Assessment of Meet-URO and CANLPH Prognostic Models in Metastatic RCC: Insights From a Single-Institution Cohort Predominantly Treated With TKIs

Assessment of Meet-URO and CANLPH Prognostic Models in Metastatic RCC: Insights From a Single-Institution Cohort Predominantly Treated With TKIs | 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 Assessment of Meet-URO and CANLPH Prognostic Models in Metastatic RCC: Insights From a Single-Institution Cohort Predominantly Treated With TKIs ömer faruk kuzu, nuri karadurmuş, nebi batuhan kanat, dilruba ilayda özel bozbağ, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8327307/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Accurate prognostic assessment remains crucial in metastatic renal cell carcinoma (mRCC), especially as treatment options have expanded beyond vascular endothelial growth factor (VEGF)–targeted therapies to include immune checkpoint inhibitors (ICIs) and ICI–TKI combinations. The widely used IMDC classification shows important limitations in the modern therapeutic era, highlighting the need for complementary prognostic tools. In this context, the Meet-URO and CANLPH scores—incorporating clinical, inflammatory, and nutritional markers have emerged as promising alternatives. Objective To evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in a real-world mRCC cohort predominantly treated with first-line tyrosine kinase inhibitor (TKI) monotherapy due to limited access to ICI-based combinations. Methods This retrospective single-center study included 112 patients with mRCC. The Meet-URO score was calculated for all patients, while the CANLPH score was assessed in 56 patients with complete laboratory data. CAR, NLR, and PHR were computed using baseline pre-treatment measurements. Overall survival (OS) and progression-free survival (PFS) the latter defined exclusively for first-line therapy—were estimated using the Kaplan–Meier method. Correlations between inflammatory markers and survival outcomes were analyzed using Spearman’s rho. Results Meet-URO demonstrated clear prognostic stratification across all five categories, with the most favorable outcomes in score group 2 and progressively poorer OS and PFS in higher-risk groups. CANLPH also showed meaningful survival discrimination, with the highest inflammatory group (score 3) exhibiting markedly reduced OS and PFS. CAR was the strongest individual predictor of survival, while NLR and PHR showed weaker associations. Conclusion Both Meet-URO and CANLPH provide strong, complementary prognostic information in mRCC, even in a cohort largely treated with TKI monotherapy. Their integration into routine risk assessment may enhance clinical decision-making, particularly in resource-limited settings. Metastatic renal cell carcinoma Meet-URO score CANLPH score systemic inflammation prognosis Figures Figure 1 Figure 2 Figure 3 Introduction Renal cell carcinoma (RCC) is a clinically heterogeneous and biologically complex malignancy, accounting for 3%–5% of all adult cancers and more than 80,000 new cases annually ( 1 ). Approximately one-quarter of patients present with metastatic disease at diagnosis, reflecting its often silent progression and aggressive behavior ( 2 ). Clear-cell RCC is the predominant histopathological subtype, comprising nearly 70% of cases, followed by papillary, chromophobe, and collecting duct variants ( 3 ). While advances in systemic therapy have significantly improved outcomes, survival remains highly variable, highlighting the continued need for accurate and clinically meaningful prognostic tools. For more than a decade, the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classification has served as the most widely implemented prognostic system in metastatic RCC (mRCC), incorporating readily available clinical and laboratory parameters ( 4 , 5 ). Developed in the era of VEGF-targeted therapies( 6 ), the IMDC model has guided treatment selection and clinical trial stratification with considerable success. However, the rapid evolution of the therapeutic landscape—with the introduction of immune checkpoint inhibitors (ICIs), VEGF–TKI/ICI combinations, and dual immunotherapy—has revealed important limitations of the IMDC score. Notably, favorable-risk patients do not consistently derive an overall survival (OS) benefit from ICI-based combinations ( 7 ) and the benefit of first-line nivolumab plus ipilimumab has been confirmed primarily in intermediate- and poor-risk groups ( 8 ). These findings underscore the need for complementary prognostic tools that more accurately reflect contemporary treatment biology. Systemic inflammation is now recognized as a key determinant of cancer progression and therapeutic resistance across multiple tumor types ( 9 , 10 ). Simple, inexpensive inflammatory markers derived from peripheral blood—such as neutrophil-to-lymphocyte ratio (NLR), platelet-based indices, and acute-phase reactants—have demonstrated prognostic value across diverse malignancies and therapeutic settings( 11 – 15 ). Building upon the IMDC framework and acknowledging the prognostic relevance of systemic inflammation, the Meet-URO score was developed and validated in a cohort of 571 patients receiving second-line nivolumab ( 16 ). By incorporating baseline bone metastases and pretreatment NLR (≥ 3.2) into the IMDC criteria, the Meet-URO score demonstrated superior prognostic accuracy, both for patients treated with nivolumab and those receiving later-line cabozantinib ( 16 , 17 ). Traditional molecular biomarkers such as microsatellite instability (MSI) ( 18 ) and tumor mutational burden (TMB) ( 19 ) have limited relevance in RCC ( 20 ), further emphasizing the need for alternative prognostic tools. Recent studies highlight the key role of systemic inflammation—particularly C-reactive protein (CRP)—in shaping outcomes in patients treated with ICIs ( 21 ). In this context, the CANLPH score has emerged as a promising, inflammation-based prognostic model. This composite index incorporates three systemic markers: the CRP-to-albumin ratio (CAR) ( 22 ), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-hemoglobin ratio (PHR) ( 23 , 24 ). Collectively, these parameters reflect systemic inflammation, nutritional status, and hematologic physiology, offering a comprehensive assessment of host–tumor interaction. Taken collectively, the limitations of traditional prognostic systems, the emergence of novel inflammation-based biomarkers, and the diversity of real-world treatment settings highlight the need for models that maintain prognostic performance across different therapeutic contexts. Notably, the distinct survival gradients observed in our cohort—across both Meet-URO categories and CANLPH inflammatory groups—suggest that integrating clinical and inflammatory markers may offer more robust and clinically actionable prognostic stratification. Therefore, the present study was designed to comprehensively evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in patients with metastatic RCC receiving systemic therapy. By assessing these models side-by-side in a real-world cohort, the study aims to determine whether they provide improved and clinically relevant risk discrimination beyond existing frameworks, particularly in settings where access to modern ICI-based combinations remains limited. Materials and Methods This retrospective, single-center observational study included 112 patients diagnosed with renal cell carcinoma (RCC) who received systemic therapy. Clinical, laboratory, and pathological data at the time of diagnosis were extracted from institutional electronic medical records. Among the full cohort, 56 patients had complete biochemical data required for calculation of the CANLPH score, while 112 patients had sufficient information for assessment of the MEET-URO score. Patients lacking one or more laboratory parameters necessary for CANLPH scoring—including C-reactive protein (CRP), albumin, neutrophil count, lymphocyte