A Novel Hemogram-Based Method for More Accurate Preoperative Prediction of Tumor Aggressiveness in Bladder Cancer: A Retrospective Cohort Study

A Novel Hemogram-Based Method for More Accurate Preoperative Prediction of Tumor Aggressiveness in Bladder Cancer: A Retrospective Cohort Study | 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 A Novel Hemogram-Based Method for More Accurate Preoperative Prediction of Tumor Aggressiveness in Bladder Cancer: A Retrospective Cohort Study Murat Uçar, Nureddin Raym, Uğur Soy, Erkan Karadağ, Murat Topçuoğlu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9342974/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Purpose To evaluate the prognostic value of preoperative hemoglobin-lymphocyte-neutrophil ratio (HLNR) and hemoglobin-lymphocyte-platelet ratio (HLPR) for predicting tumor recurrence at control cystoscopy in patients with non-muscle invasive bladder cancer (NMIBC) and to compare these indices with lymphocyte-neutrophil ratio (LNR) and platelet-lymphocyte ratio (PLR). Methods We retrospectively analyzed 180 patients who underwent transurethral resection for pathological Ta/T1 urothelial bladder tumors. ROC curve analysis was used to assess the predictive performance of each index, and factors associated with recurrence were evaluated using univariate and multivariate logistic regression. An exploratory composite risk score integrating European Organisation for Research and Treatment of Cancer (EORTC)-like clinicopathological factors with HLNR was developed. Results HLNR showed the highest predictive accuracy with an AUC of 0.75 (95% CI: 0.68–0.82), whereas HLPR, LNR, and PLR had AUC values of 0.65, 0.61, and 0.58, respectively. An optimal cut-off of ≥ 2.0 for HLNR yielded 72% sensitivity and 70% specificity (p < 0.001), and high HLNR (≥ 2.0) remained independently associated with a lower risk of recurrence in multivariate analysis (OR 0.34; 95% CI: 0.18–0.63; p < 0.001). Although high HLPR (≥ 0.45) was protective in univariate analysis (OR 0.55; p = 0.031), it did not retain independent significance in the multivariate model (p = 0.209). Conclusion Preoperative HLNR is significantly and independently associated with tumor recurrence at control cystoscopy in patients with NMIBC. Integration of HLNR into EORTC-like risk models appears promising, but its prognostic value should be validated in prospective, multicenter studies. Hematologic tests Prognosis Transitional Cell Carcinoma Urinary Bladder Neoplasms Figures Figure 1 Figure 2 1. Introductıon Bladder cancer accounts for approximately 3% of all cancer diagnoses worldwide. Urothelial carcinoma represents the most common histological subtype, comprising nearly 95% of cases [ 1 ]. At initial presentation, approximately 75% of patients are diagnosed with non–muscle-invasive bladder cancer (NMIBC), limited to the mucosal and submucosal layers, whereas the remaining 25% present with muscle-invasive bladder cancer (MIBC), which is characterized by muscularis propria tissue infiltration. Patients with MIBC generally exhibit a significantly poorer prognosis compared with those with NMIBC [ 2 ]. Despite significant advances in surgical management and adjuvant therapies, NMIBC remains characterized by high recurrence and progression rates. Over the course of follow-up, approximately 70% of patients experience disease recurrence, while nearly 30% eventually progress to MIBC [ 3 ]. Current risk stratification models remain inadequate, underscoring the need for reliable prognostic biomarkers to improve treatment decisions and facilitate individualized patient management. The tumor microenvironment plays a pivotal role in the initiation, progression, and therapeutic resistance of many malignancies, including bladder cancer. Specifically, immune and inflammatory cells such as neutrophils, lymphocytes, monocytes, and platelets are integral components of this microenvironment. Depending on their activation state and localization, these cells may exert either tumor-promoting or tumor-suppressing effects. [ 1 ]. Among systemic inflammatory response (SIR) indicators, the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have emerged as significant due to their practicality, low cost, and prognostic value across a wide range of solid tumors [ 4 ]. These indices reflect the dynamic interplay between proinflammatory responses and antitumor immunity, thereby offering critical insights into tumor behavior. In particular, elevated NLR values have been associated with more aggressive tumor behavior and adverse pathological features [ 5 ]. The prognostic value of preoperative systemic inflammatory markers in bladder cancer has been extensively documented [ 6 , 7 ]. In particular, NLR and PLR have been associated with tumor grade, stage, and recurrence. These parameters are readily accessible through routine complete blood counts, making them non-invasive and cost-effective biomarkers. However, conventional risk models are primarily based on clinicopathological features and often fail to reflect the host immune response. In this context, inflammation-based indices such as NLR and PLR may facilitate the early identification of high-risk patients and assist in the development of personalized therapeutic strategies [ 8 ]. In parallel with the growing interest in inflammation-based prognostic markers, recent years have seen the emergence of novel hematological indices that integrate standard blood parameters in innovative ways. Uçar et al. [ 9 ] introduced the hemoglobin × lymphocyte/neutrophil ratio (HLNR) and demonstrated its potential utility in predicting tumor aggressiveness among patients with renal cell carcinoma. Their findings revealed significant associations between this index and adverse pathological features such as higher tumor stage, nuclear grade, and necrosis. Building upon this, the present study aims to investigate the prognostic significance of preoperative hemogram parameters—particularly the value of HLNR, as described in Uçar’s work—in the context of bladder cancer [ 9 ]. By applying this novel hematological marker to urothelial carcinoma, this research holds the potential to more accurately predict tumor behavior and develop risk assessment models that are more individualized and biologically informative 2. Materials and Methods 2.1.Study Design and Patient Selection This single-center, retrospective cohort study included consecutive patients who underwent transurethral resection of bladder (TUR-B) tumors between February 2016 and February 2025 at our institution. A total of 180 patients who underwent TUR-B were included (Figure 1). Pathology reports were reviewed to identify patients with non-muscle invasive urothelial carcinoma (stage Ta or T1). The study was conducted in accordance with the 1964 Declaration of Helsinki and approved by the Institutional Clinical Research Ethics Committee (Approval No: 10354421-2025/03-17). Inclusion criteria: Pathological stage Ta or T1 Histologically confirmed urothelial carcinoma Comprehensive preoperative hematological and biochemical profiles (hemoglobin, white blood cell count, platelet count, platelet distribution width [PDW], mean platelet volume [MPV], neutrophil count, lymphocyte count, red cell distribution width [RDW], urea, creatinine) Available control cystoscopy findings and follow-up data Exclusion criteria: Muscle-invasive disease (Stage ≥T2) or non-urothelial histology. Incomplete primary endpoint data (e.g., missing recurrence status). Missing laboratory values preventing the calculation of inflammatory indices. Follow-up duration shorter than 3 months (loss to follow-up). All personal identifiers (national ID number, name, surname) were removed from the dataset prior to analysis to ensure anonymity. 2.2. Data Collection and Hematological Parameters Demographic data, including age, sex, pathological stage and grade, and the presence of recurrence, were obtained from medical records. Preoperative peripheral blood samples collected within one week prior to surgery were analyzed using an automated hematology analyzer. Hemoglobin (Hb) levels, neutrophil count, lymphocyte count and platelet count were recorded. Based on these values, the following ratios and indices were calculated: lymphocyte-to-neutrophil ratio (LNR) platelet-to-lymphocyte ratio (PLR), hemoglobin × lymphocyte/neutrophil ratio (HLNR), and hemoglobin × platelet/lymphocyte ratio (HLPR). The HLNR and HLPR formula was originally described by Uçar et al. [9], who demonstrated its potential utility in predicting tumor aggressiveness in patients with renal cell carcinoma. 2.3. Pathological Evaluation Tumor staging was performed according to the TNM classification system, while grading was based on the World Health Organization (WHO) criteria [10]. Bladder tumors were classified as non–muscle-invasive (Ta, T1) or muscle-invasive (≥T2). Pathological assessment, including the distinction between high-grade and low-grade disease, was conducted by specialized uropathologists blinded to the hematological data. 2.4. Statistical Analysis Statistical analyses were performed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA) and R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria). 2.4.1. Descriptive statistics Continuous variables were summarized as mean ± standard deviation (SD) or median (interquartile range [IQR]) depending on distribution, assessed by the Shapiro-Wilk test and histograms. Categorical variables were presented as number and percentage [n (%)]. 