Multi-institutional analysis of clinical risk factors for disease recurrence in locally advanced renal cell carcinoma

Multi-institutional analysis of clinical risk factors for disease recurrence in locally advanced renal cell carcinoma | 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 Multi-institutional analysis of clinical risk factors for disease recurrence in locally advanced renal cell carcinoma Ozan Bozkurt, Oktay Ucer, Ender Ozden, Sumer Baltacı, Bulent Akdogan, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7682253/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 Introduction: This study aimed to identify clinical risk factors associated with disease recurrence in patients with locally advanced renal cell carcinoma (RCC). Materials and Methods: Patients diagnosed with locally advanced RCC (T3–T4N0M0 or TxN1M0) from the Turkish Urooncology Association Kidney Cancer Database were included. Demographic and clinical variables (age, sex, body mass index [BMI], performance status, Charlson Comorbidity Index [CCI], symptomatic status, hematologic parameters) and tumor-related characteristics (stage, grade, size, vascular invasion, necrosis, surgical margin status, and lymph node involvement) were analyzed. Results : A total of 749 patients were evaluated, including 216 women and 533 men. The mean age was 59.9 ± 11.6 years (range: 18–86), and the median follow-up was 25.9 ± 31 months (range: 1–180). Disease recurrence occurred in 192 patients (25.6%), with a mean time to recurrence of 9.4 ± 17.8 months (range: 1–84). On univariate and multivariate analyses, Fuhrman grade 3–4 (OR = 0.169, 95% CI: 0.032–0.899, p = 0.037) and platelet count (OR = 1.012, 95% CI: 1.005–1.020, p = 0.001) emerged as independent predictors of recurrence. ROC curve analysis identified a platelet count cut-off of 363,000/µL (AUC = 0.642, 95% CI: 0.600–0.683, p < 0.001; sensitivity: 38.8%, specificity: 86.2%). Conclusions: High Fuhrman grade (3–4) and elevated platelet count were identified as independent risk factors for disease recurrence in patients with locally advanced RCC. These findings may contribute to risk stratification and help guide follow-up strategies and adjuvant treatment considerations in both clinical practice and clinical trial design. Urology & Nephrology Renal cell carcinoma Fuhrmann grade Thrombocytosis Prognosis Figures Figure 1 Figure 2 Introduction Renal cell carcinoma (RCC) represents a significant global health burden, accounting for approximately 2.2% of all malignancies, with an estimated 400,000 new diagnoses and 175,000 deaths annually (1,2). At presentation, 70–75% of cases are confined to the kidney, while 15% present with locoregional disease. The 5-year survival rate decreases markedly from 75–80% in stage I–II disease to around 50% in stage III disease in large population-based series (3). Despite curative-intent surgery, recurrence rates can reach up to 80% in patients with high-risk localized or locoregional disease (4). Several prognostic models have been developed to identify patients at higher risk for recurrence. The UCLA Integrated Staging System (UISS), the Leibovich score, the Stage, Size, Grade, and Necrosis (SSIGN) system, and the Karakiewicz nomogram primarily incorporate tumor-related variables and have frequently been applied in the selection of patients for adjuvant clinical trials (5). Currently, active surveillance with close follow-up remains the standard of care for high-risk patients, given the limited efficacy demonstrated in previous adjuvant treatment studies. Accurately selecting patients who may truly benefit from perioperative therapy remains a critical challenge, as inappropriate patient selection is considered one of the main reasons for the failure of prior adjuvant trials. To address this limitation, gene expression signatures and molecular assays are being actively investigated as potential tools for individualized risk stratification (6). However, their integration into routine clinical practice is hampered by issues of cost, accessibility, and lack of standardization. In this context, the present study aimed to evaluate readily available clinical and tumor-related parameters as predictors of recurrence in a relatively homogeneous cohort of patients with locally advanced RCC. Materials and Methods After local ethical approval, all patients with locally advanced renal cell carcinoma (T3-T4N0M0, TxN1M0) in the Turkish Urooncology Association – Renal Tumors Database were included except those receiving adjuvant therapy. Patients with no follow-up data were excluded. All procedures were in accordance with the 1964 Helsinki declaration. All patients underwent standardized radical nephrectomy and/or lymphadenectomy with open or laparoscopic approach. The decision to perform lymphadenectomy has been made preoperatively with imaging findings or intraoperatively. Tumors invading perinephric tissues, pelvicalyceal system, renal vein, or segmental branches were defined as PT3 and PT4 if the tumor extended beyond Gerota’s fascia according to TNM classification. N1 was defined as the metastatic spread of the disease to the regional lymph nodes. Follow-up protocols were in accordance with the latest guidelines on renal cell carcinoma ( 7 ). All relapses including regional lymph