs and VTTs, as well as variations in their clonal evolution patterns[ 10 , 44 ]. Some scholars have even found that most metastases were sourced from VTT rather than PTs, suggesting that VTT served as a reservoir for metastases in the majority of ccRCC patients[ 14 ]. Our analysis revealed that VTT grade was a key point in the prognostic model. Therefore, incorporating VTT grade assessment into routine histopathology reporting has been deemed feasible and informative. Several limitations still remain in our study. First, as a retrospective study, it has the inherent design biases. Second, although the prediction model demonstrated good efficacy in internal validation in this study, external validation in a multicenter setting was still needed. Third, whether the four adjustable predictors identified in this study, especially the usage of VTT grade, can as a risk factor for patient prognosis still needs to be verified by prospective randomized trials. Conclusion In conclusion, we establish the contemporary model to predict OS for non-metastatic ccRCC with VTT patients after surgery. The prognostic model was developed using commonly accessible clinicopathologic features validated in prior research, independent of TNM staging, enabling versatile and broadly applicable clinical use. Furthermore, the model exhibited superior performance compared to existing prognostic models, offering robust predictive accuracy for non-metastatic ccRCC with VTT. Declarations Acknowledgements None. Author contribution Baohua Zhu, Ziyang Mo and Na Ta: Conceptualization, Investigation, Writing - Original Draft, Methodology, Literature review, Writing - Review & Editing, Visualization. Baohua Zhu and Wei Zhang: Investigation, Writing Original Draft, Methodology, Literature review, Writing - Review & Editing. Linhui Wang: Investigation, Writing - Review & Editing. All authors read and approved the final manuscript. Data availability statement The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Ethical approval The study was approved by the institutional review board of initiating center Changhai Hospital (ID Number: CHEC2021-191). Written informed consent was obtained from all patients. This study was conducted adhering to guidelines of Declaration of Helsinki for biomedical research and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD). Clinical trial number: not applicable Authorship The authors affirm their adherence to rigorous scientific research methodologies and acknowledge their substantial contributions to the drafting, revision, and final approval of the manuscript intended for submission to European Urology. Furthermore, the authors confirm that the manuscript has neither been previously published nor is it currently being considered for publication elsewhere. Competing interests All authors declare no conflicts of interest. References Bray, F., et al., Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024. 74 (3): p. 229-263. Ljungberg, B., et al., EAU guidelines on renal cell carcinoma: 2014 update. Eur Urol, 2015. 67 (5): p. 913-24. Abbasi, A., et al., Duplicated vena cava with tumor thrombus from renal cancer: use of venogram for safer operative planning. Urology, 2012. 79 (4): p. e57-8. Reese, A.C., J.M. Whitson, and M.V. Meng, Natural history of untreated renal cell carcinoma with venous tumor thrombus. Urol Oncol, 2013. 31 (7): p. 1305-9. Ljungberg, B., et al., European Association of Urology Guidelines on Renal Cell Carcinoma: The 2022 Update. Eur Urol, 2022. 82 (4): p. 399-410. 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Eur Urol, 2007. 52 (3): p. 658-62. Karakiewicz, P.I., et al., Multi-institutional validation of a new renal cancer-specific survival nomogram. J Clin Oncol, 2007. 25 (11): p. 1316-22. Buti, S., et al., Validation of a new prognostic model to easily predict outcome in renal cell carcinoma: the GRANT score applied to the ASSURE trial population. Ann Oncol, 2017. 28 (11): p. 2747-2753. Frank, I., et al., An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: the SSIGN score. J Urol, 2002. 168 (6): p. 2395-400. Leibovich, B.C., et al., Prediction of progression after radical nephrectomy for patients with clear cell renal cell carcinoma: a stratification tool for prospective clinical trials. Cancer, 2003. 97 (7): p. 1663-71. Wang, K., et al., T.H.R.O.B.V.S. Score - A Comprehensive Model to Predict the Surgical Complexity of Renal Cell Carcinoma With Tumor Thrombus. Front Oncol, 2022. 12 : p. 900550. Abel, E.J., et al., Predictive Nomogram for Recurrence following Surgery for Nonmetastatic Renal Cell Cancer with Tumor Thrombus. J Urol, 2017. 198 (4): p. 810-816. Templeton, A.J., et al., Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. J Natl Cancer Inst, 2014. 106 (6): p. dju124. Diakos, C.I., et al., Cancer-related inflammation and treatment effectiveness. Lancet Oncol, 2014. 15 (11): p. e493-503. Gooden, M.J., et al., The prognostic influence of tumour-infiltrating lymphocytes in cancer: a systematic review with meta-analysis. Br J Cancer, 2011. 105 (1): p. 93-103. Wen, R.M., et al., Preoperative Neutrophil to Lymphocyte Ratio as a Prognostic Factor in Patients with Non-metastatic Renal Cell Carcinoma. Asian Pac J Cancer Prev, 2015. 16 (9): p. 3703-8. Nagamoto, S., et al., Impact of the neutrophil-to-lymphocyte ratio as a surgical prognostic factor in renal cell carcinoma with inferior-vena-cava tumor thrombus. Asian J Surg, 2023. 46 (1): p. 192-200. Pichler, M., et al., Validation of the pre-treatment neutrophil-lymphocyte ratio as a prognostic factor in a large European cohort of renal cell carcinoma patients. Br J Cancer, 2013. 108 (4): p. 901-7. Blum, K.A., et al., Sarcomatoid renal cell carcinoma: biology, natural history and management. Nat Rev Urol, 2020. 17 (12): p. 659-678. Merrill, M.M., et al., Clinically nonmetastatic renal cell carcinoma with sarcomatoid dedifferentiation: Natural history and outcomes after surgical resection with curative intent. Urol Oncol, 2015. 33 (4): p. 166.e21-9. Billis, A., Sarcomatoid renal cell carcinoma: an examination of underlying histologic subtype and an analysis of associations with patient outcome. Int Braz J Urol, 2004. 30 (4): p. 347-8. Keskin, S.K., et al., Outcomes of Patients with Renal Cell Carcinoma and Sarcomatoid Dedifferentiation Treated with Nephrectomy and Systemic Therapies: Comparison between the Cytokine and Targeted Therapy Eras. J Urol, 2017. 