2.4.2. Group comparisons Patients with and without recurrence were compared using Student's t-test or Mann-Whitney U test for continuous variables, and chi-square or Fisher's exact test for categorical variables. 2.4.3. ROC curve analysis The discriminative ability of HLNR, HLPR, LNR, and PLR for predicting recurrence was evaluated by receiver operating characteristic (ROC) curve analysis. Area under the curve (AUC) values with 95% confidence intervals (CI) were calculated. Optimal cut-off values were determined using the Youden index (sensitivity + specificity − 1). Based on these cut-offs, each index was dichotomized into "low" and "high" categories. 2.4.4. Logistic regression analysis Univariate logistic regression was performed to identify potential predictors of recurrence. Variables with p<0.10 in univariate analysis and clinically relevant covariates (age, sex, tumor multiplicity, tumor size ≥30 mm, stage T1, high grade, CIS, LVI) were entered into multivariate logistic regression models. HLNR and HLPR were included as mandatory covariates in the primary model; LNR and PLR were evaluated in separate exploratory models to avoid collinearity. Odds ratios (OR) with 95% CI were reported. Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test. 2.4.5. Exploratory composite risk score A simple additive risk score integrating European Organisation for Research and Treatment of Cancer (EORTC)-like clinicopathological factors and HLNR was constructed. Points were assigned as follows: multiple tumors (1 point), tumor size ≥30 mm (1 point), stage T1 (1 point), high grade (1 point), CIS present (1 point), and HLNR <2.0 (2 points). Total scores ranged from 0 to 7. Patients were categorized into three risk groups: low (0–2 points), intermediate (3–5 points), and high (6–8 points if HLPR <0.45 was added as 1 point). Recurrence rates were compared across risk categories. 2.4.6. Sensitivity analyses The primary analyses were repeated in subgroups of patients with at least 6 months and at least 12 months of follow-up to assess the robustness of findings. All tests were two-sided, and p<0.05 was considered statistically significant. 3. Results 3.1. Patient Characteristics A total of 180 patients met the inclusion criteria. The cohort’s mean age was 65.8 ± 11.2 years, with a predominant male representation (n=163, 90.6%). Smoking history was present in 114 (63.3%) patients. At initial diagnosis, 99 (55.0%) patients had solitary tumors and 81 (45.0%) had multiple tumors. Median tumor size was 30 mm (IQR 20–35). All patients had Ta or T1 stage disease; 57 (31.7%) were low-grade and 123 (68.3%) were high-grade. Concomitant CIS was present in 29 (16.1%) patients, and lymphovascular invasion was observed in 5 (2.8%). Median follow-up duration was 18 months (IQR 6–38). During follow-up, 67 (37.2%) patients developed recurrence at control cystoscopy, whereas 113 (62.8%) remained recurrence-free. Baseline demographic and tumor characteristics, stratified by recurrence status, are presented in Table 1. Patients with recurrence were more likely to have multiple tumors (p=0.028) and had longer follow-up duration (p=0.022), reflecting the time-dependent nature of recurrence detection. 3.2. Hematologic Parameters and Indices Preoperative hematologic parameters and composite indices are summarized in Table 2. Patients who developed recurrence had significantly lower hemoglobin (12.9 vs. 13.8 g/dL; p=0.004), higher neutrophil counts (5.0 vs. 4.4 ×10⁹/L; p=0.021), and lower lymphocyte counts (1.5 vs. 1.9 ×10⁹/L; p<0.001) compared to those without recurrence. Median HLNR was significantly lower in the recurrence group (1.48 vs. 2.25; p<0.001), as was HLPR (0.44 vs. 0.62; p=0.018). LNR showed a borderline significant difference (p=0.041), while PLR did not reach statistical significance (p=0.092). 3.3. ROC Curve Analysis ROC curve analysis was performed to evaluate the discriminative ability of each hematologic index for predicting recurrence (Table 3). HLNR demonstrated the highest predictive accuracy (AUC 0.75; 95% CI: 0.68–0.82; p<0.001), significantly outperforming HLPR (AUC 0.65), LNR (AUC 0.61), and PLR (AUC 0.58). The optimal HLNR cut-off of ≥2.0 yielded 72% sensitivity and 70% specificity. For HLPR, the optimal cut-off of ≥0.45 had 61% sensitivity and 62% specificity (p=0.018). Neither LNR nor PLR achieved robust discriminative performance. ROC curves for HLNR, HLPR, LNR and PLR are presented in Figure 2, illustrating the superior discriminative performance of HLNR compared with the other indices. 3.4. Univariate and Multivariate Logistic Regression Univariate and multivariate logistic regression analyses for predictors of tumor recurrence are shown in Table 4. In univariate analysis, multiple tumors (OR 2.25; p=0.005), tumor size ≥30 mm (OR 2.62; p=0.001), stage T1 (OR 1.89; p=0.034), high HLNR ≥2.0 (OR 0.29; p<0.001, protective), and high HLPR ≥0.45 (OR 0.55; p=0.031, protective) were significantly associated with recurrence. In multivariate analysis adjusting for age, sex, smoking, tumor characteristics, and both HLNR and HLPR, multiple tumors (adjusted OR 1.98; 95% CI: 1.06–3.71; p=0.032) and tumor size ≥30 mm (adjusted OR 2.21; 95% CI: 1.15–4.26; p=0.018) were identified as independent predictors for recurrence. High HLNR (≥2.0) was independently associated with a significantly lower risk of recurrence (adjusted OR 0.34; 95% CI: 0.18–0.63; p<0.001), indicating a strong protective effect. HLPR lost statistical significance in the multivariate model (adjusted OR 0.68; p=0.209). LNR and PLR were not independently predictive in separate exploratory models. The Hosmer-Lemeshow test indicated good model fit (p=0.52). 3.5. Exploratory Composite Risk Score An exploratory risk score integrating EORTC-like clinicopathological variables (multiple tumors, tumor size ≥30 mm, stage T1, high grade, CIS) and HLNR (<2.0 = 2 points) was constructed, yielding a 0–7 point scale. Patients were stratified into low (0–2 points), intermediate (3–5 points), and high (6–7 points) risk categories. Recurrence rates increased stepwise across risk groups: 18.2% in low-risk, 42.5% in intermediate-risk, and 68.4% in high-risk patients (p for trend <0.001). This exploratory score demonstrated improved discrimination compared to purely clinical scores without HLNR (AUC 0.78 vs. 0.68; p=0.012). 3.6. Sensitivity Analyses When analyses were restricted to patients with at least 6 months of follow-up (n=154) and at least 12 months of follow-up (n=128), the direction and magnitude of associations remained consistent. HLNR retained independent prognostic significance in both subgroups (6-month cohort: adjusted OR 0.36, p=0.001; 12-month cohort: adjusted OR 0.31, p<0.001), confirming the robustness of findings. 4. Discussion This retrospective cohort study demonstrates that preoperative HLNR is a significant and independent predictor of tumor recurrence at control cystoscopy in patients with NMIBC, outperforming HLPR, LNR, and PLR. Patients with HLNR < 2.0 had approximately three times higher odds of recurrence compared to those with HLNR ≥ 2.0, even after adjusting for established clinicopathological risk factors including tumor multiplicity, size, stage, grade, CIS, and lymphovascular invasion. These findings support the incorporation of HLNR into existing risk stratification frameworks and underscore the value of readily available hematologic parameters as prognostic biomarkers in NMIBC. Our findings align with prior reports emphasizing the prognostic role of hematologic indices in NMIBC and other urologic malignancies. Zhao and colleagues demonstrated that a neutrophil-hemoglobin-lymphocyte (NHL) composite ratio significantly predicted postoperative recurrence in NMIBC, consistent with our observation that HLNR—a similar composite index—is strongly associated with recurrence [ 6 , 11 ]. Tang et al. [ 12 ] reported that HPR (hemoglobin-platelet ratio) was a weak predictor of recurrence (non-significant for recurrence-free survival) but strongly predicted progression-free survival, overall survival, and cancer-specific survival in patients with high-risk NMIBC and muscle-invasive bladder cancer. Our study corroborates this pattern: HLPR showed only borderline significance in univariate analysis and lost independent predictive value in multivariate models for recurrence, suggesting that HLPR may be more relevant for long-term outcomes such as progression and mortality rather than early recurrence. The HALP score (hemoglobin × albumin × lymphocyte / platelet) and its derivative, HALPA (HALP combined with ASA grade), have been validated in radical cystectomy cohorts, where they distinguished tumor stage and independently predicted overall survival [ 13 ]. While we did not calculate HALP due to the absence of albumin data in our dataset, our exploratory EORTC-integrated HLNR score conceptually parallels the HALPA approach by combining tumor-related factors with an inflammation-immune index, yielding stepwise risk stratification. The strong prognostic value of HLNR likely reflects its ability to capture multiple aspects of tumor biology and host response. Anemia, resulting from chronic blood loss, iron deficiency, or cytokine-mediated suppression of erythropoiesis (elevated IL-6, TNF-α), is associated with tissue hypoxia, upregulation of HIF-1α, and enhanced angiogenesis via VEGF secretion, all of which promote tumor invasiveness and metastatic potential [ 14 ]. Thrombocytosis, driven by IL-6 and GATA-2 overexpression, facilitates platelet-mediated tumor cell adhesion, protects circulating tumor cells from NK cell attack, and supports endothelial attachment and extravasation. Lymphopenia reflects impaired immune