nodes and metastasis to the lungs, bones, brain, and liver were defined as disease recurrence. Patients were divided into two groups according to disease recurrence status group 1 (no recurrence) and group 2 (recurrent disease). Demographic and clinical factors in concordance with the existing literature; age, sex, body mass index (BMI), performance status, Charlson comorbity index (CCI), symptomatic status, hematologic parameters, and tumor-related factors; stage, grade, diameter, vascular invasion, necrosis, surgical margin status, lymph node involvement; were compared between groups and independent prognostic factors for disease recurrence were investigated. Statistical analysis Student’s t-test and Mann-Whitney U were used for the comparison of numerical and chi-square tests for categorical variables. Results were expressed with mean values and standard deviations. Univariate and multivariate analyses were performed to define independent prognostic factors for disease recurrence. The receiver operating characteristic (ROC) curve and respective area under the curve (AUC) were calculated for independent variables and Kaplan-Meier survival analysis was done. p values < 0.05 were considered statistically significant. Statistical Package for Social Sciences (SPSS, Chicago, IL) software version 24.0 was used for all statistical analysis. Results A total of 749 patients were included in the final analysis, comprising 216 females and 533 males. The mean age at diagnosis was 59.9 ± 11.6 years (range: 18–86), with a median follow-up of 25.9 ± 31 months (range: 1–180). Disease recurrence occurred in 192 patients (25.6%), with a mean time to recurrence of 9.4 ± 17.8 months (range: 1–84). At the time of recurrence, 46 patients (23.9%) presented with locoregional relapse, while distant metastases were most frequently observed in the lungs (n = 90, 46.8%), followed by the liver (n = 25, 13.0%), bone (n = 17, 8.8%), and brain (n = 14, 7.3%). On univariate analysis (Table 1 ), several clinicopathological parameters were associated with recurrence risk. Multivariate logistic regression identified high Fuhrman nuclear grade (grade 3–4; OR = 0.169, 95% CI: 0.032–0.899, p = 0.037) and preoperative platelet count (OR = 1.012, 95% CI: 1.005–1.020, p = 0.001) as independent predictors of recurrence (Fig. 1). Table 1 Clinicopathological parameters associated with disease recurrence in locally advanced renal tumors on univariate analysis (p < 0.05) No recurrence (n = 557) Recurrence (n = 192) p Asymptomatic (%) Symptomatic (%) 47.6 52.4 31.4 68.6 < 0.001 Radiologically approved renal vein thrombus (%) No Yes 91.8 8.2 83.2 16.8 0.004 Radiologically approved vena cava thrombus (%) No Yes 95.8 4.2 91.4 8.6 0.042 Radiologically approved Gerota invasion (%) No Yes 98.7 1.3 92.9 7.1 0.001 Radiologically approved lymph node metastasis (%) 0 1 ≥ 2 85.4 9.9 4.6 78.6 10.7 10.7 0.028 Blood transfusion (%) No Yes 87.1 12.9 75.7 24.3 < 0.001 Pathologically approved adrenal invasion (%) No Yes 91.8 8.2 86.5 13.5 0.038 Pathologically approved vena cava thrombus (%) No Yes 98.2 1.8 95.5 4.5 0.044 Surgical margin (-) (%) Surgical margin (+) (%) 89.6 10.4 81.4 18.6 0.004 Pathologically approved lymph node metastasis (%) 0 1 ≥ 2 92.2 2.7 5.2 79.1 9.3 11.6 < 0.001 Fuhrman Grade (%) 1–2 3–4 52.8 47.2 24.1 75.9 < 0.001 Pathologically approved tumor necrosis (%) No Yes 68.4 31.6 59.4 40.6 0.040 Pathologically approved sarcomatoid component (%) No Yes 95.7 4.3 85.2 14.8 < 0.001 Pathological subtype (%) Clear cell Chromophobe Papillary 75 10.6 14.4 82.3 2.6 15.1 0.003 Preoperative hemoglobin 13.20 ± 2.07 11.86 ± 2.09 < 0.001 Preoperative hematocrit 39.78 ± 6.05 36.42 ± 5.92 < 0.001 Preoperative platelet count 277.77 ± 89.99 338.45 ± 127.23 < 0.001 Radiological tumor size (cm) (median, min-max) 5.75 (0.35-26) 7.85 (0.59-18) < 0.001 Pathological tumor size (cm) (median, min-max) 6 (1–37) 8 (1–40) < 0.001 BMI (median, min-max) 28.04 (17.9-49.61) 25.6 (18.2–37) 0.004 ROC curve analysis demonstrated a platelet count threshold of 363,000/µL as the optimal cut-off according to the Youden index (AUC = 0.642, 95% CI: 0.600–0.683, p < 0.001), yielding a sensitivity of 38.8% and a specificity of 86.2% (Fig. 2). Discussion Kidney tumors comprise a disease spectrum with the highest mortality rates among urological cancers which requires close monitoring and follow-up ( 1 , 2 ). Identifying parameters that can indicate recurrence development will allow us to offer adjuvant treatment options to patients, monitor patients more carefully, and predict patients’ prognosis. Commonly known parameters include tumoral and clinical characteristics which constitute various prognostic models. However, those models include all non-metastatic disease stages as stage I, II and III but not specifically locally advanced disease. And despite great effort, there is currently no established biomarker in the literature to predict disease recurrence and survival for locally advanced disease ( 8 ). There comes the aim of the present study looking for possible prognostic parameters in locally advanced disease. Cao et al. evaluated 128 patients retrospectively with renal vein thrombus with the aim of prognostic factors on long-term survival and proposed higher Fuhrmann grade, perirenal fat invasion, paraneoplastic