count, platelet count, and hemoglobin—were excluded from inflammation-based analyses. All laboratory tests were obtained prior to the initiation of first-line systemic therapy, ensuring that inflammatory markers reflected pretreatment baseline values. Using these measurements, the CAR, NLR, and PHR were calculated for each patient. These indices were used both for constructing the CANLPH score and for additional exploratory analyses. Specifically, Spearman’s rank correlation was applied to evaluate the association of each marker with overall survival (OS) and progression-free survival (PFS) in the available subsets (CAR: n = 58 for OS and n = 54 for PFS; NLR: n = 104 for OS and n = 96 for PFS; PHR: n = 104 for OS and n = 96 for PFS). One-tailed significance testing was employed based on the predefined hypothesis that elevated systemic inflammation would be associated with poorer clinical outcomes. Demographic and clinical variables collected included age, sex, histologic subtype, disease stage at diagnosis, and sites of metastasis (bone, liver, lung, central nervous system, lymph nodes, or soft tissue). Treatment-related variables recorded included the number of systemic therapy lines received and the specific first-line regimen administered (sunitinib, pazopanib, cabozantinib, or nivolumab plus cabozantinib). The CANLPH score was constructed in accordance with the methodology described by Komura et al. ( 24 ). Using cut-off values determined by the Youden Index ( 25 ), each of the following was assigned one point: CAR ≥ 1.5, NLR ≥ 2.8, and sex-specific thresholds for PHR (≥ 2.1 for men and ≥ 2.3 for women). This yielded a composite inflammation score ranging from 0 to 3 for each patient. The Meet-URO score incorporates the presence of bone metastases and baseline neutrophil-to-lymphocyte ratio (NLR) ≥ 3.2 into the IMDC score (a web calculator is available here: https://proviso.shinyapps.io/ Meet-URO15_score/) ( 16 ). This scoring system stratifies patients from group 1 (most favorable prognosis) to group 5 (poorest prognosis), with lower- numbered groups associated with longer survival. Previous studies have demonstrated that the Meet-URO model outperforms the IMDC model in patients with mRCC treated with second-line nivolumab or cabozantinib, as well as in those receiving first-line nivolumab plus ipilimumab ( 17 , 26 ). OS and PFS were calculated using standard time-to-event methodology. OS was defined as the interval from diagnosis of metastatic disease to death from any cause or last follow-up. PFS was defined exclusively for first-line systemic therapy as the time from treatment initiation to radiologic or clinical progression or death. Patients without documented progression were censored at their last available disease assessment. All statistical analyses were performed using the IBM SPSS Statistics 27.0 (IBM Corp., Armonk, NY, USA) software package. Continuous variables were described as medians (interquartile range (IQR)) and categorical variables as percentages. Survival curves and rates were estimated using the Kaplan–Meier method. The log-rank test was used to compare the survival outcomes between the groups. All reported p-values were two-sided, and p-values < 0.05 were regarded as statistically significant.. Results A total of 112 patients were included in the analysis. Baseline demographic, pathological, and clinical characteristics are presented in Table 1 . The cohort comprised 37 females (33.0%) and 75 males (67.0%). Clear cell renal cell carcinoma was the predominant histologic subtype (89.3%), followed by papillary (6.3%), chromophobe (1.8%), and collecting duct carcinoma (1.8%). At diagnosis, the majority of patients presented with advanced disease, with 65.2% classified as Stage IV. The most common metastatic sites were the lung (59.8%) and lymph nodes or soft tissue (60.7%), followed by bone (43.8%), liver (22.3%), and the central nervous system (8.9%). Regarding treatment exposure, 62.5% of patients had received three or more lines of systemic therapy. Sunitinib (48.2%) and pazopanib (45.5%) were the most frequently used first-line regimens, whereas cabozantinib (4.5%) and the nivolumab plus cabozantinib combination (1.8%) were less common (Table 1 ). Survival Outcomes According to the MEET-URO Score Survival analyses demonstrated a clear separation across MEET-URO risk categories. In the lowest-risk group (score 1), the median OS was 62 months (95% CI: 12.0–111.9), and PFS was 27 months (95% CI: 0.0–68.6). Patients with score 2 showed the most favorable OS at 134 months (95% CI: 70.3–197.7), while PFS was 12 months (95% CI: 3.3–20.7). In the intermediate-risk group (score 3), OS declined to 50 months (95% CI: 29.9–70.1) and PFS to 12 months (95% CI: 3.9–20.1). Higher-risk categories demonstrated progressively poorer outcomes: score 4 was associated with an OS of 28 months (95% CI: 6.1–49.9) and PFS of 7 months (95% CI: 5.2–8.8), while the highest-risk category (score 5) showed the shortest survival, with an OS of 7 months (95% CI: 0.0–15.8) and PFS of 2 months (Fig. 1). Survival Outcomes According to the CANLPH Score Similar stratification was observed when patients were categorized according to the CANLPH score. Patients in the lowest-risk group (score 0) had a median OS of 32 months (95% CI: 27.20–36.80) and PFS 7 months (95% CI: 0.0–14.84). The score 1 group demonstrated the longest OS at 60 months (95% CI: 33.91–86.09), while PFS remained 7 months (95% CI: 4.81–9.19). For patients in the score 2 group, OS was 47 months (95% CI: 31.16–62.84) with the longest PFS observed at 13 months (95% CI: 3.40–22.60). The highest-risk group (score 3) exhibited markedly inferior outcomes, with OS falling to 9 (95% CI: 0.60–17.40) months and PFS to 3 months (95% CI: 0.60–5.40) (Fig. 2). Correlation Between Inflammatory Markers and Survival Outcomes Spearman’s correlation analysis revealed heterogeneous associations between the inflammatory biomarkers and survival endpoints (Table 2 ). CAR demonstrated significant negative correlations with both OS (ρ = − 0.380, p = 0.002, n = 58) and PFS (ρ = − 0.376, p = 0.003, n = 54), indicating that higher baseline CAR values were associated with shorter survival. In contrast, NLR was not significantly correlated with OS (ρ = 0.041, p = 0.341, n = 104) or PFS (ρ = − 0.149, p = 0.074, n = 96). PHR demonstrated a weak but statistically significant negative correlation with OS (ρ = − 0.179, p = 0.035, n = 104), while its relationship with PFS did not reach significance (ρ = − 0.047, p = 0.324, n = 96). These findings are illustrated in (Fig. 3). Table 1 Baseline Clinical Characteristics Variable Category n % Sex Female 37 33.0 Male 75 67.0 Histology Clear cell 100 89.3 Papillary 7 6.3 Chromophobe 2 1.8 Collecting duct 2 1.8 Stage at diagnosis Stage I 8 7.1 Stage II 24 21.4 Stage III 7 6.3 Stage IV 73 65.2 Metastasis Bone 49 43.8 Liver 25 22.3 Lung 67 59.8 CNS 10 8.9 LN / Soft Tissue 68 60.7 Systemic therapy lines ≤ 2 42 37.5 ≥ 3 70 62.5 First-line treatment Sunitinib 54 48.2 Pazopanib 51 45.5 Cabozantinib 5 4.5 Nivolumab + Cabozantinib 2 1.8 Table 2 Associations of CAR, NLR, and PHR with survival outcomes based on Spearman’s rho coefficients. Inflammatory Marker Outcome Spearman’s rho Significance (1-tailed) N CAR OS -0.380 0.002 58 PFS -0.376 0.003 54 NLR OS 0.041 0.341 104 PFS -0.149 0.074 96 PHR OS -0.179 0.035 104 PFS -0.047 0.324 96 Discussion The management of mRCC continues to evolve rapidly, driven by advances in systemic therapies and an improved understanding of tumor biology. Prognostic stratification remains essential for optimizing treatment selection, designing clinical trials, and counseling patients about expected outcomes. Historically, the IMDC score has served as the standard prognostic model for patients receiving VEGF-targeted therapies ( 4 – 6 ). However, with the emergence of ICIs, VEGF–TKI/ICI combinations, and dual checkpoint blockade, the limitations of the IMDC system have become increasingly