surveillance, with reduced cytotoxic T-cell activity enabling tumor proliferation and migration. Conversely, relative neutrophilia contributes to a pro-inflammatory milieu that may favor tumor progression. HLNR integrates hemoglobin, neutrophil, and lymphocyte values, thereby providing a composite measure of anemia, systemic inflammation, and immune dysfunction—three interconnected processes central to cancer progression [ 15 ]. From a clinical perspective, HLNR offers several advantages: it is derived from routine preoperative complete blood counts, incurs no additional cost, and can be calculated immediately. Our ROC-derived cut-off of ≥ 2.0 yielded balanced sensitivity (72%) and specificity (70%), suggesting practical applicability. Importantly, HLNR remained an independent predictor after adjusting for EORTC-like clinicopathological variables, indicating that it adds incremental prognostic information beyond tumor characteristics alone. The exploratory composite risk score we developed—assigning points to multiple tumors, tumor size ≥ 30 mm, stage T1, high grade, CIS, and HLNR < 2.0—demonstrated stepwise increases in recurrence rates across low-, intermediate-, and high-risk categories. This integrated approach mirrors the EORTC and Club Urologico Espanol de Tratamiento Oncologico (CUETO) risk calculators but incorporates a hematologic dimension. If validated in external cohorts, such a score could assist clinicians in tailoring surveillance intensity, selecting candidates for maintenance intravesical therapy, and counseling patients regarding recurrence risk. A notable challenge in the field is the lack of standardized cut-off values for hematologic indices. Prior studies have used various thresholds: hemoglobin 240×10³/µL for thrombocytosis, HPR cut-off of 0.615 by ROC analysis [ 12 ], and HALP cut-off of 22.2 by X-tile software [ 13 ]. Our HLNR cut-off of ≥ 2.0 and HLPR cut-off of ≥ 0.45 were derived using ROC curve-based Youden index optimization, consistent with methodological approaches in the literature. However, external validation in diverse populations is essential before these thresholds can be broadly applied. Prospective, multicenter studies with larger cohorts are needed to establish consensus cut-offs and evaluate the generalizability of our findings. Several limitations warrant acknowledgment. First, the retrospective, single-center design limits causal inference and generalizability. Selection bias may have influenced which patients underwent timely control cystoscopy and complete laboratory workup. Second, we measured hematologic parameters at a single preoperative time point; dynamic changes over time were not assessed. Serial measurements could capture evolving inflammatory states and improve prognostic accuracy. Third, we did not have albumin levels to calculate HALP, nor did we routinely assess other inflammatory markers (e.g., C-reactive protein). Fourth, comorbidities and concurrent infections that may influence hematologic parameters were not systematically recorded or excluded. Fifth, our primary analysis used logistic regression with recurrence as a binary endpoint; we did not perform time-to-event analyses (Cox regression, Kaplan-Meier curves) for recurrence-free survival, which would provide additional insights into the temporal dynamics of risk. Finally, the exploratory EORTC-integrated HLNR score has not been externally validated and requires confirmation in independent cohorts before clinical implementation. Future research should prioritize prospective, multicenter validation of HLNR and the integrated risk score. Time-to-event analyses incorporating Kaplan-Meier curves and Cox proportional hazards regression would allow assessment of recurrence-free survival, with C-index evaluation to quantify discriminative accuracy. Serial measurement of HLNR at 3, 6, and 12 months post-TUR-B could reveal whether dynamic changes predict recurrence better than baseline values alone. Integration with established risk calculators (EORTC, CUETO) should be formally tested, and nomograms combining clinical, pathological, and hematologic variables could be developed. Machine learning approaches may further enhance predictive performance by identifying complex interactions among variables. Finally, interventional studies examining whether correcting anemia or modulating systemic inflammation improves outcomes in high-risk patients would be of great interest. In conclusion, preoperative HLNR is significantly and independently associated with tumor recurrence at control cystoscopy in patients with non-muscle invasive bladder cancer. HLNR demonstrated superior discriminative ability compared to HLPR, LNR, and PLR, and retained independent prognostic value after adjustment for multiple clinicopathological factors. As a readily obtainable, low-cost index derived from routine blood counts, HLNR may serve as a valuable adjunct to existing risk stratification systems. An exploratory composite risk score integrating EORTC-like tumor features with HLNR showed promise for enhancing recurrence prediction. These findings warrant validation in prospective, multicenter cohorts and exploration of HLNR's role in guiding surveillance and adjuvant therapy decisions. Declarations Funding: The authors have no relevant financial or non-financial interests to disclose. Conflict of interest: All authors declared that they have no conflict of interest. Ethics approval : This The study was conducted in accordance with the 1964 Declaration of Helsinki and approved by the institutional Clinical Research Ethics Committee (Approval No: 10354421-2025/03-17). Consent to participate: Informed consent forms were obtained from all patients. Written Consent for publication : Applicable Availability of data and material: I used and generated research data in this study. The data supporting the findings of this study are available from the corresponding author upon reasonable request. Code availability: Not applicable Author Contributions Murat Uçar: Project development, Data collection and management, Data analysis, Manuscript writing and editing. Nureddin Raym: Data collection and management, Data analysis. Uğur Soy: Data collection and management, Data analysis. Erkan Karadağ: Data analysis, Manuscript writing. Murat Topçuoğlu: Data analysis, Manuscript editing. Ali Akkoç: Data analysis, Manuscript editing. 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DOI: 10.1002/jcla.23153 Tables Table 1 : Baseline Clinicopathological Characteristics of the Study Population Variable No Relapse (n=113) Relapse (n=67) p value Age, years, mean ± SD 66.5 ± 10.7 63.5 ± 11.5 0.111 Male sex, n (%) 104 (92.0) 59 (88.1) 0.221 Smoking, n (%) 70 (61.9) 44 (65.7) 0.836 Solitary tumor, n (%) 69 (61.1) 30 (44.8) 0.028* Tumor size, mm, median (IQR) 30 (20–35) 30 (20–40) 0.080 Stage T1, n (%) 38 (33.6) 29 (43.3) 0.350 High grade, n (%) 74 (65.5) 49 (73.1) 0.421 CIS present, n (%) 14 (12.4) 15 (22.4) 0.513 Lymphovascular invasion, n (%) 3 (2.7) 2 (3.0) 1.000 Follow-up, months, median (IQR) 16 (5–36) 28 (14–40) 0.022* SD= standard deviation, IQR= interquartile range, CIS= carcinoma in situ, * p value < 0.05 Table 2 : Preoperative Hematologic Parameters and Indices in Patients with and without Tumor Recurrence Variable No Relapse (n=113) Relapse (n=67) p value Hemoglobin (g/dL) 13.8 ± 1.4 12.9 ± 1.6 0.004* WBC (×10⁹/L) 7.6 ± 2.1 7.9 ± 2.4 0.318 Platelet (×10⁹/L), median (IQR) 245 (210–285) 260 (225–305) 0.072 Neutrophil (×10⁹/L), median (IQR) 4.4 (3.6–5.5) 5.0 (4.1–6.2) 0.021* Lymphocyte (×10⁹/L), median (IQR) 1.9 (1.6–2.3) 1.5 (1.2–1.9) <0.001** RDW (%) 13.4 ± 1.1 13.9 ± 1.3 0.058 HLPR, median (IQR) 0.62 (0.48–0.79) 0.44 (0.33–0.60) 0.018* HLNR, median (IQR) 2.25 (1.72–2.94) 1.48 (1.05–2.01) <0.001** LNR, median (IQR) 1.78 (1.32–2.21) 1.42 (1.10–1.83) 0.041* PLR, median (IQR) 135 (112–165) 155 (128–188) 0.092 WBC= white blood cell, IQR= interquartile range, RDW= red cell distribution width, HLPR= hemoglobin-lymphocyte-platelet ratio, HLNR= hemoglobin-lymphocyte-platelet ratio, LNR= lymphocyte/neutrophil ratio , PLR= platelet/lymphocyte ratio. * p value < 0.05, ** p value <0.001 Table 3: ROC Curve Analysis of Hematologic Indices for Predicting Tumor Recurrence Index AUC (95% CI) Optimal Cut-off Sensitivity (%) Specificity (%) p value HLNR 0.75 (0.68–0.82) 2.0 72 70 <0.001** HLPR 0.65 (0.57–0.73) 0.45 61 62 0.018* LNR 0.61 (0.53–0.69) 1.6 58 59 0.041* PLR 0.58 (0.50–0.66) 150 55 56 0.092 AUC= area under curve, IQR= interquartile range, HLNR= hemoglobin-lymphocyte-platelet ratio, HLPR= hemoglobin-lymphocyte-platelet ratio, LNR= lymphocyte/neutrophil ratio , PLR= platelet/lymphocyte ratio. * p value < 0.05, ** p value <0.001 Table 4: Univariate and Multivariate Logistic Regression Analyses for Predictors of Tumor Recurrence Variable Univariate OR (95% CI) p value Multivariate OR (95% CI) p value Age (per year) 0.99 (0.97–1.01) 0.284 0.99 (0.97–1.02) 0.412 Male sex 0.62 (0.32–1.21) 0.168 0.71 (0.33–1.54) 0.392 Smoking 0.91 (0.52–1.61) 0.742 0.98 (0.53–1.83) 0.961 Multiple tumors 2.25 (1.27–3.98) 0.005* 1.98 (1.06–3.71) 0.032 Tumor size *** 1.28 (0.77–2.14) 0.343 — — OR= odds ratio, * p value < 0.05, ***Evaluated in separate models to avoid collinearity. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 01 May, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 07 Apr, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-9342974","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623999425,"identity":"ec3463bf-ff47-4601-87b1-6cf9978a2e12","order_by":0,"name":"Murat Uçar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYHAD5gMMD6BMCYKKD4BJtgSGBIQWA2K08BgQp0V3Ru7Dzx8YDsvzzz7z8UNCzZ3E/gbmg7d5GP7k49JidiPdWOIAw2HDGedyN0skHHuWOOMAW7I1D4OBZQNOLWkMIC0JDGd4N0gksB1ObDjAYyYN1ILTZUAtzD9AWuTP8Dz+kfDvcOL8A/zfCGlhA9ticIaHTSKx7XDihgM8bPi1nHnGZnHGIN1w4xk2M4vEvsPGGw+zGVvOMTDGreV4GvONigprebkzzI9vfPh2WHbe8eaHN95UyBGIGCRpxwZmNBGCwJ4EtaNgFIyCUTBCAAB1I1c/5ymy3wAAAABJRU5ErkJggg==","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":true,"prefix":"","firstName":"Murat","middleName":"","lastName":"Uçar","suffix":""},{"id":623999426,"identity":"3d10b6aa-49e2-4cac-b49b-db17fcff0a90","order_by":1,"name":"Nureddin Raym","email":"","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":false,"prefix":"","firstName":"Nureddin","middleName":"","lastName":"Raym","suffix":""},{"id":623999428,"identity":"fc2d588c-9e3f-4622-a09c-96e0426d7d72","order_by":2,"name":"Uğur Soy","email":"","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":false,"prefix":"","firstName":"Uğur","middleName":"","lastName":"Soy","suffix":""},{"id":623999429,"identity":"8e08c01a-063d-46a9-9a3f-6e8acb850ce7","order_by":3,"name":"Erkan Karadağ","email":"","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":false,"prefix":"","firstName":"Erkan","middleName":"","lastName":"Karadağ","suffix":""},{"id":623999430,"identity":"e6d71ad9-e789-4455-9336-9529493ea05b","order_by":4,"name":"Murat Topçuoğlu","email":"","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":false,"prefix":"","firstName":"Murat","middleName":"","lastName":"Topçuoğlu","suffix":""},{"id":623999431,"identity":"fcda2e4d-4e04-4188-b040-af05992737fd","order_by":5,"name":"Ali Akkoç","email":"","orcid":"","institution":"Alanya Alaaddin Keykubat University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"","lastName":"Akkoç","suffix":""}],"badges":[],"createdAt":"2026-04-07 09:54:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9342974/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9342974/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107355880,"identity":"341ea912-f60c-4105-b424-a4e3d188e1b8","added_by":"auto","created_at":"2026-04-20 16:55:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88792,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram to demonstrate the patients selection criteria. TUR-B: Transurethral resection of bladder\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9342974/v1/e6f9a1d935dc45c122553a7a.png"},{"id":107355746,"identity":"ad6b1d4b-dfaa-49fd-863e-46541c16a27f","added_by":"auto","created_at":"2026-04-20 16:55:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73733,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Inflammatory Indices with ROC curves. LNR=lymphocyte/neutrophil ratio, PLR=platelet/lymphocyte ratio, HLNR=hemoglobin-lymphocyte-neutrophil ratio, HLPR=hemoglobin-lymphocyte-platelet ratio, ROC= receiver operating characteristic\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9342974/v1/9a4cfc7d202aef1336f4f0a7.png"},{"id":107355961,"identity":"f19e9970-8899-4c96-9bf5-056a02d880ea","added_by":"auto","created_at":"2026-04-20 16:55:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":570750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9342974/v1/b3bf9ea2-f08b-42cf-8255-1ad4dd9add26.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Novel Hemogram-Based Method for More Accurate Preoperative Prediction of Tumor Aggressiveness in Bladder Cancer: A Retrospective Cohort Study","fulltext":[{"header":"1. Introductıon","content":"\u003cp\u003eBladder cancer accounts for approximately 3% of all cancer diagnoses worldwide. Urothelial carcinoma represents the most common histological subtype, comprising nearly 95% of cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. At initial presentation, approximately 75% of patients are diagnosed with non\u0026ndash;muscle-invasive bladder cancer (NMIBC), limited to the mucosal and submucosal layers, whereas the remaining 25% present with muscle-invasive bladder cancer (MIBC), which is characterized by muscularis propria tissue infiltration. Patients with MIBC generally exhibit a significantly poorer prognosis compared with those with NMIBC [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite significant advances in surgical management and adjuvant therapies, NMIBC remains characterized by high recurrence and progression rates. Over the course of follow-up, approximately 70% of patients experience disease recurrence, while nearly 30% eventually progress to MIBC [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Current risk stratification models remain inadequate, underscoring the need for reliable prognostic biomarkers to improve treatment decisions and facilitate individualized patient management.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment plays a pivotal role in the initiation, progression, and therapeutic resistance of many malignancies, including bladder cancer. Specifically, immune and inflammatory cells such as neutrophils, lymphocytes, monocytes, and platelets are integral components of this microenvironment. Depending on their activation state and localization, these cells may exert either tumor-promoting or tumor-suppressing effects. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Among systemic inflammatory response (SIR) indicators, the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have emerged as significant due to their practicality, low cost, and prognostic value across a wide range of solid tumors [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These indices reflect the dynamic interplay between proinflammatory responses and antitumor immunity, thereby offering critical insights into tumor behavior. In particular, elevated NLR values have been associated with more aggressive tumor behavior and adverse pathological features [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prognostic value of preoperative systemic inflammatory markers in bladder cancer has been extensively documented [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In particular, NLR and PLR have been associated with tumor grade, stage, and recurrence. These parameters are readily accessible through routine complete blood counts, making them non-invasive and cost-effective biomarkers. However, conventional risk models are primarily based on clinicopathological features and often fail to reflect the host immune response. In this context, inflammation-based indices such as NLR and PLR may facilitate the early identification of high-risk patients and assist in the development of personalized therapeutic strategies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn parallel with the growing interest in inflammation-based prognostic markers, recent years have seen the emergence of novel hematological indices that integrate standard blood parameters in innovative ways. U\u0026ccedil;ar et al. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] introduced the hemoglobin \u0026times; lymphocyte/neutrophil ratio (HLNR) and demonstrated its potential utility in predicting tumor aggressiveness among patients with renal cell carcinoma. Their findings revealed significant associations between this index and adverse pathological features such as higher tumor stage, nuclear grade, and necrosis. Building upon this, the present study aims to investigate the prognostic significance of preoperative hemogram parameters\u0026mdash;particularly the value of HLNR, as described in U\u0026ccedil;ar\u0026rsquo;s work\u0026mdash;in the context of bladder cancer [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. By applying this novel hematological marker to urothelial carcinoma, this research holds the potential to more accurately predict tumor behavior and develop risk assessment models that are more individualized and biologically informative\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003e\u003cem\u003e2.1.Study Design and Patient Selection\u003c/em\u003e\u003cstrong\u003e\u003cbr\u003e\u003c/strong\u003eThis single-center, retrospective cohort study included consecutive patients who underwent transurethral resection of bladder (TUR-B) tumors between February 2016 and February 2025 at our institution. A total of 180 patients who underwent TUR-B were included (Figure 1). Pathology reports were reviewed to identify patients with non-muscle invasive urothelial carcinoma (stage Ta or T1). The study was conducted in accordance with the 1964 Declaration of Helsinki and approved by the Institutional Clinical Research Ethics Committee (Approval No: 10354421-2025/03-17).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInclusion criteria:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePathological stage Ta or T1\u003c/li\u003e\n \u003cli\u003eHistologically confirmed urothelial carcinoma\u003c/li\u003e\n \u003cli\u003eComprehensive preoperative hematological and biochemical profiles (hemoglobin, white blood cell count, platelet count, platelet distribution width [PDW], mean platelet volume [MPV], neutrophil count, lymphocyte count, red cell distribution width [RDW], urea, creatinine)\u003c/li\u003e\n \u003cli\u003eAvailable control cystoscopy findings and follow-up data\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cem\u003eExclusion criteria:\u003c/em\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eMuscle-invasive disease (Stage \u0026ge;T2) or non-urothelial histology.\u003c/li\u003e\n \u003cli\u003eIncomplete primary endpoint data (e.g., missing recurrence status).\u003c/li\u003e\n \u003cli\u003eMissing laboratory values preventing the calculation of inflammatory indices.