syndromes and friable thrombus as poor prognostic factors in those patients ( 9 ). These findings are similar with our study regarding higher Fuhrmann grade which leads to higher risk for disease recurrence in our analysis. Inconsistent findings may be attributed to different patient populations as all locally advanced disease with or without renal thrombus were included in our final analysis. Shimizu et al. reported in a retrospective case series in 91 PT3aN0M0 patients that tumor size (> 7 cm), collecting system invasion and clinically detected renal vein thrombus were related to worse prognosis ( 10 ). Tumor size and presence of renal vein thrombus were also associated with recurrent disease in our univariate analysis, but lost in multivariate analysis. Collecting system invasion was even not associated with recurrence in univariate analysis. There is also discrepancy between published studies regarding the prognostic value of collecting system invasion in locally advanced kidney cancer ( 11 , 12 ). Compared to limited patient populations in those studies, our cohort included 749 patients which is one of the largest series in the existing literature and different findings may be the result of different patient populations and methods. The relationship between thrombocytosis and cancer was first established by Levin et al. in 1964. They conducted a study that included 14,000 patients with various types of cancer, including stomach, colon, lung, breast, ovarian, liver, esophageal, uterine, bone, and soft tissue cancer. It was determined that in 38% of these patients, the platelet count was found to be above 400,000/ µL ( 13 ). Following this study, it was believed that thrombocytosis could be prognostic in many cancers. In a meta-analysis published by Wu et al. in 2011, it was suggested that the systemic inflammatory response could be a prognostic factor in kidney cancer patients ( 14 ). The conclusion of the meta-analysis suggests that platelet count may be relevant to cancer-specific survival (CSS) and recurrence-free survival (RFS) but not to overall survival (OS). However, the results of these studies did not allow for the establishment of a common cut-off value for platelet count to determine prognosis ( 14 ). Gu et al. hypothesized an elevated platelet count may lead to tumor cells not being recognized by the immune system. Tumor cells can create tumor thrombi to evade the immune system. Additionally, it was proposed that factors synthesized by platelets such as PDGF and VEGF might contribute to tumor cell proliferation ( 15 ). Brookman et al. in 2009 preferred to use a cut-off value of 400,000/ µL for platelet count, similar to what Levin had used ( 16 ). However, Lehmann et al. considered a cut-off value of 338,500 for platelet count in their study ( 17 ). This discrepancy in cut-off values suggests some variability in the literature regarding the specific platelet count considered significant concerning kidney cancer prognosis. In the study conducted by Porkopowics et al. in 2016, involving 397 kidney cancer patients, they found that a platelet count above 351,000/ µL was determined as the cut-off value to predict recurrence ( 18 ). Gogus et al. also evaluated the impact of thrombocytosis which they defined as > 400,000/ µL and concluded that thrombocytosis was related to more advanced stage, worse clinical course and higher rates of disease recurrence in patients with localized kidney cancer ( 19 ). The result of our study revealed that recurrence was more frequent in patients with a platelet count above 363,000/ µL. This finding is consistent with the literature demonstrating the need for further explorative studies for possible pathophysiologic mechanisms leading to tumor recurrence with the involvement of platelets. Main limitations of the present study arise from its retrospective nature such as the lack of centralised radiological and pathological review which may compromise results compared to prospective controlled studies. However, most of the studies evaluating prognostic and predictive parameters for kidney tumors in the literature are also retrospective and our patient cohort is one of the largest for locally advanced disease compared to previously published articles. Conclusion Our findings suggest that there may be a relatively higher need for adjuvant treatments in locally advanced kidney tumors, especially in patient groups with Fuhrman grades 3–4 and with preoperative platelet counts exceeding 363.000/ µL, who are at a higher risk of recurrence. Our findings may guide establishing follow-up and treatment protocols for similar patient groups with further support by prospective controlled studies. Declarations Ethics All procedures were done after ethical approval and in accordance with the 1964 Helsinki declaration Data Availability Statement All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author. Acknowledgements The authors thank all the patients, their families, and caregivers for their participation in this study. Conflicts Of Interest The authors declare there was no conflict of interest in this study. Source of Funding No funding Clinical trial number This study is not a clinical trial, clinical trial number not applicable Credit Authorship Contribution Statement OB: Corresponding