198 (3): p. 530-537. Hirono, M., et al., Impacts of clinicopathologic and operative factors on short-term and long-term survival in renal cell carcinoma with venous tumor thrombus extension: a multi-institutional retrospective study in Japan. BMC Cancer, 2013. 13 : p. 447. Whitson, J.M., A.C. Reese, and M.V. Meng, Population based analysis of survival in patients with renal cell carcinoma and venous tumor thrombus. Urol Oncol, 2013. 31 (2): p. 259-63. Martínez-Salamanca, J.I., et al., Prognostic impact of the 2009 UICC/AJCC TNM staging system for renal cell carcinoma with venous extension. Eur Urol, 2011. 59 (1): p. 120-7. Tilki, D., et al., Impact of histologic subtype on cancer-specific survival in patients with renal cell carcinoma and tumor thrombus. Eur Urol, 2014. 66 (3): p. 577-83. Blute, M.L., et al., The Mayo Clinic experience with surgical management, complications and outcome for patients with renal cell carcinoma and venous tumour thrombus. BJU Int, 2004. 94 (1): p. 33-41. Wagner, B., et al., Prognostic value of renal vein and inferior vena cava involvement in renal cell carcinoma. Eur Urol, 2009. 55 (2): p. 452-9. Kim, K., et al., Determinants of renal cell carcinoma invasion and metastatic competence. Nat Commun, 2021. 12 (1): p. 5760. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5636265","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":391477547,"identity":"f9534cb3-fe52-4c2b-b0c6-0fa0b9368ed3","order_by":0,"name":"Baohua Zhu","email":"","orcid":"","institution":"Changhai Hospital, Naval Medical University (Second Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Baohua","middleName":"","lastName":"Zhu","suffix":""},{"id":391477548,"identity":"bb784063-923c-415f-bc9b-72e2d48fd83a","order_by":1,"name":"Ziyang Mo","email":"","orcid":"","institution":"Changhai Hospital, Naval Medical University (Second Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Ziyang","middleName":"","lastName":"Mo","suffix":""},{"id":391477549,"identity":"eebca579-9331-4d98-b6ad-508f503457d1","order_by":2,"name":"Na Ta","email":"","orcid":"","institution":"Changhai Hospital, Naval Medical University (Second Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Ta","suffix":""},{"id":391477550,"identity":"fd2ccd9d-cb19-41ee-b4a3-58918d86728f","order_by":3,"name":"Linhui Wang","email":"","orcid":"","institution":"Changhai Hospital, Naval Medical University (Second Military Medical University)","correspondingAuthor":false,"prefix":"","firstName":"Linhui","middleName":"","lastName":"Wang","suffix":""},{"id":391477551,"identity":"b4f1f0ae-e2a0-47a4-a2f4-e32e206a7cd7","order_by":4,"name":"Wei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACCSBOADHYGxsffiBNC8/hZmMJorVAGOltAjzE6JCf3WP44MGfbdEGNx+2AfXbyek2ENDCOOeMsUFi2+3cDbcT2x4UMCQbmx0goIVZInebRGIDWEu7gQTDgcRthLSwSeRu/5HwB6jl5sE2CR5itPAAbWFIYANqucFIpBYJifzPEiC/zDyTCAxkAyL8Ij8jLfHjD6DD+o4ff/jwQ4WdHEEtaMCANOWjYBSMglEwCnAAAPtNSFQVJbiaAAAAAElFTkSuQmCC","orcid":"","institution":"Changhai Hospital, Naval Medical University (Second Military Medical University)","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-12-13 07:23:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5636265/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5636265/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72288347,"identity":"a73fc8d8-4dac-455e-99e6-3e2f4e9f58ba","added_by":"auto","created_at":"2024-12-24 17:16:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":231006,"visible":true,"origin":"","legend":"\u003cp\u003eThe schematic of VTT grade.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5636265/v1/14249b00a2a303549797c9ee.png"},{"id":72288349,"identity":"7f129cb0-d2aa-46b9-b160-84fb3ecc53bd","added_by":"auto","created_at":"2024-12-24 17:16:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25594,"visible":true,"origin":"","legend":"\u003cp\u003eThe nomogram for predicting 1-, 3-, and 5-year OS probabilities.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5636265/v1/ff544b378f3639537d27981f.png"},{"id":72288348,"identity":"4328a9dd-36ef-4f68-b219-9f2234e7f969","added_by":"auto","created_at":"2024-12-24 17:16:26","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":101730,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for OS in the training cohorts (A). Time-ROC curve analysis and AUC values of nomogram in the training cohorts (B). Calibration plots for prediction of prognosis of risk score system in training cohorts (C). Time c-index curve and decision curves comparing the prognosis accuracy of risk score system with other models in training cohorts (D, E).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5636265/v1/f21d0fea73d5e497a5822968.png"},{"id":72288353,"identity":"d1e78ac5-3e70-4dc1-b4e7-42a51e1a5119","added_by":"auto","created_at":"2024-12-24 17:16:26","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":104285,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan-Meier curves for OS in the testing cohorts (A). Time-ROC curve analysis and AUC values of nomogram in the testing cohorts (B). Calibration plots for prediction of prognosis of risk score system in testing cohorts (C). Time c-index curve and decision curves comparing the prognosis accuracy of risk score system with other models in testing cohorts (D, E).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5636265/v1/84209cc2f64ab800d46def62.png"},{"id":90006597,"identity":"93f495a9-a580-4ff4-9d7c-45cae8d1cb16","added_by":"auto","created_at":"2025-08-27 09:39:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1666079,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5636265/v1/e111f0cb-092a-4f99-895f-d7a147d2789a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Developing and validating an innovative risk stratification model for non-metastatic clear cell renal cell carcinoma patients with venous tumor thrombus","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClear cell Renal cell carcinoma (ccRCC) is one of the most common malignant tumors in the urinary system[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. As the detection rate of early-stage ccRCC rises, the 5-year survival rate for these patients has reached 93%[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, locally advanced or metastatic renal cell carcinoma (RCC) is frequently associated with venous tumor thrombus (VTT), affecting approximately 4\u0026ndash;10% of RCC patients[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Historically, venous invasion by ccRCC has been considered a marker of poor prognosis[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Surgical intervention is the main treatment for these patients, and some of