\u003c/li\u003e\n \u003cli\u003eFollow-up duration shorter than 3 months (loss to follow-up).\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAll personal identifiers (national ID number, name, surname) were removed from the dataset prior to analysis to ensure anonymity.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2. Data Collection and Hematological Parameters\u003c/em\u003e\u003cbr\u003eDemographic data, including age, sex, pathological stage and grade, and the presence of recurrence, were obtained from medical records. Preoperative peripheral blood samples collected within one week prior to surgery were analyzed using an automated hematology analyzer. Hemoglobin (Hb) levels, neutrophil count, lymphocyte count and platelet count were recorded. Based on these values, the following ratios and indices were calculated: lymphocyte-to-neutrophil ratio (LNR) platelet-to-lymphocyte ratio (PLR), hemoglobin \u0026times; lymphocyte/neutrophil ratio (HLNR), and hemoglobin \u0026times; platelet/lymphocyte ratio (HLPR). The HLNR and HLPR formula was originally described by U\u0026ccedil;ar et al. [9], who demonstrated its potential utility in predicting tumor aggressiveness in patients with renal cell carcinoma.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3. Pathological Evaluation\u003c/em\u003e\u003cbr\u003eTumor staging was performed according to the TNM classification system, while grading was based on the World Health Organization (WHO) criteria [10]. Bladder tumors were classified as non\u0026ndash;muscle-invasive (Ta, T1) or muscle-invasive (\u0026ge;T2). Pathological assessment, including the distinction between high-grade and low-grade disease, was conducted by specialized uropathologists blinded to the hematological data.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4. Statistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were performed using IBM SPSS Statistics version 26.0 (IBM Corp., Armonk, NY, USA) and R version 4.2.0 (R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.1.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eDescriptive statistics\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Continuous variables were summarized as mean \u0026plusmn; standard deviation (SD) or median (interquartile range [IQR]) depending on distribution, assessed by the Shapiro-Wilk test and histograms. Categorical variables were presented as number and percentage [n (%)].\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.2. Group comparisons\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Patients with and without recurrence were compared using Student\u0026apos;s t-test or Mann-Whitney U test for continuous variables, and chi-square or Fisher\u0026apos;s exact test for categorical variables.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.3. ROC curve analysis\u003c/em\u003e\u003cbr\u003e\u0026nbsp;The discriminative ability of HLNR, HLPR, LNR, and PLR for predicting recurrence was evaluated by receiver operating characteristic (ROC) curve analysis. Area under the curve (AUC) values with 95% confidence intervals (CI) were calculated. Optimal cut-off values were determined using the Youden index (sensitivity + specificity \u0026minus; 1). Based on these cut-offs, each index was dichotomized into \u0026quot;low\u0026quot; and \u0026quot;high\u0026quot; categories.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.4. Logistic regression analysis\u003c/em\u003e\u003cbr\u003e\u0026nbsp;Univariate logistic regression was performed to identify potential predictors of recurrence. Variables with p\u0026lt;0.10 in univariate analysis and clinically relevant covariates (age, sex, tumor multiplicity, tumor size \u0026ge;30 mm, stage T1, high grade, CIS, LVI) were entered into multivariate logistic regression models. HLNR and HLPR were included as mandatory covariates in the primary model; LNR and PLR were evaluated in separate exploratory models to avoid collinearity. Odds ratios (OR) with 95% CI were reported. Model fit was assessed using the Hosmer-Lemeshow goodness-of-fit test.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.5. Exploratory composite risk score\u003c/em\u003e\u003cbr\u003e\u0026nbsp;A simple additive risk score integrating\u0026nbsp;European Organisation for Research and Treatment of Cancer (EORTC)-like clinicopathological factors and HLNR was constructed. Points were assigned as follows: multiple tumors (1 point), tumor size \u0026ge;30 mm (1 point), stage T1 (1 point), high grade (1 point), CIS present (1 point), and HLNR \u0026lt;2.0 (2 points). Total scores ranged from 0 to 7. Patients were categorized into three risk groups: low (0\u0026ndash;2 points), intermediate (3\u0026ndash;5 points), and high (6\u0026ndash;8 points if HLPR \u0026lt;0.45 was added as 1 point). Recurrence rates were compared across risk categories.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.6. Sensitivity analyses\u003c/em\u003e\u003cbr\u003e\u0026nbsp;The primary analyses were repeated in subgroups of patients with at least 6 months and at least 12 months of follow-up to assess the robustness of findings.\u003c/p\u003e\n\u003cp\u003eAll tests were two-sided, and p\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1. Patient Characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA total of 180 patients met the inclusion criteria. The cohort\u0026rsquo;s mean age was 65.8 \u0026plusmn; 11.2 years, with a predominant male representation (n=163, 90.6%). Smoking history was present in 114 (63.3%) patients. At initial diagnosis, 99 (55.0%) patients had solitary tumors and 81 (45.0%) had multiple tumors. Median tumor size was 30 mm (IQR 20\u0026ndash;35). All patients had Ta or T1 stage disease; 57 (31.7%) were low-grade and 123 (68.3%) were high-grade. Concomitant CIS was present in 29 (16.1%) patients, and lymphovascular invasion was observed in 5 (2.8%). Median follow-up duration was 18 months (IQR 6\u0026ndash;38). During follow-up, 67 (37.2%) patients developed recurrence at control cystoscopy, whereas 113 (62.8%) remained recurrence-free.\u003c/p\u003e\n\u003cp\u003eBaseline demographic and tumor characteristics, stratified by recurrence status, are presented in Table 1. Patients with recurrence were more likely to have multiple tumors (p=0.028) and had longer follow-up duration (p=0.022), reflecting the time-dependent nature of recurrence detection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. Hematologic Parameters and Indices\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePreoperative hematologic parameters and composite indices are summarized in Table 2. Patients who developed recurrence had significantly lower hemoglobin (12.9 vs. 13.8 g/dL; p=0.004), higher neutrophil counts (5.0 vs. 4.4 \u0026times;10⁹/L; p=0.021), and lower lymphocyte counts (1.5 vs. 1.9 \u0026times;10⁹/L; p\u0026lt;0.001) compared to those without recurrence. Median HLNR was significantly lower in the recurrence group (1.48 vs. 2.25; p\u0026lt;0.001), as was HLPR (0.44 vs. 0.62; p=0.018). LNR showed a borderline significant difference (p=0.041), while PLR did not reach statistical significance (p=0.092).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.3. ROC Curve Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis was performed to evaluate the discriminative ability of each hematologic index for predicting recurrence (Table 3). HLNR demonstrated the highest predictive accuracy (AUC 0.75; 95% CI: 0.68\u0026ndash;0.82; p\u0026lt;0.001), significantly outperforming HLPR (AUC 0.65), LNR (AUC 0.61), and PLR (AUC 0.58). The optimal HLNR cut-off of \u0026ge;2.0 yielded 72% sensitivity and 70% specificity. For HLPR, the optimal cut-off of \u0026ge;0.45 had 61% sensitivity and 62% specificity (p=0.018). Neither LNR nor PLR achieved robust discriminative performance. ROC curves for HLNR, HLPR, LNR and PLR are presented in Figure 2, illustrating the superior discriminative performance of HLNR compared with the other indices.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4. Univariate and Multivariate Logistic Regression\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate logistic regression analyses for predictors of tumor recurrence are shown in Table 4. In univariate analysis, multiple tumors (OR 2.25; p=0.005), tumor size \u0026ge;30 mm (OR 2.62; p=0.001), stage T1 (OR 1.89; p=0.034), high HLNR \u0026ge;2.0 (OR 0.29; p\u0026lt;0.001, protective), and high HLPR \u0026ge;0.45 (OR 0.55; p=0.031, protective) were significantly associated with recurrence.\u003c/p\u003e\n\u003cp\u003eIn multivariate analysis adjusting for age, sex, smoking, tumor characteristics, and both HLNR and HLPR, multiple tumors (adjusted OR 1.98; 95% CI: 1.06\u0026ndash;3.71; p=0.032) and tumor size \u0026ge;30 mm (adjusted OR 2.21; 95% CI: 1.15\u0026ndash;4.26; p=0.018) were identified as independent predictors for recurrence. High HLNR (\u0026ge;2.0) was independently associated with a significantly lower risk of recurrence (adjusted OR 0.34; 95% CI: 0.18\u0026ndash;0.63; p\u0026lt;0.001), indicating a strong protective effect. HLPR lost statistical significance in the multivariate model (adjusted OR 0.68; p=0.209). LNR and PLR were not independently predictive in separate exploratory models. The Hosmer-Lemeshow test indicated good model fit (p=0.52).