Author, Project development, Data Collection, Data Analysis, Manuscript Writing and Editing. OU: Data Collection, Data Analysis, Manuscript Writing and Editing. EO: Data Collection, Data Analysis, Manuscript Writing and Editing. SB: Data Collection, Data Analysis, Manuscript Writing and Editing. BA: Data Collection, Data Analysis, Manuscript Writing and Editing. VI: Data Collection, Data Analysis, Manuscript Writing and Editing. SS: Data Collection, Data Analysis, Manuscript Writing and Editing. TM: Data Collection, Data Analysis, Manuscript Writing and Editing. IT: Data Collection, Data Analysis, Manuscript Writing and Editing. GA: Data Collection, Data Analysis, Manuscript Writing and Editing. References Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424 Dy GW, Gore JL, Forouzanfar MH, Naghavi M, Fitzmaurice C (2017) Global burden of urologic cancers, 1990–2013. 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Cancer 101(7):1552–1562. https://doi.org/10.1002/cncr.20549) Prokopowicz G, Życzkowski M, Nowakowski K, Bogacki R, Bryniarski P, Paradysz A (2016) Basic Parameters of Blood Count as Prognostic Factors for Renal Cell Carcinoma. BioMed Research International, 2016. https://doi.org/10.1155/2016/8687575 ) Gogus C, Baltaci S, Filiz E, Elhan A, Beduk Y (2004) Significance of thrombocytosis for determining prognosis in patients with localized renal cell carcinoma. Urology 63(3):447–450. 10.1016/j.urology.2003.10.039 Additional Declarations The authors declare no competing interests. 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-7682253","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518955765,"identity":"a8d66948-8adc-4482-b498-8e19773b6457","order_by":0,"name":"Ozan 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09:30:31","extension":"html","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76864,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7682253/v1/78dcdc17a57d4840560e7421.html"},{"id":92069156,"identity":"69997d7b-d26d-49c1-ba07-69bb40b9848e","added_by":"auto","created_at":"2025-09-24 09:30:37","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":142610,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7682253/v1/9989d5c4e4be47dc14505887.png"},{"id":92069157,"identity":"b6a6f40b-73c0-4632-b852-adccca653b3c","added_by":"auto","created_at":"2025-09-24 09:30:37","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":140765,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7682253/v1/67fa93479c7f0288258c758f.png"},{"id":92070071,"identity":"ae4b71e9-329d-47ab-96ae-396056ee64df","added_by":"auto","created_at":"2025-09-24 09:37:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1131860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7682253/v1/5988bc7b-93e7-4f67-a674-baf5022db7b4.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMulti-institutional analysis of clinical risk factors for disease recurrence in locally advanced renal cell carcinoma\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) represents a significant global health burden, accounting for approximately 2.2% of all malignancies, with an estimated 400,000 new diagnoses and 175,000 deaths annually (1,2). At presentation, 70–75% of cases are confined to the kidney, while 15% present with locoregional disease. The 5-year survival rate decreases markedly from 75–80% in stage I–II disease to around 50% in stage III disease in large population-based series (3). Despite curative-intent surgery, recurrence rates can reach up to 80% in patients with high-risk localized or locoregional disease (4). Several prognostic models have been developed to identify patients at higher risk for recurrence. The UCLA Integrated Staging System (UISS), the Leibovich score, the Stage, Size, Grade, and Necrosis (SSIGN) system, and the Karakiewicz nomogram primarily incorporate tumor-related variables and have frequently been applied in the selection of patients for adjuvant clinical trials (5). Currently, active surveillance with close follow-up remains the standard of care for high-risk patients, given the limited efficacy demonstrated in previous adjuvant treatment studies. Accurately selecting patients who may truly benefit from perioperative therapy remains a critical challenge, as inappropriate patient selection is considered one of the main reasons for the failure of prior adjuvant trials. To address this limitation, gene expression signatures and molecular assays are being actively investigated as potential tools for individualized risk stratification (6). However, their integration into routine clinical practice is hampered by issues of cost, accessibility, and lack of standardization. In this context, the present study aimed to evaluate readily available clinical and tumor-related parameters as predictors of recurrence in a relatively homogeneous cohort of patients with locally advanced RCC.