them have achieved long-term survival after radical nephrectomy with thrombectomy[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Regarding the prognosis of patients with ccRCC and VTT and its related factors, the descriptions of previous studies vary greatly due to the limited sample size, and the overall survival time of patients varies from a few months to more than 10 years[\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditionally, thrombus has been considered merely an extension of the primary tumor (PT) into the vascular space, with the assumption of similar molecular and histological characteristics[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, Wang and Shi et al. independently conducted transcriptome sequencing and single-cell RNA sequencing analysis on the normal-tumor-cancer thrombosis triad associated with ccRCC. Their findings revealed that patients with VTT exhibit distinct molecular characteristics compared to those without VTT and the tumor microenvironment between these two patient groups was notably disparate[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, The TRACERx Renal project has shed light on the potential role of VTT as a source of metastatic dissemination in patients with ccRCC[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Furthermore, our prior research also indicates that VTT acts as a reservoir for metastases in the majority of ccRCC patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This study aimed to identify independent prognostic factors for ccRCC with VTT by collecting a series of clinical sample data, including VTT grade, and to develop a nomogram for predicting the survival time of clinical patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and Patients selection\u003c/h2\u003e \u003cp\u003eFrom January 2011 to October 2024, a cohort of patients diagnosed as ccRCC with VTT was assembled at Changhai Hospital. The inclusion criteria were as follows: (a) patients with non-metastatic ccRCC featuring VTT graded 0 to IV; (b) patients who underwent radical nephrectomy (RN) with concurrent VTT extraction, without VTT detachment or perioperative mortality; (c) histologically confirmed ccRCC post-surgery; and (d) patients who could be followed up during the study duration. Ultimately, 124 patients with ccRCC and VTT were enrolled. Subsequently, these patients were randomly stratified into a training cohort (n\u0026thinsp;=\u0026thinsp;86) and a testing cohort (n\u0026thinsp;=\u0026thinsp;38) in a 7:3 ratio.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003eThe following perioperative clinical data were obtained from the electronic medical records of our institution, including age at surgery, gender, symptoms, body mass index, several preoperative blood routine examination results (Creatinine, Hemoglobin, Blood platelet and Neutrophil to Lymphocyte Ratio (NLR)), VTT Mayo classification, PT tumor size and grade, perirenal fat invasion of PT, sarcomatoid differentiation and necrosis in PT etc. All patients underwent comprehensive preoperative evaluations, encompassing clinical assessments, computed tomography (CT) scans, and/or magnetic resonance imaging (MRI) studies, to establish a definitive diagnosis. The principal outcome was overall survival (OS), measured as the duration from the date of radical surgery to the date of death due to any cause or the censoring date at the conclusion of follow-up, which was finalized in October 2024.\u003c/p\u003e\n\u003ch3\u003eVTT Pathological variables\u003c/h3\u003e\n\u003cp\u003eWe additionally assessed the World Health Organization/International Society of Urological Pathology (WHO/ISUP) grading system in VTT specimens. The VTT grade was assigned based on the highest grade identified on any slide, regardless of whether it was focal, consistent with the approach used for PT grade. In detail, ISUP grade I was characterized by the absence or subtle presence of nucleoli at \u0026times;400 magnification. ISUP grade II was defined by nucleoli that were clearly visible at \u0026times;400 magnification but not discernible at \u0026times;100 magnification. For ISUP grade III, nucleoli were distinctly apparent at \u0026times;100 magnification. ISUP grade IV was distinguished by the presence of marked nuclear pleomorphism, tumor giant cells, and/or the presence of rhabdoid and/or sarcomatoid differentiation. A schematic of the above mentioned VTT grade is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eContinuous variables are reported as means and standard deviations, while categorical variables are presented as percentages. Differences among groups for continuous data were examined using ANOVA or the Kruskal\u0026ndash;Walli\u0026rsquo;s test, depending on the data distribution. Chi-square and Cochran\u0026ndash;Mantel\u0026ndash;Haenszel tests assessed differences in categorical and ordinal data, respectively. The optimal cut-off value for NLR was determined using Youden\u0026rsquo;s index. Univariate and multivariate analyses in the training corht were conducted using Cox proportional hazards models to identify independent prognostic factors. A nomogram for predicting patient OS was constructed using the rms package in R. Kaplan-Meier survival curves were generated to compare OS between different groups. Model accuracy was quantified using time-dependent receiver operating characteristic (ROC) curves and areas under the curve (AUC), with model discrimination assessed through the concordance index (C-index) and AUC values. Decision curve analysis (DCA) was performed to explore the clinical utility of the predictive model, identifying net benefits across various probability thresholds. Calibration plots were created to evaluate the accuracy of predicted outcomes against observed events. All p-values were two-tailed, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant. Statistical analyses were conducted using R software (version 4.1.3).