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5. Exploratory Composite Risk Score\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAn exploratory risk score integrating EORTC-like clinicopathological variables (multiple tumors, tumor size \u0026ge;30 mm, stage T1, high grade, CIS) and HLNR (\u0026lt;2.0 = 2 points) was constructed, yielding a 0\u0026ndash;7 point scale. Patients were stratified into low (0\u0026ndash;2 points), intermediate (3\u0026ndash;5 points), and high (6\u0026ndash;7 points) risk categories. Recurrence rates increased stepwise across risk groups: 18.2% in low-risk, 42.5% in intermediate-risk, and 68.4% in high-risk patients (p for trend \u0026lt;0.001). This exploratory score demonstrated improved discrimination compared to purely clinical scores without HLNR (AUC 0.78 vs. 0.68; p=0.012).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.6. Sensitivity Analyses\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWhen analyses were restricted to patients with at least 6 months of follow-up (n=154) and at least 12 months of follow-up (n=128), the direction and magnitude of associations remained consistent. HLNR retained independent prognostic significance in both subgroups (6-month cohort: adjusted OR 0.36, p=0.001; 12-month cohort: adjusted OR 0.31, p\u0026lt;0.001), confirming the robustness of findings.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis retrospective cohort study demonstrates that preoperative HLNR is a significant and independent predictor of tumor recurrence at control cystoscopy in patients with NMIBC, outperforming HLPR, LNR, and PLR. Patients with HLNR\u0026thinsp;\u0026lt;\u0026thinsp;2.0 had approximately three times higher odds of recurrence compared to those with HLNR\u0026thinsp;\u0026ge;\u0026thinsp;2.0, even after adjusting for established clinicopathological risk factors including tumor multiplicity, size, stage, grade, CIS, and lymphovascular invasion. These findings support the incorporation of HLNR into existing risk stratification frameworks and underscore the value of readily available hematologic parameters as prognostic biomarkers in NMIBC.\u003c/p\u003e \u003cp\u003eOur findings align with prior reports emphasizing the prognostic role of hematologic indices in NMIBC and other urologic malignancies. Zhao and colleagues demonstrated that a neutrophil-hemoglobin-lymphocyte (NHL) composite ratio significantly predicted postoperative recurrence in NMIBC, consistent with our observation that HLNR\u0026mdash;a similar composite index\u0026mdash;is strongly associated with recurrence [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Tang et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] reported that HPR (hemoglobin-platelet ratio) was a weak predictor of recurrence (non-significant for recurrence-free survival) but strongly predicted progression-free survival, overall survival, and cancer-specific survival in patients with high-risk NMIBC and muscle-invasive bladder cancer. Our study corroborates this pattern: HLPR showed only borderline significance in univariate analysis and lost independent predictive value in multivariate models for recurrence, suggesting that HLPR may be more relevant for long-term outcomes such as progression and mortality rather than early recurrence.\u003c/p\u003e \u003cp\u003eThe HALP score (hemoglobin \u0026times; albumin \u0026times; lymphocyte / platelet) and its derivative, HALPA (HALP combined with ASA grade), have been validated in radical cystectomy cohorts, where they distinguished tumor stage and independently predicted overall survival [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While we did not calculate HALP due to the absence of albumin data in our dataset, our exploratory EORTC-integrated HLNR score conceptually parallels the HALPA approach by combining tumor-related factors with an inflammation-immune index, yielding stepwise risk stratification.\u003c/p\u003e \u003cp\u003eThe strong prognostic value of HLNR likely reflects its ability to capture multiple aspects of tumor biology and host response. Anemia, resulting from chronic blood loss, iron deficiency, or cytokine-mediated suppression of erythropoiesis (elevated IL-6, TNF-α), is associated with tissue hypoxia, upregulation of HIF-1α, and enhanced angiogenesis via VEGF secretion, all of which promote tumor invasiveness and metastatic potential [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Thrombocytosis, driven by IL-6 and GATA-2 overexpression, facilitates platelet-mediated tumor cell adhesion, protects circulating tumor cells from NK cell attack, and supports endothelial attachment and extravasation. Lymphopenia reflects impaired immune surveillance, with reduced cytotoxic T-cell activity enabling tumor proliferation and migration. Conversely, relative neutrophilia contributes to a pro-inflammatory milieu that may favor tumor progression. HLNR integrates hemoglobin, neutrophil, and lymphocyte values, thereby providing a composite measure of anemia, systemic inflammation, and immune dysfunction\u0026mdash;three interconnected processes central to cancer progression [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a clinical perspective, HLNR offers several advantages: it is derived from routine preoperative complete blood counts, incurs no additional cost, and can be calculated immediately. Our ROC-derived cut-off of \u0026ge;\u0026thinsp;2.0 yielded balanced sensitivity (72%) and specificity (70%), suggesting practical applicability. Importantly, HLNR remained an independent predictor after adjusting for EORTC-like clinicopathological variables, indicating that it adds incremental prognostic information beyond tumor characteristics alone.\u003c/p\u003e \u003cp\u003eThe exploratory composite risk score we developed\u0026mdash;assigning points to multiple tumors, tumor size\u0026thinsp;\u0026ge;\u0026thinsp;30 mm, stage T1, high grade, CIS, and HLNR\u0026thinsp;\u0026lt;\u0026thinsp;2.0\u0026mdash;demonstrated stepwise increases in recurrence rates across low-, intermediate-, and high-risk categories. This integrated approach mirrors the EORTC and Club Urologico Espanol de Tratamiento Oncologico (CUETO) risk calculators but incorporates a hematologic dimension. If validated in external cohorts, such a score could assist clinicians in tailoring surveillance intensity, selecting candidates for maintenance intravesical therapy, and counseling patients regarding recurrence risk.\u003c/p\u003e \u003cp\u003eA notable challenge in the field is the lack of standardized cut-off values for hematologic indices. Prior studies have used various thresholds: hemoglobin\u0026thinsp;\u0026lt;\u0026thinsp;125 g/L for anemia, platelet\u0026thinsp;\u0026gt;\u0026thinsp;240\u0026times;10\u0026sup3;/\u0026micro;L for thrombocytosis, HPR cut-off of 0.615 by ROC analysis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and HALP cut-off of 22.2 by X-tile software [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our HLNR cut-off of \u0026ge;\u0026thinsp;2.0 and HLPR cut-off of \u0026ge;\u0026thinsp;0.45 were derived using ROC curve-based Youden index optimization, consistent with methodological approaches in the literature. However, external validation in diverse populations is essential before these thresholds can be broadly applied. Prospective, multicenter studies with larger cohorts are needed to establish consensus cut-offs and evaluate the generalizability of our findings.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant acknowledgment. First, the retrospective, single-center design limits causal inference and generalizability. Selection bias may have influenced which patients underwent timely control cystoscopy and complete laboratory workup. Second, we measured hematologic parameters at a single preoperative time point; dynamic changes over time were not assessed. Serial measurements could capture evolving inflammatory states and improve prognostic accuracy. Third, we did not have albumin levels to calculate HALP, nor did we routinely assess other inflammatory markers (e.g., C-reactive protein). Fourth, comorbidities and concurrent infections that may influence hematologic parameters were not systematically recorded or excluded. Fifth, our primary analysis used logistic regression with recurrence as a binary endpoint; we did not perform time-to-event analyses (Cox regression, Kaplan-Meier curves) for recurrence-free survival, which would provide additional insights into the temporal dynamics of risk. Finally, the exploratory EORTC-integrated HLNR score has not been externally validated and requires confirmation in independent cohorts before clinical implementation.\u003c/p\u003e \u003cp\u003eFuture research should prioritize prospective, multicenter validation of HLNR and the integrated risk score. Time-to-event analyses incorporating Kaplan-Meier curves and Cox proportional hazards regression would allow assessment of recurrence-free survival, with C-index evaluation to quantify discriminative accuracy. Serial measurement of HLNR at 3, 6, and 12 months post-TUR-B could reveal whether dynamic changes predict recurrence better than baseline values alone. Integration with established risk calculators (EORTC, CUETO) should be formally tested, and nomograms combining clinical, pathological, and hematologic variables could be developed. Machine learning approaches may further enhance predictive performance by identifying complex interactions among variables. Finally, interventional studies examining whether correcting anemia or modulating systemic inflammation improves outcomes in high-risk patients would be of great interest.