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eAfter local ethical approval, all patients with locally advanced renal cell carcinoma (T3-T4N0M0, TxN1M0) in the Turkish Urooncology Association \u0026ndash; Renal Tumors Database were included except those receiving adjuvant therapy. Patients with no follow-up data were excluded. All procedures were in accordance with the 1964 Helsinki declaration. All patients underwent standardized radical nephrectomy and/or lymphadenectomy with open or laparoscopic approach. The decision to perform lymphadenectomy has been made preoperatively with imaging findings or intraoperatively. Tumors invading perinephric tissues, pelvicalyceal system, renal vein, or segmental branches were defined as PT3 and PT4 if the tumor extended beyond Gerota\u0026rsquo;s fascia according to TNM classification. N1 was defined as the metastatic spread of the disease to the regional lymph nodes. Follow-up protocols were in accordance with the latest guidelines on renal cell carcinoma (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). All relapses including regional lymph nodes and metastasis to the lungs, bones, brain, and liver were defined as disease recurrence. Patients were divided into two groups according to disease recurrence status group 1 (no recurrence) and group 2 (recurrent disease). Demographic and clinical factors in concordance with the existing literature; age, sex, body mass index (BMI), performance status, Charlson comorbity index (CCI), symptomatic status, hematologic parameters, and tumor-related factors; stage, grade, diameter, vascular invasion, necrosis, surgical margin status, lymph node involvement; were compared between groups and independent prognostic factors for disease recurrence were investigated.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003cp\u003eStudent\u0026rsquo;s t-test and Mann-Whitney U were used for the comparison of numerical and chi-square tests for categorical variables. Results were expressed with mean values and standard deviations. Univariate and multivariate analyses were performed to define independent prognostic factors for disease recurrence. The receiver operating characteristic (ROC) curve and respective area under the curve (AUC) were calculated for independent variables and Kaplan-Meier survival analysis was done. p values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant. Statistical Package for Social Sciences (SPSS, Chicago, IL) software version 24.0 was used for all statistical analysis.\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 749 patients were included in the final analysis, comprising 216 females and 533 males. The mean age at diagnosis was 59.9\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6 years (range: 18\u0026ndash;86), with a median follow-up of 25.9\u0026thinsp;\u0026plusmn;\u0026thinsp;31 months (range: 1\u0026ndash;180). Disease recurrence occurred in 192 patients (25.6%), with a mean time to recurrence of 9.4\u0026thinsp;\u0026plusmn;\u0026thinsp;17.8 months (range: 1\u0026ndash;84). At the time of recurrence, 46 patients (23.9%) presented with locoregional relapse, while distant metastases were most frequently observed in the lungs (n\u0026thinsp;=\u0026thinsp;90, 46.8%), followed by the liver (n\u0026thinsp;=\u0026thinsp;25, 13.0%), bone (n\u0026thinsp;=\u0026thinsp;17, 8.8%), and brain (n\u0026thinsp;=\u0026thinsp;14, 7.3%). On univariate analysis (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), several clinicopathological parameters were associated with recurrence risk. Multivariate logistic regression identified high Fuhrman nuclear grade (grade 3\u0026ndash;4; OR\u0026thinsp;=\u0026thinsp;0.169, 95% CI: 0.032\u0026ndash;0.899, p\u0026thinsp;=\u0026thinsp;0.037) and preoperative platelet count (OR\u0026thinsp;=\u0026thinsp;1.012, 95% CI: 1.005\u0026ndash;1.020, p\u0026thinsp;=\u0026thinsp;0.001) as independent predictors of recurrence (Fig. 1).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \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\u003eClinicopathological parameters associated with disease recurrence in locally advanced renal tumors on univariate analysis (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" style=\"width: 56.6358%;\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003eNo recurrence\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;557)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRecurrence\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;192)\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 \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAsymptomatic (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSymptomatic (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003cp\u003e52.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.4\u003c/p\u003e\n \u003cp\u003e68.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiologically approved renal vein thrombus (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e83.2\u003c/p\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiologically approved vena cava thrombus (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e95.8\u003c/p\u003e\n \u003cp\u003e4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e91.4\u003c/p\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiologically approved Gerota invasion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e92.9\u003c/p\u003e\n \u003cp\u003e7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiologically approved lymph node metastasis (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge; 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e85.4\u003c/p\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003cp\u003e4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e78.6\u003c/p\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003cp\u003e10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBlood transfusion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e87.1\u003c/p\u003e\n \u003cp\u003e12.