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n \u003cp\u003eThe clinical features of the patients comprising both the training and testing cohorts are concisely summarized in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Notably, no statistically significant disparities were observed in the clinical and pathological attributes between the training and testing cohorts. Among the 124 study participants, 33 (26.61%) were female, whereas 91 (73.39%) were male. Those who presented with no symptoms, local symptoms and systemic symptoms were 37 (29.84%), 69 (55.65%) and 18 (55.65%) patients. Regarding pathological T staging, 46 cases (37.1%) were classified as T3a, 57 (45.97%) as T3b, 7 (5.65%) as T3c, and 14 (11.29%) as T4. In terms of the Mayo classification, 43 patients (34.68%) were categorized as stage 0, 18 (14.52%) as stage I, 32 (25.81%) as stage II, 24 (19.35%) as stage III, and 7 (5.65%) as stage IV. Nearly half (48.39%) of all patients had PT perirenal fat invasion. The maximum diameter of the tumor ranged from 2 to 20 cm with a mean size of 85 mm. VTT grade (WHO/ISUP grade) was 2 in 39 (31.5%), 3 in 52 (42%), and 4 in 33 (26.5%). PT grade (WHO/ISUP grade) was 2 in 39 (31.45%) cases, 3 in 54 (43.55%), 4 in 31 (25%). Based on the optimal cut-off value derived using the maximum Youden index, a threshold of 3.84 was established for NLR. The median follow-up was 35 months.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eBaseline data of 124 included patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLevel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTrain\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (26.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10 (26.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (26.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91 (73.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28 (73.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63 (73.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (12.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57 (12.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (12.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.32 (3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.13 (3.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.40 (3.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSymptoms (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (29.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (28.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (30.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9621\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (55.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21 (55.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (55.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystemic symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (14.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASA_score (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (4.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (4.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1844\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89 (71.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27 (71.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62 (72.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (21.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (18.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (23.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayor (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43 (34.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (39.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28 (32.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (14.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (25.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8 (21.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (27.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (19.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7 (18.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (19.77)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (5.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal_tumour (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (45.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (43.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (54.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (56.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative LDH (U/L) (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209.69 (117.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e200.95 (87.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e213.56 (128.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative Creatinine (\u0026micro;mol/L) (mean\u003c/p\u003e\n \u003cp\u003e(SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.48 (24.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82.92 (19.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.93 (25.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative Neutrophils 10\u003csup\u003e9\u003c/sup\u003e/L (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.94 (2.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.75 (1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.03 (2.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5417\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative lymphocyte 10\u003csup\u003e9\u003c/sup\u003e/L (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.36 (0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36 (0.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.9242\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative Blood platelet (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.24 (110.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e252.24 (88.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e247.92 (118.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8415\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.43 (4.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.04 (6.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.16 (3.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3436\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative Hemoglobin (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115.23 (24.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118.03 (24.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e113.99 (25.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOperation Time (hours) (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.35 (1.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.16 (1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.434 (1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood_loss (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1404(1309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1246.05 (1166)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1474(1368)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3736\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT_grade (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (31.