\u003c/p\u003e \u003cp\u003eIn conclusion, preoperative HLNR is significantly and independently associated with tumor recurrence at control cystoscopy in patients with non-muscle invasive bladder cancer. HLNR demonstrated superior discriminative ability compared to HLPR, LNR, and PLR, and retained independent prognostic value after adjustment for multiple clinicopathological factors. As a readily obtainable, low-cost index derived from routine blood counts, HLNR may serve as a valuable adjunct to existing risk stratification systems. An exploratory composite risk score integrating EORTC-like tumor features with HLNR showed promise for enhancing recurrence prediction. These findings warrant validation in prospective, multicenter cohorts and exploration of HLNR's role in guiding surveillance and adjuvant therapy decisions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eAll authors declared that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eapproval\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis The study was conducted in accordance with the 1964 Declaration of Helsinki and approved by the institutional Clinical Research Ethics Committee (Approval No: 10354421-2025/03-17).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eInformed consent forms were obtained from all patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWritten Consent for publication\u003c/strong\u003e\u003cstrong\u003e:\u0026nbsp;\u003c/strong\u003eApplicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e I used and generated research data in this study. The data supporting the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability:\u0026nbsp;\u003c/strong\u003eNot applicable\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMurat U\u0026ccedil;ar:\u0026nbsp;\u003c/strong\u003eProject development, Data collection and management, Data analysis, Manuscript writing and editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNureddin Raym:\u0026nbsp;\u003c/strong\u003eData collection and management, Data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eUğur Soy:\u0026nbsp;\u003c/strong\u003eData collection and management, Data analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eErkan Karadağ:\u0026nbsp;\u003c/strong\u003eData analysis, Manuscript writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMurat Top\u0026ccedil;uoğlu:\u0026nbsp;\u003c/strong\u003eData analysis, Manuscript editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAli Akko\u0026ccedil;:\u0026nbsp;\u003c/strong\u003eData analysis, Manuscript editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHassan, W.A., et al., \u003cem\u003eSignificance of tumor-associated neutrophils, lymphocytes, and neutrophil-to-lymphocyte ratio in non-invasive and invasive bladder urothelial carcinoma.\u003c/em\u003e J Pathol Transl Med, 2023. 57(2): p. 88-94. DOI: 10.4132/jptm.2022.11.06\u003c/li\u003e\n\u003cli\u003eComp\u0026eacute;rat, E., et al., \u003cem\u003eNonmuscle-invasive bladder cancer, old problems, new insights.\u003c/em\u003e Curr Opin Urol, 2022. 32(4): p. 352-357. DOI: 10.1097/MOU.0000000000000997\u003c/li\u003e\n\u003cli\u003eLiu, K., et al., \u003cem\u003eThe prognostic values of tumor-infiltrating neutrophils, lymphocytes and neutrophil/lymphocyte rates in bladder urothelial cancer.\u003c/em\u003e Pathol Res Pract, 2018. 214(8): p. 1074-1080. DOI: 10.1016/j.prp.2018.05.010\u003c/li\u003e\n\u003cli\u003eCantiello, F., et al., \u003cem\u003eSystemic Inflammatory Markers and Oncologic Outcomes in Patients with High-risk Non-muscle-invasive Urothelial Bladder Cancer.\u003c/em\u003e Eur Urol Oncol, 2018. 1(5): p. 403-410. DOI: 10.1016/j.euo.2018.06.006\u003c/li\u003e\n\u003cli\u003eWu, S., et al., \u003cem\u003ePretreatment Neutrophil-Lymphocyte Ratio as a Predictor in Bladder Cancer and Metastatic or Unresectable Urothelial Carcinoma Patients: a Pooled Analysis of Comparative Studies.\u003c/em\u003e Cell Physiol Biochem, 2018. 46(4): p. 1352-1364. DOI: 10.1159/000489152\u003c/li\u003e\n\u003cli\u003eMano, R., et al., \u003cem\u003eNeutrophil-to-lymphocyte ratio predicts progression and recurrence of non-muscle-invasive bladder cancer.\u003c/em\u003e Urol Oncol, 2015. 33(2): p. 67.e1-7. DOI: 10.1016/j.urolonc.2014.06.010\u003c/li\u003e\n\u003cli\u003eLuo, Y., et al., \u003cem\u003eEvaluation of the clinical value of hematological parameters in patients with urothelial carcinoma of the bladder.\u003c/em\u003e Medicine (Baltimore), 2018. 97(14): p. e0351. DOI: 10.1097/MD.0000000000010351\u003c/li\u003e\n\u003cli\u003eLuo, Y., et al., \u003cem\u003ePretreatment Neutrophil to Lymphocyte Ratio as a Prognostic Predictor of Urologic Tumors: A Systematic Review and Meta-Analysis.\u003c/em\u003e Medicine (Baltimore), 2015. 94(40): p. e1670. DOI: 10.1097/MD.0000000000001670\u003c/li\u003e\n\u003cli\u003eU\u0026ccedil;ar, M., et al., \u003cem\u003eCan we Predict Preoperative Tumor Aggressivity with Hemogram Parameters in Renal Cell Carcinoma? a Novel Calculation Method.\u003c/em\u003e J Med Syst, 2019. 44(1): p. 19. DOI: 10.1007/s10916-019-1491-2\u003c/li\u003e\n\u003cli\u003eSun, M. and Q.D. Trinh, \u003cem\u003eDiagnosis and staging of bladder cancer.\u003c/em\u003e Hematol Oncol Clin North Am, 2015. 29(2): p. 205-18, vii. DOI: 10.1016/j.hoc.2014.10.013\u003c/li\u003e\n\u003cli\u003eKang, M., et al., \u003cem\u003ePreoperative neutrophil-lymphocyte ratio can significantly predict mortality outcomes in patients with non-muscle invasive bladder cancer undergoing transurethral resection of bladder tumor.\u003c/em\u003e Oncotarget, 2017. 8(8): p. 12891-12901. DOI: 10.18632/oncotarget.14179\u003c/li\u003e\n\u003cli\u003eTang, G., et al., \u003cem\u003ePreoperative hemoglobin-platelet ratio can significantly predict progression and mortality outcomes in patients with T1G3 bladder cancer undergoing transurethral resection of bladder tumor.\u003c/em\u003e Oncotarget, 2018. 9(26): p. 18627-18636. DOI: 10.18632/oncotarget.23896\u003c/li\u003e\n\u003cli\u003ePeng, D., et al., \u003cem\u003ePrognostic significance of HALP (hemoglobin, albumin, lymphocyte and platelet) in patients with bladder cancer after radical cystectomy.\u003c/em\u003e Sci Rep, 2018. 8(1): p. 794. DOI: 10.1038/s41598-018-19146-y\u003c/li\u003e\n\u003cli\u003eAlbisinni, S., et al., \u003cem\u003eThe impact of neutrophil-to-lymphocyte, platelet-to-lymphocyte and haemoglobin-to-platelet ratio on localised renal cell carcinoma oncologic outcomes.\u003c/em\u003e Prog Urol, 2019. 29(8-9): p. 423-431. DOI: 10.1016/j.purol.2019.05.008\u003c/li\u003e\n\u003cli\u003eMo, C.J., et al., \u003cem\u003eDiagnostic value of platelet-lymphocyte ratio and hemoglobin-platelet ratio in patients with rectal cancer.\u003c/em\u003e J Clin Lab Anal, 2020. 34(4): p. e23153. DOI: 10.1002/jcla.23153\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Baseline Clinicopathological Characteristics of the Study Population\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Relapse (n=113)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelapse (n=67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eAge, years, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e66.5 \u0026plusmn; 10.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e63.5 \u0026plusmn; 11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.111\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eMale sex, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e104 (92.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e59 (88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eSmoking, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e70 (61.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e44 (65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eSolitary tumor, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e69 (61.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e30 (44.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.028*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eTumor size, mm, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e30 (20\u0026ndash;35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e30 (20\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eStage T1, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e38 (33.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e29 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHigh grade, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e74 (65.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e49 (73.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eCIS present, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e14 (12.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e15 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.513\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eLymphovascular invasion, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e3 (2.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2 (3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eFollow-up, months, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e16 (5\u0026ndash;36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e28 (14\u0026ndash;40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.022*\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\u003e\u003cem\u003eSD= standard deviation, IQR= interquartile range, CIS= carcinoma in situ, * p value \u0026lt; 0.05\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Preoperative Hematologic Parameters and Indices in Patients with and without Tumor Recurrence\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo Relapse (n=113)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelapse (n=67)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e13.8 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e12.9 \u0026plusmn; 1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eWBC (\u0026times;10⁹/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e7.6 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e7.9 \u0026plusmn; 2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003ePlatelet (\u0026times;10⁹/L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e245 (210\u0026ndash;285)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e260 (225\u0026ndash;305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eNeutrophil (\u0026times;10⁹/L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e4.4 (3.6\u0026ndash;5.