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e75.7\u003c/p\u003e\n \u003cp\u003e24.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathologically approved adrenal invasion (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e91.8\u003c/p\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e86.5\u003c/p\u003e\n \u003cp\u003e13.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathologically approved vena cava thrombus (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e95.5\u003c/p\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical margin (-) (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSurgical margin (+) (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003cp\u003e10.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81.4\u003c/p\u003e\n \u003cp\u003e18.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathologically approved lymph node metastasis (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge; 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e92.2\u003c/p\u003e\n \u003cp\u003e2.7\u003c/p\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e79.1\u003c/p\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFuhrman Grade (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e1\u0026ndash;2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3\u0026ndash;4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e52.8\u003c/p\u003e\n \u003cp\u003e47.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e24.1\u003c/p\u003e\n \u003cp\u003e75.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathologically approved tumor necrosis (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e68.4\u003c/p\u003e\n \u003cp\u003e31.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e59.4\u003c/p\u003e\n \u003cp\u003e40.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathologically approved sarcomatoid component (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e95.7\u003c/p\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e85.2\u003c/p\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathological subtype (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eClear cell\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eChromophobe\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePapillary\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003cp\u003e14.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e82.3\u003c/p\u003e\n \u003cp\u003e2.6\u003c/p\u003e\n \u003cp\u003e15.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative hemoglobin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e13.20\u0026thinsp;\u0026plusmn;\u0026thinsp;2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.86\u0026thinsp;\u0026plusmn;\u0026thinsp;2.09\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative hematocrit\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e39.78\u0026thinsp;\u0026plusmn;\u0026thinsp;6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.42\u0026thinsp;\u0026plusmn;\u0026thinsp;5.92\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePreoperative platelet count\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e277.77\u0026thinsp;\u0026plusmn;\u0026thinsp;89.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e338.45\u0026thinsp;\u0026plusmn;\u0026thinsp;127.23\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiological tumor size (cm) (median, min-max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e5.75 (0.35-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.85 (0.59-18)\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathological tumor size (cm) (median, min-max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e6 (1\u0026ndash;37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (1\u0026ndash;40)\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" style=\"width: 56.6358%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (median, min-max)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" style=\"width: 18.9815%;\"\u003e\n \u003cp\u003e28.04 (17.9-49.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.6 (18.2\u0026ndash;37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eROC curve analysis demonstrated a platelet count threshold of 363,000/\u0026micro;L as the optimal cut-off according to the Youden index (AUC\u0026thinsp;=\u0026thinsp;0.642, 95% CI: 0.600\u0026ndash;0.683, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), yielding a sensitivity of 38.8% and a specificity of 86.2% (Fig. 2).