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (34.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26 (30.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54 (43.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14 (36.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (46.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (28.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (23.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVTT_GRADE (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (31.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12 (31.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e27 (31.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (42.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (34.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (45.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33 (26.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13 (34.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (23.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClavien Grading, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53 (42.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (40.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.7791\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67 (54.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19 (50.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48 (55.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (3.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1 (2.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT Perirenal fat invasion (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (51.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20 (52.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44 (51.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (48.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18 (47.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42 (48.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT Sarcomatoid differentiation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105 (84.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32 (84.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e73 (84.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (15.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6 (15.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13 (15.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT necrosis (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (41.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (39.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37 (43.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.8635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72 (58.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (60.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (56.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epT stage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e46 (37.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (42.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (34.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57 (45.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (42.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (47.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (5.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4 (10.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epN (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e110 (88.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36 (94.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (86.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (11.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2 (5.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (13.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume (mm) (mean (SD))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85.815 (36.460)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.842 (40.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.686 (34.746)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.6905\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOS (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56 (45.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15 (39.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (47.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.5155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e68 (54.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23 (60.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e45 (52.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003eModel development\u003c/h3\u003e\n\u003cp\u003eIn the training cohort, the above clinical indicators were performed in the univariate analysis. Subsequent multivariate analysis pinpointed PT Sarcomatoid differentiation (p\u0026thinsp;=\u0026thinsp;0.034), PT perirenal fat invasion (p\u0026thinsp;=\u0026thinsp;0.046), VTT grade (p\u0026thinsp;=\u0026thinsp;0.045) and NLR (p\u0026thinsp;=\u0026thinsp;0.007) as an independent predictor of OS, as detailed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Leveraging these four candidate predictors, a nomogram (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) was constructed to estimate the likelihood of predicting the survival time of clinical patients.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate and multivariate Cox regression analysis in the Training cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal(N)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eMultivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHazard ratio (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.721 (0.387\u0026ndash;1.342)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.999 (0.976\u0026ndash;1.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.958 (0.873\u0026ndash;1.050)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSymptomatic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocal symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984 (0.513\u0026ndash;1.