pronounced. Multiple studies have demonstrated that patients classified as favorable-risk by IMDC may not derive a clear OS benefit from ICI-based combinations ( 7 ). Additionally, the confirmed benefit of nivolumab plus ipilimumab in the CheckMate-214 trial was predominantly restricted to intermediate- and poor-risk groups ( 8 ), highlighting the need for refined prognostic tools capable of reflecting the biology and treatment response patterns of the modern therapeutic landscape. The Meet-URO score was developed to address some of these gaps by integrating two additional variables—baseline bone metastases and pretreatment NLR—into the traditional IMDC framework ( 16 ). Both variables have strong biological rationale: bone metastases reflect aggressive disease biology and niche-mediated tumor support, while NLR represents systemic inflammation and dysregulated immunity, both of which are known to influence response to immunotherapy ( 9 – 15 ). Validation studies across multiple cohorts have demonstrated that Meet-URO outperforms the IMDC model in predicting OS and PFS in patients treated with second-line nivolumab, cabozantinib, and even first-line nivolumab plus ipilimumab in expanded-access programs ( 16 , 17 , 26 ). These results suggest that Meet-URO offers broader prognostic applicability across multiple treatment lines and therapeutic classes. Our findings strongly align with the existing literature and further validate the prognostic performance of the Meet-URO scoring system. A clear and clinically meaningful separation in survival outcomes was observed across all five Meet-URO categories. Patients in score group 2—representing the most favorable profile within the non–IMDC favorable-risk population—experienced the longest survival, whereas outcomes declined progressively toward the highest-risk group (score 5), which demonstrated markedly inferior OS and PFS. The preservation of this stratification effect in our patient population is particularly noteworthy, given the distinctive treatment patterns in our cohort. A distinctive aspect of our study is the real-world therapeutic context in which these prognostic systems were evaluated. Unlike high-income countries where ICI–TKI combinations constitute the standard first-line therapy, the vast majority of our patients received single-agent VEGFR-TKIs (mainly sunitinib or pazopanib) in the first-line setting. Limited national access to immunotherapy, financial constraints, and reimbursement restrictions—all common features of developing healthcare systems—loom as major determinants of treatment choice. Despite these constraints, Meet-URO maintained its strong discriminatory performance, reinforcing the robustness and generalizability of this scoring system beyond immunotherapy-rich environments. This finding has meaningful implications for global oncology, as it supports the utility of Meet-URO in diverse socioeconomic and therapeutic settings. Alongside the Meet-URO score, the CANLPH score also demonstrated significant prognostic relevance in our cohort. Inflammation and nutrition-related biomarkers have increasingly gained attention as prognostic tools in cancer due to their correlation with tumor progression, host immune response, and treatment resistance ( 21 – 24 ). The CANLPH model incorporates three readily available laboratory measures—CAR, NLR, and PHR—that together capture systemic inflammation, nutritional status, and hematologic physiology. Consistent with previous findings by Komura et al. ( 24 ), we observed that higher CANLPH scores were associated with progressively shorter OS and PFS. CANLPH 0–1 groups exhibited more favorable outcomes, whereas CANLPH 3—the highest inflammatory burden—was associated with a dramatically reduced survival. Among the individual biomarkers, CAR emerged as the strongest predictor of both OS and PFS. Elevated CAR reflects increased CRP (a marker of cytokine-driven inflammation) combined with reduced albumin (a surrogate for malnutrition and systemic metabolic stress). This dual representation of inflammation and nutritional decline has been shown to predict poor outcomes in numerous malignancies and across treatment modalities ( 21 , 22 ). Meanwhile, NLR and PHR showed weaker and more heterogeneous associations with survival, which may reflect their sensitivity to transient physiological changes or heterogeneous disease dynamics. Nonetheless, when integrated into the CANLPH model, these markers collectively produced a robust stratification pattern, supporting the clinical value of composite inflammatory scoring systems. The complementary prognostic performance of Meet-URO and CANLPH in our study highlights the relevance of integrating clinical, metastatic, and inflammatory characteristics into contemporary prognostic assessment. While Meet-URO incorporates tumor burden and immune-inflammatory interactions through bone metastases and NLR, CANLPH focuses more specifically on systemic inflammation and nutritional physiology. The strong prognostic gradients observed with both models underscore the multidimensional nature of mRCC biology and the potential advantage of utilizing more than one scoring system to achieve precise prognostication. Limitations This study has several limitations that must be considered. First, the retrospective and single-center design increases the potential for selection and information bias. Second, although 112 patients were included overall, only 56 had complete biochemical data required for CANLPH scoring, limiting the power of inflammatory biomarker analyses. Third, treatment heterogeneity—including predominant first-line TKI monotherapy due to restricted access to ICI-based combinations—may affect survival outcomes and limit comparability with international cohorts. Finally, the absence of external validation limits the generalizability of our findings. Future Directions Future research should incorporate multicenter prospective studies involving larger patient populations and including those treated with modern first-line ICI–TKI combinations or dual checkpoint inhibitor regimens. Evaluating dynamic changes in inflammation-related biomarkers during treatment could provide additional prognostic information and support adaptive therapeutic strategies. Integrating established clinical models such as IMDC and Meet-URO with inflammatory or nutritional indices like CANLPH, as well as emerging biomarkers—radiomics, circulating tumor DNA, cytokine signatures, and machine-learning–based risk calculators—may further refine prognostic precision. Given the global disparities in access to immunotherapy, additional real-world studies from low- and middle-income countries are essential to ensure broad applicability and equity in prognostic assessment. Conclusion In conclusion, our findings demonstrate that both the Meet-URO and CANLPH scoring systems provide strong and independent prognostic information in metastatic RCC, even in a real-world population in which most patients received TKI monotherapy rather than modern immunotherapy-based combinations. Meet-URO effectively stratified risk across all five categories, while CANLPH distinguished meaningful differences in survival based on systemic inflammation and nutritional status. These results highlight the complementary nature of clinical and inflammatory prognostic models and support their integration into routine risk assessment, particularly in resource-limited settings. Further prospective validation in diverse treatment landscapes is warranted to refine and extend their clinical utility. Declarations Author Contributions: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ömer Faruk Kuzu, Nuri Karadurmuş, Nebi Batuhan Kanat, Dilruba İlayda Özel Bozbağ, Berkan Karadurmuş, Esmanur Kaplan Tüzün, Hüseyin Atacan, Nurlan Mammadzada, Gizem Yıldırım, Musa Barış Aykan, İsmail Ertürk. The first draft of the manuscript was written by Ömer Faruk Kuzu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Data Availability Statement: This manuscript does not report data generation or analysis. Therefore, there are no datasets available for public access. Conflict of interest : The authors report no conflict of interest. The datasets used and analyzed during the current study are available from the cor-responding author on reasonable request. Ethics Approval: Approval for the study was obtained from the Gulhane Education and Research Hospital Ethics Committee on 04 December 2025; approved number: 2025/256. This study was conducted in accordance with the guideli-nes approved by the ethics committee. Informed Consent Statement: Due to the retrospective nature of the study, the Gulhane Education and Research Hospital Ethics Committee waived the need for obta-ining informed consent. References Siegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. CA: A Cancer Journal for Clinicians . 