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eKidney tumors comprise a disease spectrum with the highest mortality rates among urological cancers which requires close monitoring and follow-up (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Identifying parameters that can indicate recurrence development will allow us to offer adjuvant treatment options to patients, monitor patients more carefully, and predict patients\u0026rsquo; prognosis. Commonly known parameters include tumoral and clinical characteristics which constitute various prognostic models. However, those models include all non-metastatic disease stages as stage I, II and III but not specifically locally advanced disease. And despite great effort, there is currently no established biomarker in the literature to predict disease recurrence and survival for locally advanced disease (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). There comes the aim of the present study looking for possible prognostic parameters in locally advanced disease. Cao et al. evaluated 128 patients retrospectively with renal vein thrombus with the aim of prognostic factors on long-term survival and proposed higher Fuhrmann grade, perirenal fat invasion, paraneoplastic syndromes and friable thrombus as poor prognostic factors in those patients (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). These findings are similar with our study regarding higher Fuhrmann grade which leads to higher risk for disease recurrence in our analysis. Inconsistent findings may be attributed to different patient populations as all locally advanced disease with or without renal thrombus were included in our final analysis. Shimizu et al. reported in a retrospective case series in 91 PT3aN0M0 patients that tumor size (\u0026gt;\u0026thinsp;7 cm), collecting system invasion and clinically detected renal vein thrombus were related to worse prognosis (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Tumor size and presence of renal vein thrombus were also associated with recurrent disease in our univariate analysis, but lost in multivariate analysis. Collecting system invasion was even not associated with recurrence in univariate analysis. There is also discrepancy between published studies regarding the prognostic value of collecting system invasion in locally advanced kidney cancer (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Compared to limited patient populations in those studies, our cohort included 749 patients which is one of the largest series in the existing literature and different findings may be the result of different patient populations and methods.\u003c/p\u003e\u003cp\u003eThe relationship between thrombocytosis and cancer was first established by Levin et al. in 1964. They conducted a study that included 14,000 patients with various types of cancer, including stomach, colon, lung, breast, ovarian, liver, esophageal, uterine, bone, and soft tissue cancer. It was determined that in 38% of these patients, the platelet count was found to be above 400,000/ \u0026micro;L (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Following this study, it was believed that thrombocytosis could be prognostic in many cancers. In a meta-analysis published by Wu et al. in 2011, it was suggested that the systemic inflammatory response could be a prognostic factor in kidney cancer patients (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). The conclusion of the meta-analysis suggests that platelet count may be relevant to cancer-specific survival (CSS) and recurrence-free survival (RFS) but not to overall survival (OS). However, the results of these studies did not allow for the establishment of a common cut-off value for platelet count to determine prognosis (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Gu et al. hypothesized an elevated platelet count may lead to tumor cells not being recognized by the immune system. Tumor cells can create tumor thrombi to evade the immune system. Additionally, it was proposed that factors synthesized by platelets such as PDGF and VEGF might contribute to tumor cell proliferation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Brookman et al. in 2009 preferred to use a cut-off value of 400,000/ \u0026micro;L for platelet count, similar to what Levin had used (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, Lehmann et al. considered a cut-off value of 338,500 for platelet count in their study (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). This discrepancy in cut-off values suggests some variability in the literature regarding the specific platelet count considered significant concerning kidney cancer prognosis. In the study conducted by Porkopowics et al. in 2016, involving 397 kidney cancer patients, they found that a platelet count above 351,000/ \u0026micro;L was determined as the cut-off value to predict recurrence (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Gogus et al. also evaluated the impact of thrombocytosis which they defined as \u0026gt;\u0026thinsp;400,000/ \u0026micro;L and concluded that thrombocytosis was related to more advanced stage, worse clinical course and higher rates of disease recurrence in patients with localized kidney cancer (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The result of our study revealed that recurrence was more frequent in patients with a platelet count above 363,000/ \u0026micro;L. This finding is consistent with the literature demonstrating the need for further explorative studies for possible pathophysiologic mechanisms leading to tumor recurrence with the involvement of platelets.