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSystemic symptoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.733 (0.263\u0026ndash;2.039)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASA_score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.988 (0.235\u0026ndash;4.164)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.383 (0.305\u0026ndash;6.261)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.626 (0.666\u0026ndash;3.968)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.401 (0.665\u0026ndash;2.955)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.922 (0.378\u0026ndash;2.247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.858\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅣ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.898 (0.206\u0026ndash;3.921)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal tumour\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeft\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.739 (0.411\u0026ndash;1.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative LDH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000 (0.998\u0026ndash;1.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative Creatinine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.002 (0.990\u0026ndash;1.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative blood platelet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.001 (0.999\u0026ndash;1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026ge;\u0026thinsp;3.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.068 (2.149\u0026ndash;7.700)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.703 (1.307\u0026ndash;5.590)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePreoperative Hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.998 (0.985\u0026ndash;1.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOperation time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.061 (0.850\u0026ndash;1.325)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlood loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000 (1.000\u0026ndash;1.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.557\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.391 (0.648\u0026ndash;2.982)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.333 (1.046\u0026ndash;5.204)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVTT_GRADE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.018 (0.900\u0026ndash;4.525)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.232 (0.514\u0026ndash;2.951)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.993 (2.129\u0026ndash;11.712)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.517 (1.020\u0026ndash;6.213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eClavien Grading\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅠ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅡ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.725 (0.395\u0026ndash;1.330)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eⅢ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.417 (0.713\u0026ndash;8.190)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT Perirenal fat invasion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.152 (2.090\u0026ndash;8.250)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.162 (1.015\u0026ndash;4.606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT Sarcomatoid differentiation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.421 (1.700\u0026ndash;6.883)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.230 (1.063\u0026ndash;4.676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT necrosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.856 (0.476\u0026ndash;1.538)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epT stage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.328 (0.693\u0026ndash;2.546)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT3c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.159 (0.153\u0026ndash;8.803)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eT4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.039 (0.408\u0026ndash;2.642)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.700 (0.753\u0026ndash;3.834)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004 (0.997\u0026ndash;1.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch3\u003eModel performance and validation\u003c/h3\u003e\n\u003cp\u003eTo evaluate the precision of the aforementioned risk score system, we performed cross-validation, yielding Kaplan-Meier curves and ROC curves for both the training and testing cohorts. The results demonstrate that our risk score system effectively stratifies patients and predicts 1-, 3-, 5-year, and beyond survival rates with considerable precision. Furthermore, in the both training and testing cohorts, the Kaplan-Meier curves indicated that patients assigned to the high-risk score group exhibited a poorer prognosis (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) and the average AUC for 1-, 3-, and 5-year OS reached 0.75 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). The apparent calibration curve closely approximated the ideal 45\u0026deg; line, suggesting that the observed probabilities aligned well with the predicted probabilities within the training cohort (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC). Following the establishment of our model, we conducted a comparative analysis with several well-documented prognostic models, namely SSIGN, Leibovich, GRANT, Karakiewicz Nomogram, Abel Nomogram, and the Mayo score system. Also, The time-dependent C-index for OS of our scoring system in the training cohorts demonstrated a statistically significant superiority compared to other established prognostic models (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eD). Additionally, DCA of our model surpassed that of other prognostic models, particularly in distinguishing between patients with and without 5-year survival (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eE). The time-dependent C-index for OS (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD), DCA (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eE), and calibration curves (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC) further substantiated the robust superiority of our model in terms of prognostic accuracy and clinical utility within the testing cohorts.