2023;73(1):17-48. doi:10.3322/caac.21763 Cohen HT, McGovern FJ. Renal-cell carcinoma. N Engl J Med . 2005;353(23):2477-2490. doi:10.1056/NEJMra043172 Vamesu S, Ursica OA, Milea SE, et al. Same Organ, Two Cancers: Complete Analysis of Renal Cell Carcinomas and Upper Tract Urothelial Carcinomas. Medicina (Kaunas) . 2024;60(7):1126. doi:10.3390/medicina60071126 Heng DY, Xie W, Regan MM, et al. 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The CANLPH Score, an Integrative Model of Systemic Inflammation and Nutrition Status (SINS), Predicts Clinical Outcomes After Surgery in Renal Cell Carcinoma: Data From a Multicenter Cohort in Japan. Ann Surg Oncol . 2019;26(9):2994-3004. doi:10.1245/s10434-019-07530-5 Youden WJ. Index for rating diagnostic tests. Cancer . 1950;3(1):32-35. doi:10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3 Rebuzzi SE, Signori A, Buti S, et al. Validation of the Meet-URO score in patients with metastatic renal cell carcinoma receiving first-line nivolumab and ipilimumab in the Italian Expanded Access Program. ESMO Open . 2022;7(6):100634. doi:10.1016/j.esmoop.2022.100634 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. 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1","display":"","copyAsset":false,"role":"figure","size":235378,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;See image above for figure legend.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8327307/v1/76421ff987519498ce8bdc85.png"},{"id":100036444,"identity":"808298c7-a343-4e20-9083-b8fc668397c1","added_by":"auto","created_at":"2026-01-12 10:24:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":208672,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;See image above for figure legend.\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-8327307/v1/4566b023d6b5651cca64a44a.png"},{"id":100036334,"identity":"bc54b06d-0281-4c83-8544-08064c8d0d7a","added_by":"auto","created_at":"2026-01-12 10:24:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":296742,"visible":true,"origin":"","legend":"\u003cp\u003e\u0026nbsp;See image above for figure legend.\u003c/p\u003e","description":"","filename":"33.png","url":"https://assets-eu.researchsquare.com/files/rs-8327307/v1/e8d03568080be697dcf36640.png"},{"id":101881745,"identity":"608a0aea-c6df-4634-9252-938e9fcc6c7e","added_by":"auto","created_at":"2026-02-04 15:15:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1430383,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8327307/v1/dd72bd8f-7d53-44ce-823f-88515f210f48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Meet-URO and CANLPH Prognostic Models in Metastatic RCC: Insights From a Single-Institution Cohort Predominantly Treated With TKIs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRenal cell carcinoma (RCC) is a clinically heterogeneous and biologically complex malignancy, accounting for 3%\u0026ndash;5% of all adult cancers and more than 80,000 new cases annually (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Approximately one-quarter of patients present with metastatic disease at diagnosis, reflecting its often silent progression and aggressive behavior (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Clear-cell RCC is the predominant histopathological subtype, comprising nearly 70% of cases, followed by papillary, chromophobe, and collecting duct variants (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). While advances in systemic therapy have significantly improved outcomes, survival remains highly variable, highlighting the continued need for accurate and clinically meaningful prognostic tools.\u003c/p\u003e \u003cp\u003eFor more than a decade, the International Metastatic Renal Cell Carcinoma Database Consortium (IMDC) classification has served as the most widely implemented prognostic system in metastatic RCC (mRCC), incorporating readily available clinical and laboratory parameters (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Developed in the era of VEGF-targeted therapies(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), the IMDC model has guided treatment selection and clinical trial stratification with considerable success. However, the rapid evolution of the therapeutic landscape\u0026mdash;with the introduction of immune checkpoint inhibitors (ICIs), VEGF\u0026ndash;TKI/ICI combinations, and dual immunotherapy\u0026mdash;has revealed important limitations of the IMDC score. Notably, favorable-risk patients do not consistently derive an overall survival (OS) benefit from ICI-based combinations (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) and the benefit of first-line nivolumab plus ipilimumab has been confirmed primarily in intermediate- and poor-risk groups (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). These findings underscore the need for complementary prognostic tools that more accurately reflect contemporary treatment biology.\u003c/p\u003e \u003cp\u003eSystemic inflammation is now recognized as a key determinant of cancer progression and therapeutic resistance across multiple tumor types (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Simple, inexpensive inflammatory markers derived from peripheral blood\u0026mdash;such as neutrophil-to-lymphocyte ratio (NLR), platelet-based indices, and acute-phase reactants\u0026mdash;have demonstrated prognostic value across diverse malignancies and therapeutic settings(\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding upon the IMDC framework and acknowledging the prognostic relevance of systemic inflammation, the Meet-URO score was developed and validated in a cohort of 571 patients receiving second-line nivolumab (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). By incorporating baseline bone metastases and pretreatment NLR (\u0026ge;\u0026thinsp;3.2) into the IMDC criteria, the Meet-URO score demonstrated superior prognostic accuracy, both for patients treated with nivolumab and those receiving later-line cabozantinib (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditional molecular biomarkers such as microsatellite instability (MSI) (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and tumor mutational burden (TMB) (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) have limited relevance in RCC (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e), further emphasizing the need for alternative prognostic tools. Recent studies highlight the key role of systemic inflammation\u0026mdash;particularly C-reactive protein (CRP)\u0026mdash;in shaping outcomes in patients treated with ICIs (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In this context, the CANLPH score has emerged as a promising, inflammation-based prognostic model. This composite index incorporates three systemic markers: the CRP-to-albumin ratio (CAR) (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-hemoglobin ratio (PHR) (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Collectively, these parameters reflect systemic inflammation, nutritional status, and hematologic physiology, offering a comprehensive assessment of host\u0026ndash;tumor interaction.