\u003c/p\u003e\u003cp\u003eMain limitations of the present study arise from its retrospective nature such as the lack of centralised radiological and pathological review which may compromise results compared to prospective controlled studies. However, most of the studies evaluating prognostic and predictive parameters for kidney tumors in the literature are also retrospective and our patient cohort is one of the largest for locally advanced disease compared to previously published articles.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur findings suggest that there may be a relatively higher need for adjuvant treatments in locally advanced kidney tumors, especially in patient groups with Fuhrman grades 3\u0026ndash;4 and with preoperative platelet counts exceeding 363.000/ \u0026micro;L, who are at a higher risk of recurrence. Our findings may guide establishing follow-up and treatment protocols for similar patient groups with further support by prospective controlled studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll procedures were done after ethical approval and in accordance with the 1964 Helsinki declaration\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank all the patients, their families, and caregivers for their participation in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts Of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare there was no conflict of interest in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is not a clinical trial, clinical trial number not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCredit Authorship Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOB: \u0026nbsp;Corresponding Author, Project development, Data Collection, Data Analysis, Manuscript Writing and Editing. OU: Data Collection, Data Analysis, Manuscript Writing and Editing. EO: Data Collection, Data Analysis, Manuscript Writing and Editing. SB: Data Collection, Data Analysis, Manuscript Writing and Editing. BA: Data Collection, Data Analysis, Manuscript Writing and Editing. VI: Data Collection, Data Analysis, Manuscript Writing and Editing. SS: Data Collection, Data Analysis, Manuscript Writing and Editing. TM: Data Collection, Data Analysis, Manuscript Writing and Editing. IT: Data Collection, Data Analysis, Manuscript Writing and Editing. GA: Data Collection, Data Analysis, Manuscript Writing and Editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Urology 63(3):447\u0026ndash;450. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.urology.2003.10.039\u003c/span\u003e\u003cspan address=\"10.1016/j.urology.2003.10.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Dokuz Eylül University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Renal cell carcinoma, Fuhrmann grade, Thrombocytosis, Prognosis","lastPublishedDoi":"10.21203/rs.3.rs-7682253/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7682253/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003eThis study aimed to identify clinical risk factors associated with disease recurrence in patients with locally advanced renal cell carcinoma (RCC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and Methods: \u003c/strong\u003ePatients diagnosed with locally advanced RCC (T3–T4N0M0 or TxN1M0) from the Turkish Urooncology Association Kidney Cancer Database were included. Demographic and clinical variables (age, sex, body mass index [BMI], performance status, Charlson Comorbidity Index [CCI], symptomatic status, hematologic parameters) and tumor-related characteristics (stage, grade, size, vascular invasion, necrosis, surgical margin status, and lymph node involvement) were analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: A total of 749 patients were evaluated, including 216 women and 533 men. The mean age was 59.9 ± 11.6 years (range: 18–86), and the median follow-up was 25.9 ± 31 months (range: 1–180). Disease recurrence occurred in 192 patients (25.6%), with a mean time to recurrence of 9.4 ± 17.8 months (range: 1–84). On univariate and multivariate analyses, Fuhrman grade 3–4 (OR = 0.169, 95% CI: 0.032–0.899, p = 0.037) and platelet count (OR = 1.012, 95% CI: 1.005–1.020, p = 0.001) emerged as independent predictors of recurrence. ROC curve analysis identified a platelet count cut-off of 363,000/µL (AUC = 0.642, 95% CI: 0.600–0.683, p \u0026lt; 0.001; sensitivity: 38.8%, specificity: 86.2%).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eHigh Fuhrman grade (3–4) and elevated platelet count were identified as independent risk factors for disease recurrence in patients with locally advanced RCC. These findings may contribute to risk stratification and help guide follow-up strategies and adjuvant treatment considerations in both clinical practice and clinical trial design.\u003c/p\u003e","manuscriptTitle":"Multi-institutional analysis of clinical risk factors for disease recurrence in locally advanced renal cell carcinoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 09:23:28","doi":"10.21203/rs.3.rs-7682253/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":"17b1072e-77c5-4fdb-9afe-bfd3ca9c58ca","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55147677,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2025-09-24T09:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 09:23:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7682253","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7682253","identity":"rs-7682253","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}