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eNon-metastatic ccRCC with VTT has been a challenge in urology. Untreated patients with renal cancer and VTT experience a brief natural history, with a median survival time of only 5 months and a 1-year CSS rate of 29%[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Moreover, the effectiveness of drug therapy is also very limited, a thorough examination of a significant patient cohort who underwent systemic targeted therapy for in situ RCC tumor thrombi revealed a restricted clinical effectiveness in diminishing the level of tumor thrombus[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Fortunately, Successful radical nephrectomy and thrombectomy offer significant palliative benefits to a subset of non-metastatic ccRCC patients with VTT, occasionally resulting in improved long-term survival rates. For non-metastatic RCC patients with VTT who underwent surgery, the median survival time was between 41 and 44 months, and the 5-year overall survival rate ranged between 39% and 60%, which were significantly higher than those observed in non-operated patients[\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, even non-metastatic renal cancer patients with VTT before surgery may still have disease progression due to the occurrence of distant metastases, and the three-year tumor recurrence rate is as high as 50%[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, numerous researchers have endeavored to analyze the prognostic factors in this subset of patients. However, there is a notable lack of consistency in the predictive modeling of non-metastatic ccRCC patients with VTT. And the majority of prognostic models have been derived from a broad RCC patient population, rather than tailored specifically for non-metastatic ccRCC patients with VTT[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Meanwhile, predictive models are typically developed using patient data and intended for practical application, may still have limitations such as suboptimal calibration and small sample sizes within specific subgroups of predictive variables[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Therefore, more accurate and robust models are needed.\u003c/p\u003e \u003cp\u003eSimultaneously, a prognostic survival model was developed specifically for this patient cohort. The predictive model was evaluated using tools such as ROC curve, DCA curve, Time c-index, and Calibration curve, and it was found that the predictive model was able to accurately differentiate between individual patients in terms of disease prognosis. Furthermore, our model demonstrated superior accuracy in predicting survival outcomes for non-metastatic ccRCC patients with VTT compared to the common prognostic models for RCC patients[\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study identified: NLR, PT sarcomatoid differentiation, PT perirenal fat invasion, VTT grade as prognostic risk factors. NLR, acknowledged as a cost-effective systemic inflammatory marker, is readily derived from peripheral blood counts and has been reported to serve as a valuable prognostic indicator for a spectrum of solid cancers[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. From a biological perspective, the neutrophil enumeration is purported to mirror the inflammatory microenvironment, which subsequently exhibits tumor-propagating effects, encompassing the survival and proliferation of cancer cells, angiogenesis, metastasis, and the suppression of adaptive immune responses[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Lymphocytes serve as potent inhibitors of cancer progression, and their presence, notably within the tumor microenvironment, is considered indicative of host immune responsiveness[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. For RCC and RCC with VTT, NLR has been reported to function as an informative biomarker for prognosis[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. A recent investigation has revealed that, among patients with non-metastatic RCC, those possessing NLR\u0026thinsp;\u0026ge;\u0026thinsp;1.7 demonstrated a statistically significantly higher recurrence-free survival rate compared to those with an NLR\u0026thinsp;\u0026lt;\u0026thinsp;1.7 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Shoichi Nagamoto et al. found that NLR is significant prognostic factors for CSS and OS in RCC with VTT[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Interestingly, another study has found that utilizing internal cut-point analysis, a preoperative NLR threshold of 2.27 was determined. Subsequent analysis revealed that the application of this specific cut-off value had a negligible impact on the prognosis[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The present study defined the cutoff value of the NLR to be 3.84 by optimal cut-off value determined through the application of the maximum Youden index and subsequent multivariate analysis pinpointed NLR as an independent predictor of the OS.\u003c/p\u003e \u003cp\u003ePrevious studies suggest that renal cancer patients with sarcomatoid differentiation have a poorer prognosis[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The majority of patients with sarcomatoid differentiation have been reported to develop tumor metastasis and die within 1 year compared to the common renal cancer pathologic type[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and approximately 20% of patients have metastases at the time of initial diagnosis[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].Even for localized renal cell carcinoma, if combined with sarcomatoid differentiation within 5\u0026ndash;26 months after surgery, nearly 80% of patients still experience tumor recurrence[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Therefore, regular review is intensified for non-metastatic ccRCC with VTT patients who have postoperative pathology suggestive of concomitant sarcomatoid differentiation.\u003c/p\u003e \u003cp\u003ePerirenal fat invasion usually indicates a higher degree of tumor invasiveness. Although some studies have failed to establish perinephric fat invasion as an independent prognostic predictor[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], two multicenter, large-scale studies have demonstrated a significant correlation between perirenal fat invasion and a poor prognosis, making it an independent prognostic predictor for CSS in all patients[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A study has shown that perirenal fat invasion is an independent predictor of prognosis for non-metastatic RCC with VTT patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In our study, this viewpoint was confirmed and used as one of the prognostic indicators for patients. In this regard, a reclassification of the current pT3a stage to distinguish between perirenal fat invasion and renal VTT should be given consideration.