\u003c/p\u003e \u003cp\u003eTaken collectively, the limitations of traditional prognostic systems, the emergence of novel inflammation-based biomarkers, and the diversity of real-world treatment settings highlight the need for models that maintain prognostic performance across different therapeutic contexts. Notably, the distinct survival gradients observed in our cohort\u0026mdash;across both Meet-URO categories and CANLPH inflammatory groups\u0026mdash;suggest that integrating clinical and inflammatory markers may offer more robust and clinically actionable prognostic stratification.\u003c/p\u003e \u003cp\u003eTherefore, the present study was designed to comprehensively evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in patients with metastatic RCC receiving systemic therapy. By assessing these models side-by-side in a real-world cohort, the study aims to determine whether they provide improved and clinically relevant risk discrimination beyond existing frameworks, particularly in settings where access to modern ICI-based combinations remains limited.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThis retrospective, single-center observational study included 112 patients diagnosed with renal cell carcinoma (RCC) who received systemic therapy. Clinical, laboratory, and pathological data at the time of diagnosis were extracted from institutional electronic medical records. Among the full cohort, 56 patients had complete biochemical data required for calculation of the CANLPH score, while 112 patients had sufficient information for assessment of the MEET-URO score. Patients lacking one or more laboratory parameters necessary for CANLPH scoring\u0026mdash;including C-reactive protein (CRP), albumin, neutrophil count, lymphocyte count, platelet count, and hemoglobin\u0026mdash;were excluded from inflammation-based analyses.\u003c/p\u003e \u003cp\u003eAll laboratory tests were obtained prior to the initiation of first-line systemic therapy, ensuring that inflammatory markers reflected pretreatment baseline values. Using these measurements, the CAR, NLR, and PHR were calculated for each patient. These indices were used both for constructing the CANLPH score and for additional exploratory analyses. Specifically, Spearman\u0026rsquo;s rank correlation was applied to evaluate the association of each marker with overall survival (OS) and progression-free survival (PFS) in the available subsets (CAR: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58 for OS and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54 for PFS; NLR: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;104 for OS and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96 for PFS; PHR: \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;104 for OS and \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96 for PFS). One-tailed significance testing was employed based on the predefined hypothesis that elevated systemic inflammation would be associated with poorer clinical outcomes.\u003c/p\u003e \u003cp\u003eDemographic and clinical variables collected included age, sex, histologic subtype, disease stage at diagnosis, and sites of metastasis (bone, liver, lung, central nervous system, lymph nodes, or soft tissue). Treatment-related variables recorded included the number of systemic therapy lines received and the specific first-line regimen administered (sunitinib, pazopanib, cabozantinib, or nivolumab plus cabozantinib).\u003c/p\u003e \u003cp\u003eThe CANLPH score was constructed in accordance with the methodology described by Komura et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Using cut-off values determined by the Youden Index (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), each of the following was assigned one point: CAR\u0026thinsp;\u0026ge;\u0026thinsp;1.5, NLR\u0026thinsp;\u0026ge;\u0026thinsp;2.8, and sex-specific thresholds for PHR (\u0026ge;\u0026thinsp;2.1 for men and \u0026ge;\u0026thinsp;2.3 for women). This yielded a composite inflammation score ranging from 0 to 3 for each patient.\u003c/p\u003e \u003cp\u003eThe Meet-URO score incorporates the presence of bone metastases and baseline neutrophil-to-lymphocyte ratio (NLR)\u0026thinsp;\u0026ge;\u0026thinsp;3.2 into the IMDC score (a web calculator is available here: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://proviso.shinyapps.io/\u003c/span\u003e\u003cspan address=\"https://proviso.shinyapps.io/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e Meet-URO15_score/) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). This scoring system stratifies patients from group 1 (most favorable prognosis) to group 5 (poorest prognosis), with lower- numbered groups associated with longer survival. Previous studies have demonstrated that the Meet-URO model outperforms the IMDC model in patients with mRCC treated with second-line nivolumab or cabozantinib, as well as in those receiving first-line nivolumab plus ipilimumab (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOS and PFS were calculated using standard time-to-event methodology. OS was defined as the interval from diagnosis of metastatic disease to death from any cause or last follow-up. PFS was defined exclusively for first-line systemic therapy as the time from treatment initiation to radiologic or clinical progression or death. Patients without documented progression were censored at their last available disease assessment.\u003c/p\u003e \u003cp\u003eAll statistical analyses were performed using the IBM SPSS Statistics 27.0 (IBM Corp., Armonk, NY, USA) software package. Continuous variables were described as medians (interquartile range (IQR)) and categorical variables as percentages. Survival curves and rates were estimated using the Kaplan\u0026ndash;Meier method. The log-rank test was used to compare the survival outcomes between the groups. All reported p-values were two-sided, and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were regarded as statistically significant..\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 112 patients were included in the analysis. Baseline demographic, pathological, and clinical characteristics are presented in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The cohort comprised 37 females (33.0%) and 75 males (67.0%). Clear cell renal cell carcinoma was the predominant histologic subtype (89.3%), followed by papillary (6.3%), chromophobe (1.8%), and collecting duct carcinoma (1.8%). At diagnosis, the majority of patients presented with advanced disease, with 65.2% classified as Stage IV. The most common metastatic sites were the lung (59.8%) and lymph nodes or soft tissue (60.7%), followed by bone (43.8%), liver (22.3%), and the central nervous system (8.9%). Regarding treatment exposure, 62.5% of patients had received three or more lines of systemic therapy. Sunitinib (48.2%) and pazopanib (45.5%) were the most frequently used first-line regimens, whereas cabozantinib (4.5%) and the nivolumab plus cabozantinib combination (1.8%) were less common (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eSurvival Outcomes According to the MEET-URO Score\u003c/h3\u003e\n\u003cp\u003eSurvival analyses demonstrated a clear separation across MEET-URO risk categories. In the lowest-risk group (score 1), the median OS was 62 months (95% CI: 12.0\u0026ndash;111.9), and PFS was 27 months (95% CI: 0.0\u0026ndash;68.6). Patients with score 2 showed the most favorable OS at 134 months (95% CI: 70.3\u0026ndash;197.7), while PFS was 12 months (95% CI: 3.3\u0026ndash;20.7). In the intermediate-risk group (score 3), OS declined to 50 months (95% CI: 29.9\u0026ndash;70.1) and PFS to 12 months (95% CI: 3.9\u0026ndash;20.1). Higher-risk categories demonstrated progressively poorer outcomes: score 4 was associated with an OS of 28 months (95% CI: 6.1\u0026ndash;49.9) and PFS of 7 months (95% CI: 5.2\u0026ndash;8.8), while the highest-risk category (score 5) showed the shortest survival, with an OS of 7 months (95% CI: 0.0\u0026ndash;15.8) and PFS of 2 months (Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003ch3\u003eSurvival Outcomes According to the CANLPH Score\u003c/h3\u003e\n\u003cp\u003eSimilar stratification was observed when patients were categorized according to the CANLPH score. Patients in the lowest-risk group (score 0) had a median OS of 32 months (95% CI: 27.20\u0026ndash;36.80) and PFS 7 months (95% CI: 0.0\u0026ndash;14.84). The score 1 group demonstrated the longest OS at 60 months (95% CI: 33.91\u0026ndash;86.09), while PFS remained 7 months (95% CI: 4.81\u0026ndash;9.19). For patients in the score 2 group, OS was 47 months (95% CI: 31.16\u0026ndash;62.84) with the longest PFS observed at 13 months (95% CI: 3.40\u0026ndash;22.60). The highest-risk group (score 3) exhibited markedly inferior outcomes, with OS falling to 9 (95% CI: 0.60\u0026ndash;17.40) months and PFS to 3 months (95% CI: 0.60\u0026ndash;5.40) (Fig.\u0026nbsp;2).\u003c/p\u003e\n\u003ch3\u003eCorrelation Between Inflammatory Markers and Survival Outcomes\u003c/h3\u003e\n\u003cp\u003eSpearman\u0026rsquo;s correlation analysis revealed heterogeneous associations between the inflammatory biomarkers and survival endpoints (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). CAR demonstrated significant negative correlations with both OS (ρ = \u0026minus;\u0026thinsp;0.380, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;58) and PFS (ρ = \u0026minus;\u0026thinsp;0.376, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;54), indicating that higher baseline CAR values were associated with shorter survival. In contrast, NLR was not significantly correlated with OS (ρ\u0026thinsp;=\u0026thinsp;0.041, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.341, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;104) or PFS (ρ = \u0026minus;\u0026thinsp;0.149, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.074, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96). PHR demonstrated a weak but statistically significant negative correlation with OS (ρ = \u0026minus;\u0026thinsp;0.179, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.035, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;104), while its relationship with PFS did not reach significance (ρ = \u0026minus;\u0026thinsp;0.047, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.324, \u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;96). These findings are illustrated in (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Clinical Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistology\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClear cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e89.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePapillary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChromophobe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollecting duct\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStage at diagnosis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e65.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetastasis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCNS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLN / Soft Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSystemic therapy lines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e37.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFirst-line treatment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSunitinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePazopanib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCabozantinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNivolumab\u0026thinsp;+\u0026thinsp;Cabozantinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations of CAR, NLR, and PHR with survival outcomes based on Spearman\u0026rsquo;s rho coefficients.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflammatory Marker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpearman\u0026rsquo;s rho\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSignificance (1-tailed)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNLR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe management of mRCC continues to evolve rapidly, driven by advances in systemic therapies and an improved understanding of tumor biology. Prognostic stratification remains essential for optimizing treatment selection, designing clinical trials, and counseling patients about expected outcomes. Historically, the IMDC score has served as the standard prognostic model for patients receiving VEGF-targeted therapies (\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). However, with the emergence of ICIs, VEGF\u0026ndash;TKI/ICI combinations, and dual checkpoint blockade, the limitations of the IMDC system have become increasingly pronounced. Multiple studies have demonstrated that patients classified as favorable-risk by IMDC may not derive a clear OS benefit from ICI-based combinations (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Additionally, the confirmed benefit of nivolumab plus ipilimumab in the CheckMate-214 trial was predominantly restricted to intermediate- and poor-risk groups (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), highlighting the need for refined prognostic tools capable of reflecting the biology and treatment response patterns of the modern therapeutic landscape.\u003c/p\u003e \u003cp\u003eThe Meet-URO score was developed to address some of these gaps by integrating two additional variables\u0026mdash;baseline bone metastases and pretreatment NLR\u0026mdash;into the traditional IMDC framework (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Both variables have strong biological rationale: bone metastases reflect aggressive disease biology and niche-mediated tumor support, while NLR represents systemic inflammation and dysregulated immunity, both of which are known to influence response to immunotherapy (\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Validation studies across multiple cohorts have demonstrated that Meet-URO outperforms the IMDC model in predicting OS and PFS in patients treated with second-line nivolumab, cabozantinib, and even first-line nivolumab plus ipilimumab in expanded-access programs (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). These results suggest that Meet-URO offers broader prognostic applicability across multiple treatment lines and therapeutic classes.\u003c/p\u003e \u003cp\u003eOur findings strongly align with the existing literature and further validate the prognostic performance of the Meet-URO scoring system. A clear and clinically meaningful separation in survival outcomes was observed across all five Meet-URO categories. Patients in score group 2\u0026mdash;representing the most favorable profile within the non\u0026ndash;IMDC favorable-risk population\u0026mdash;experienced the longest survival, whereas outcomes declined progressively toward the highest-risk group (score 5), which demonstrated markedly inferior OS and PFS. The preservation of this stratification effect in our patient population is particularly noteworthy, given the distinctive treatment patterns in our cohort.\u003c/p\u003e \u003cp\u003eA distinctive aspect of our study is the real-world therapeutic context in which these prognostic systems were evaluated. Unlike high-income countries where ICI\u0026ndash;TKI combinations constitute the standard first-line therapy, the vast majority of our patients received single-agent VEGFR-TKIs (mainly sunitinib or pazopanib) in the first-line setting. Limited national access to immunotherapy, financial constraints, and reimbursement restrictions\u0026mdash;all common features of developing healthcare systems\u0026mdash;loom as major determinants of treatment choice. Despite these constraints, Meet-URO maintained its strong discriminatory performance, reinforcing the robustness and generalizability of this scoring system beyond immunotherapy-rich environments. This finding has meaningful implications for global oncology, as it supports the utility of Meet-URO in diverse socioeconomic and therapeutic settings.