\u003c/p\u003e \u003cp\u003eIn addition to PT, the clinicopathological features and prognostic potential of VTT were the focus of this study. The predictive role of VTT height levels in patients with RCC has been the subject of many studies, but remains controversial. Some researchers have suggested that patients with tumor thrombus extending into the inferior vena cava have a lower survival rate than those with renal vein thrombus alone[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. However, the results of the study by Wagner et al. showed that the prognosis of patients with different grades of inferior vena cava thrombosis was not different. In addition to the height of VTT, the study deeply explores pathological characteristics of VTT[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In the cox regression analysis, VTT grade were identified as autonomous prognostic markers for OS. These variables demonstrated consistency across testing cohorts. Research has found that at the molecular level, prior investigations have elucidated both correlations and disparities between PTs and VTTs, as well as variations in their clonal evolution patterns[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Some scholars have even found that most metastases were sourced from VTT rather than PTs, suggesting that VTT served as a reservoir for metastases in the majority of ccRCC patients[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Our analysis revealed that VTT grade was a key point in the prognostic model. Therefore, incorporating VTT grade assessment into routine histopathology reporting has been deemed feasible and informative.\u003c/p\u003e \u003cp\u003eSeveral limitations still remain in our study. First, as a retrospective study, it has the inherent design biases. Second, although the prediction model demonstrated good efficacy in internal validation in this study, external validation in a multicenter setting was still needed. Third, whether the four adjustable predictors identified in this study, especially the usage of VTT grade, can as a risk factor for patient prognosis still needs to be verified by prospective randomized trials.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, we establish the contemporary model to predict OS for non-metastatic ccRCC with VTT patients after surgery. The prognostic model was developed using commonly accessible clinicopathologic features validated in prior research, independent of TNM staging, enabling versatile and broadly applicable clinical use. Furthermore, the model exhibited superior performance compared to existing prognostic models, offering robust predictive accuracy for non-metastatic ccRCC with VTT.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaohua Zhu, Ziyang Mo and Na Ta: Conceptualization, Investigation, Writing - Original Draft, Methodology, Literature review, Writing - Review \u0026amp; Editing, Visualization. Baohua Zhu and Wei Zhang: Investigation, Writing Original Draft, Methodology, Literature review, Writing - Review \u0026amp; Editing. Linhui Wang: Investigation, Writing - Review \u0026amp; Editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional review board of initiating center Changhai Hospital (ID Number: CHEC2021-191). Written informed consent was obtained from all patients. This study was conducted adhering to guidelines of Declaration of Helsinki for biomedical research and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors affirm their adherence to rigorous scientific research methodologies and acknowledge their substantial contributions to the drafting, revision, and final approval of the manuscript intended for submission to European Urology. Furthermore, the authors confirm that the manuscript has neither been previously published nor is it currently being considered for publication elsewhere.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBray, F., et al., \u003cem\u003eGlobal cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.\u003c/em\u003e CA Cancer J Clin, 2024. \u003cstrong\u003e74\u003c/strong\u003e(3): p. 229-263.\u003c/li\u003e\n\u003cli\u003eLjungberg, B., et al., \u003cem\u003eEAU guidelines on renal cell carcinoma: 2014 update.\u003c/em\u003e Eur Urol, 2015. \u003cstrong\u003e67\u003c/strong\u003e(5): p. 913-24.\u003c/li\u003e\n\u003cli\u003eAbbasi, A., et al., \u003cem\u003eDuplicated vena cava with tumor thrombus from renal cancer: use of venogram for safer operative planning.\u003c/em\u003e Urology, 2012. \u003cstrong\u003e79\u003c/strong\u003e(4): p. e57-8.\u003c/li\u003e\n\u003cli\u003eReese, A.C., J.M. 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Our objective was to develop and validate a novel risk scoring system for the patients to predict the survival time and probability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Data were gathered from non-metastatic ccRCC patients with VTT treated between 2011 and 2024. Participants were retrospectively assigned in a 7:3 ratio to training and testing cohorts. We evaluated and quantified clinicopathological characteristics of the primary tumor (PT) and VTT, constructing multivariable models to predict overall survival (OS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study included 124 patients, with a median follow-up of 35 months. We developed a risk score system based on PT Sarcomatoid differentiation (p = 0.034), PT perirenal fat invasion (p = 0.046), VTT grade (p = 0.045) and Neutrophil to Lymphocyte Ratio(NLR) (p = 0.007). This system accurately identified a high-risk cohort exhibiting adverse outcomes among non-metastatic ccRCC patients with VTT, findings consistent in the testing group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Our study presents a nomogram integrating clinicopathological features—PT Sarcomatoid differentiation, PT perirenal fat invasion, VTT grade and NLR—facilitating risk stratification and enhancing the precision in managing non-metastatic ccRCC patients with VTT.\u003c/p\u003e","manuscriptTitle":"Developing and validating an innovative risk stratification model for non-metastatic clear cell renal cell carcinoma patients with venous tumor thrombus","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 17:16:21","doi":"10.21203/rs.3.rs-5636265/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":"ce73bfeb-f4d2-493b-abc1-aa1bbb3312d7","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-27T09:38:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-24 17:16:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5636265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5636265","identity":"rs-5636265","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}