\u003c/p\u003e \u003cp\u003eAlongside the Meet-URO score, the CANLPH score also demonstrated significant prognostic relevance in our cohort. Inflammation and nutrition-related biomarkers have increasingly gained attention as prognostic tools in cancer due to their correlation with tumor progression, host immune response, and treatment resistance (\u003cspan additionalcitationids=\"CR22 CR23\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). The CANLPH model incorporates three readily available laboratory measures\u0026mdash;CAR, NLR, and PHR\u0026mdash;that together capture systemic inflammation, nutritional status, and hematologic physiology. Consistent with previous findings by Komura et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), we observed that higher CANLPH scores were associated with progressively shorter OS and PFS. CANLPH 0\u0026ndash;1 groups exhibited more favorable outcomes, whereas CANLPH 3\u0026mdash;the highest inflammatory burden\u0026mdash;was associated with a dramatically reduced survival.\u003c/p\u003e \u003cp\u003eAmong the individual biomarkers, CAR emerged as the strongest predictor of both OS and PFS. Elevated CAR reflects increased CRP (a marker of cytokine-driven inflammation) combined with reduced albumin (a surrogate for malnutrition and systemic metabolic stress). This dual representation of inflammation and nutritional decline has been shown to predict poor outcomes in numerous malignancies and across treatment modalities (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Meanwhile, NLR and PHR showed weaker and more heterogeneous associations with survival, which may reflect their sensitivity to transient physiological changes or heterogeneous disease dynamics. Nonetheless, when integrated into the CANLPH model, these markers collectively produced a robust stratification pattern, supporting the clinical value of composite inflammatory scoring systems.\u003c/p\u003e \u003cp\u003eThe complementary prognostic performance of Meet-URO and CANLPH in our study highlights the relevance of integrating clinical, metastatic, and inflammatory characteristics into contemporary prognostic assessment. While Meet-URO incorporates tumor burden and immune-inflammatory interactions through bone metastases and NLR, CANLPH focuses more specifically on systemic inflammation and nutritional physiology. The strong prognostic gradients observed with both models underscore the multidimensional nature of mRCC biology and the potential advantage of utilizing more than one scoring system to achieve precise prognostication.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that must be considered.\u003c/p\u003e \u003cp\u003eFirst, the retrospective and single-center design increases the potential for selection and information bias.\u003c/p\u003e \u003cp\u003eSecond, although 112 patients were included overall, only 56 had complete biochemical data required for CANLPH scoring, limiting the power of inflammatory biomarker analyses.\u003c/p\u003e \u003cp\u003eThird, treatment heterogeneity\u0026mdash;including predominant first-line TKI monotherapy due to restricted access to ICI-based combinations\u0026mdash;may affect survival outcomes and limit comparability with international cohorts.\u003c/p\u003e \u003cp\u003eFinally, the absence of external validation limits the generalizability of our findings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eFuture Directions\u003c/h3\u003e\n\u003cp\u003eFuture research should incorporate multicenter prospective studies involving larger patient populations and including those treated with modern first-line ICI\u0026ndash;TKI combinations or dual checkpoint inhibitor regimens. Evaluating dynamic changes in inflammation-related biomarkers during treatment could provide additional prognostic information and support adaptive therapeutic strategies. Integrating established clinical models such as IMDC and Meet-URO with inflammatory or nutritional indices like CANLPH, as well as emerging biomarkers\u0026mdash;radiomics, circulating tumor DNA, cytokine signatures, and machine-learning\u0026ndash;based risk calculators\u0026mdash;may further refine prognostic precision. Given the global disparities in access to immunotherapy, additional real-world studies from low- and middle-income countries are essential to ensure broad applicability and equity in prognostic assessment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our findings demonstrate that both the Meet-URO and CANLPH scoring systems provide strong and independent prognostic information in metastatic RCC, even in a real-world population in which most patients received TKI monotherapy rather than modern immunotherapy-based combinations. Meet-URO effectively stratified risk across all five categories, while CANLPH distinguished meaningful differences in survival based on systemic inflammation and nutritional status. These results highlight the complementary nature of clinical and inflammatory prognostic models and support their integration into routine risk assessment, particularly in resource-limited settings. Further prospective validation in diverse treatment landscapes is warranted to refine and extend their clinical utility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by \u0026Ouml;mer Faruk Kuzu, Nuri Karadurmuş, Nebi Batuhan Kanat, Dilruba İlayda \u0026Ouml;zel Bozbağ, Berkan Karadurmuş, Esmanur Kaplan T\u0026uuml;z\u0026uuml;n, H\u0026uuml;seyin Atacan, Nurlan Mammadzada, Gizem Yıldırım, Musa Barış Aykan,\u003csup\u003e\u0026nbsp;\u003c/sup\u003eİsmail Ert\u0026uuml;rk. The first draft of the manuscript was written by \u0026Ouml;mer Faruk Kuzu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u0026nbsp;\u003c/strong\u003eThis manuscript does not report data generation or analysis. Therefore, there are no datasets available for public access.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e: The authors report no conflict of interest. The datasets used and analyzed during the current study are available from the cor-responding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval:\u0026nbsp;\u003c/strong\u003eApproval for the study was obtained from the Gulhane Education and Research Hospital Ethics Committee on 04 December 2025; approved number: 2025/256. This study was conducted in accordance with the guideli-nes approved by the ethics committee.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Due to the retrospective nature of the study, the Gulhane Education and Research Hospital Ethics Committee waived the need for obta-ining informed consent.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, Jemal A. Cancer statistics, 2023. \u003cem\u003eCA: A Cancer Journal for Clinicians\u003c/em\u003e. 2023;73(1):17-48. doi:10.3322/caac.21763\u003c/li\u003e\n\u003cli\u003eCohen HT, McGovern FJ. Renal-cell carcinoma. \u003cem\u003eN Engl J Med\u003c/em\u003e. 2005;353(23):2477-2490. doi:10.1056/NEJMra043172\u003c/li\u003e\n\u003cli\u003eVamesu S, Ursica OA, Milea SE, et al. 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The interplay between cholesterol (and other metabolic conditions) and immune-checkpoint immunotherapy: shifting the concept from the \u0026ldquo;inflamed tumor\u0026rdquo; to the \u0026ldquo;inflamed patient.\u0026rdquo; \u003cem\u003eHum Vaccin Immunother\u003c/em\u003e. 2021;17(7):1930-1934. doi:10.1080/21645515.2020.1852872\u003c/li\u003e\n\u003cli\u003eTempleton AJ, McNamara MG, \u0026Scaron;eruga B, et al. Prognostic role of neutrophil-to-lymphocyte ratio in solid tumors: a systematic review and meta-analysis. \u003cem\u003eJ Natl Cancer Inst\u003c/em\u003e. 2014;106(6):dju124. doi:10.1093/jnci/dju124\u003c/li\u003e\n\u003cli\u003eTempleton AJ, Ace O, McNamara MG, et al. Prognostic role of platelet to lymphocyte ratio in solid tumors: a systematic review and meta-analysis. \u003cem\u003eCancer Epidemiol Biomarkers Prev\u003c/em\u003e. 2014;23(7):1204-1212. doi:10.1158/1055-9965.EPI-14-0146\u003c/li\u003e\n\u003cli\u003eKumarasamy C, Sabarimurugan S, Madurantakam RM, et al. Prognostic significance of blood inflammatory biomarkers NLR, PLR, and LMR in cancer-A protocol for systematic review and meta-analysis. \u003cem\u003eMedicine (Baltimore)\u003c/em\u003e. 2019;98(24):e14834. doi:10.1097/MD.0000000000014834\u003c/li\u003e\n\u003cli\u003eShao Y, Wu B, Jia W, Zhang Z, Chen Q, Wang D. Prognostic value of pretreatment neutrophil-to-lymphocyte ratio in renal cell carcinoma: a systematic review and meta-analysis. \u003cem\u003eBMC Urol\u003c/em\u003e. 2020;20(1):90. doi:10.1186/s12894-020-00665-8\u003c/li\u003e\n\u003cli\u003eBrighi N, Farolfi A, Conteduca V, et al. The Interplay between Inflammation, Anti-Angiogenic Agents, and Immune Checkpoint Inhibitors: Perspectives for Renal Cell Cancer Treatment. \u003cem\u003eCancers (Basel)\u003c/em\u003e. 2019;11(12):1935. doi:10.3390/cancers11121935\u003c/li\u003e\n\u003cli\u003eInflammatory indices and clinical factors in metastatic renal cell carcinoma patients treated with nivolumab: the development of a novel prognostic score (Meet-URO 15 study) - Sara Elena Rebuzzi, Alessio Signori, Giuseppe Luigi Banna, Marco Maruzzo, Ugo De Giorgi, Paolo Pedrazzoli, Andrea Sbrana, Paolo Andrea Zucali, Cristina Masini, Emanuele Naglieri, Giuseppe Procopio, Sara Merler, Laura Tomasello, Lucia Fratino, Cinzia Baldessari, Riccardo Ricotta, Stefano Panni, Veronica Mollica, Mariella Sorar\u0026ugrave;, Matteo Santoni, Alessio Cortellini, Veronica Prati, Hector Jos\u0026egrave; Soto Parra, Marco Stellato, Francesco Atzori, Sandro Pignata, Carlo Messina, Marco Messina, Franco Morelli, Giuseppe Prati, Franco Nol\u0026egrave;, Francesca Vignani, Alessia Cavo, Giandomenico Roviello, Francesco Pierantoni, Chiara Casadei, Melissa Bersanelli, Silvia Chiellino, Federico Paolieri, Matteo Perrino, Matteo Brunelli, Roberto Iacovelli, Camillo Porta, Sebastiano Buti, Giuseppe Fornarini, 2021. Accessed November 20, 2025. https://journals.sagepub.com/doi/full/10.1177/17588359211019642\u003c/li\u003e\n\u003cli\u003eRebuzzi SE, Cerbone L, Signori A, et al. Application of the Meet-URO score to metastatic renal cell carcinoma patients treated with second- and third-line cabozantinib. \u003cem\u003eTher Adv Med Oncol\u003c/em\u003e. 2022;14:17588359221079580. doi:10.1177/17588359221079580\u003c/li\u003e\n\u003cli\u003eWilbur HC, Le DT, Agarwal P. Immunotherapy of MSI Cancer: Facts and Hopes. \u003cem\u003eClin Cancer Res\u003c/em\u003e. 2024;30(8):1438-1447. doi:10.1158/1078-0432.CCR-21-1935\u003c/li\u003e\n\u003cli\u003eTurajlic S, Litchfield K, Xu H, et al. Insertion-and-deletion-derived tumour-specific neoantigens and the immunogenic phenotype: a pan-cancer analysis. \u003cem\u003eLancet Oncol\u003c/em\u003e. 2017;18(8):1009-1021. doi:10.1016/S1470-2045(17)30516-8\u003c/li\u003e\n\u003cli\u003eMotzer RJ, Choueiri TK, McDermott DF, et al. 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The International Metastatic Renal Cell Carcinoma Database Consortium model as a prognostic tool in patients with metastatic renal cell carcinoma previously treated with first-line targeted therapy: a population-based study. \u003cem\u003eLancet Oncol\u003c/em\u003e. 2015;16(3):293-300. doi:10.1016/S1470-2045(14)71222-7\u003c/li\u003e\n\u003cli\u003eKomura K, Hashimoto T, Tsujino T, et al. The CANLPH Score, an Integrative Model of Systemic Inflammation and Nutrition Status (SINS), Predicts Clinical Outcomes After Surgery in Renal Cell Carcinoma: Data From a Multicenter Cohort in Japan. \u003cem\u003eAnn Surg Oncol\u003c/em\u003e. 2019;26(9):2994-3004. doi:10.1245/s10434-019-07530-5\u003c/li\u003e\n\u003cli\u003eYouden WJ. Index for rating diagnostic tests. \u003cem\u003eCancer\u003c/em\u003e. 1950;3(1):32-35. doi:10.1002/1097-0142(1950)3:1%3C32::AID-CNCR2820030106%3E3.0.CO;2-3\u003c/li\u003e\n\u003cli\u003eRebuzzi SE, Signori A, Buti S, et al. Validation of the Meet-URO score in patients with metastatic renal cell carcinoma receiving first-line nivolumab and ipilimumab in the Italian Expanded Access Program. \u003cem\u003eESMO Open\u003c/em\u003e. 2022;7(6):100634. doi:10.1016/j.esmoop.2022.100634 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metastatic renal cell carcinoma, Meet-URO score, CANLPH score, systemic inflammation, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8327307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8327307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cb\u003eBackground\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAccurate prognostic assessment remains crucial in metastatic renal cell carcinoma (mRCC), especially as treatment options have expanded beyond vascular endothelial growth factor (VEGF)\u0026ndash;targeted therapies to include immune checkpoint inhibitors (ICIs) and ICI\u0026ndash;TKI combinations. The widely used IMDC classification shows important limitations in the modern therapeutic era, highlighting the need for complementary prognostic tools. In this context, the Meet-URO and CANLPH scores\u0026mdash;incorporating clinical, inflammatory, and nutritional markers have emerged as promising alternatives.\u003c/p\u003e \u003cp\u003e \u003cb\u003eObjective\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo evaluate and compare the prognostic performance of the Meet-URO and CANLPH scoring systems in a real-world mRCC cohort predominantly treated with first-line tyrosine kinase inhibitor (TKI) monotherapy due to limited access to ICI-based combinations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMethods\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThis retrospective single-center study included 112 patients with mRCC. The Meet-URO score was calculated for all patients, while the CANLPH score was assessed in 56 patients with complete laboratory data. CAR, NLR, and PHR were computed using baseline pre-treatment measurements. Overall survival (OS) and progression-free survival (PFS) the latter defined exclusively for first-line therapy\u0026mdash;were estimated using the Kaplan\u0026ndash;Meier method. Correlations between inflammatory markers and survival outcomes were analyzed using Spearman\u0026rsquo;s rho.\u003c/p\u003e \u003cp\u003e \u003cb\u003eResults\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMeet-URO demonstrated clear prognostic stratification across all five categories, with the most favorable outcomes in score group 2 and progressively poorer OS and PFS in higher-risk groups. CANLPH also showed meaningful survival discrimination, with the highest inflammatory group (score 3) exhibiting markedly reduced OS and PFS. CAR was the strongest individual predictor of survival, while NLR and PHR showed weaker associations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConclusion\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBoth Meet-URO and CANLPH provide strong, complementary prognostic information in mRCC, even in a cohort largely treated with TKI monotherapy. Their integration into routine risk assessment may enhance clinical decision-making, particularly in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Assessment of Meet-URO and CANLPH Prognostic Models in Metastatic RCC: Insights From a Single-Institution Cohort Predominantly Treated With TKIs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-12 10:23:36","doi":"10.21203/rs.3.rs-8327307/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":"9d754f96-901d-424f-b267-693b90d29968","owner":[],"postedDate":"January 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T09:28:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-12 10:23:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8327307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8327307","identity":"rs-8327307","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}