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e5.0 (4.1\u0026ndash;6.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.021*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eLymphocyte (\u0026times;10⁹/L), median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.9 (1.6\u0026ndash;2.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.5 (1.2\u0026ndash;1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eRDW (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e13.4 \u0026plusmn; 1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e13.9 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHLPR, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.62 (0.48\u0026ndash;0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.44 (0.33\u0026ndash;0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eHLNR, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e2.25 (1.72\u0026ndash;2.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.48 (1.05\u0026ndash;2.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003eLNR, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.78 (1.32\u0026ndash;2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e1.42 (1.10\u0026ndash;1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003ePLR, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e135 (112\u0026ndash;165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e155 (128\u0026ndash;188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 145px;\"\u003e\n \u003cp\u003e0.092\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\u003e\u003cem\u003eWBC= white blood cell, IQR= interquartile range, RDW= red cell distribution width, HLPR=\u0026nbsp;\u003c/em\u003e\u003cem\u003ehemoglobin-lymphocyte-platelet ratio, HLNR= hemoglobin-lymphocyte-platelet ratio, LNR=\u0026nbsp;\u003c/em\u003e\u003cem\u003elymphocyte/neutrophil ratio\u003c/em\u003e\u003cem\u003e, PLR=\u0026nbsp;\u003c/em\u003e\u003cem\u003eplatelet/lymphocyte ratio.\u0026nbsp;\u003c/em\u003e\u003cem\u003e* p value \u0026lt; 0.05, ** p value \u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3:\u003c/strong\u003e ROC Curve Analysis of Hematologic Indices for Predicting Tumor Recurrence\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIndex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAUC (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOptimal Cut-off\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eHLNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.75 (0.68\u0026ndash;0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026lt;0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eHLPR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.65 (0.57\u0026ndash;0.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cimg width=\"11\" height=\"17\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img177669528749.png\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.018*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eLNR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.61 (0.53\u0026ndash;0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cimg width=\"11\" height=\"17\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1776695289.png\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.041*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003ePLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.58 (0.50\u0026ndash;0.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cimg width=\"11\" height=\"17\" src=\"https://myfiles.space/user_files/58895_8739fc6c57c1c19a/58895_custom_files/img1776695287.png\" v:shapes=\"_x0000_i1025\" alt=\"image\"\u003e\u0026nbsp;150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.092\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\u003e\u003cem\u003eAUC= area under curve, IQR= interquartile range,\u0026nbsp;\u003c/em\u003e\u003cem\u003eHLNR= hemoglobin-lymphocyte-platelet ratio,\u0026nbsp;\u003c/em\u003e\u003cem\u003eHLPR=\u0026nbsp;\u003c/em\u003e\u003cem\u003ehemoglobin-lymphocyte-platelet ratio, LNR=\u0026nbsp;\u003c/em\u003e\u003cem\u003elymphocyte/neutrophil ratio\u003c/em\u003e\u003cem\u003e, PLR=\u0026nbsp;\u003c/em\u003e\u003cem\u003eplatelet/lymphocyte ratio.\u0026nbsp;\u003c/em\u003e\u003cem\u003e* p value \u0026lt; 0.05, ** p value \u0026lt;0.001\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u003c/strong\u003e Univariate and Multivariate Logistic Regression Analyses for Predictors of Tumor Recurrence\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"579\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnivariate OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMultivariate OR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eAge (per year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.99 (0.97\u0026ndash;1.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eMale sex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.62 (0.32\u0026ndash;1.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.71 (0.33\u0026ndash;1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eSmoking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.91 (0.52\u0026ndash;1.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.98 (0.53\u0026ndash;1.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eMultiple tumors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e2.25 (1.27\u0026ndash;3.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.005*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1.98 (1.06\u0026ndash;3.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003eTumor size ***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e1.28 (0.77\u0026ndash;2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e0.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026mdash;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026mdash;\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\u003e\u003cem\u003eOR= odds ratio, *\u003c/em\u003e p value \u0026lt; 0.05, \u003cem\u003e***Evaluated in separate models to avoid collinearity.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"sn-comprehensive-clinical-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sncm","sideBox":"Learn more about [SN Comprehensive Clinical Medicine](https://www.springer.com/journal/42399)","snPcode":"42399","submissionUrl":"https://submission.nature.com/new-submission/42399/3","title":"SN Comprehensive Clinical Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Hematologic tests, Prognosis, Transitional Cell Carcinoma, Urinary Bladder Neoplasms","lastPublishedDoi":"10.21203/rs.3.rs-9342974/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9342974/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eTo evaluate the prognostic value of preoperative hemoglobin-lymphocyte-neutrophil ratio (HLNR) and hemoglobin-lymphocyte-platelet ratio (HLPR) for predicting tumor recurrence at control cystoscopy in patients with non-muscle invasive bladder cancer (NMIBC) and to compare these indices with lymphocyte-neutrophil ratio (LNR) and platelet-lymphocyte ratio (PLR).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively analyzed 180 patients who underwent transurethral resection for pathological Ta/T1 urothelial bladder tumors. ROC curve analysis was used to assess the predictive performance of each index, and factors associated with recurrence were evaluated using univariate and multivariate logistic regression. An exploratory composite risk score integrating European Organisation for Research and Treatment of Cancer (EORTC)-like clinicopathological factors with HLNR was developed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHLNR showed the highest predictive accuracy with an AUC of 0.75 (95% CI: 0.68\u0026ndash;0.82), whereas HLPR, LNR, and PLR had AUC values of 0.65, 0.61, and 0.58, respectively. An optimal cut-off of \u0026ge;\u0026thinsp;2.0 for HLNR yielded 72% sensitivity and 70% specificity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and high HLNR (\u0026ge;\u0026thinsp;2.0) remained independently associated with a lower risk of recurrence in multivariate analysis (OR 0.34; 95% CI: 0.18\u0026ndash;0.63; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Although high HLPR (\u0026ge;\u0026thinsp;0.45) was protective in univariate analysis (OR 0.55; p\u0026thinsp;=\u0026thinsp;0.031), it did not retain independent significance in the multivariate model (p\u0026thinsp;=\u0026thinsp;0.209).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003ePreoperative HLNR is significantly and independently associated with tumor recurrence at control cystoscopy in patients with NMIBC. Integration of HLNR into EORTC-like risk models appears promising, but its prognostic value should be validated in prospective, multicenter studies.\u003c/p\u003e","manuscriptTitle":"A Novel Hemogram-Based Method for More Accurate Preoperative Prediction of Tumor Aggressiveness in Bladder Cancer: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-20 16:53:14","doi":"10.21203/rs.3.rs-9342974/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-01T06:32:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T04:03:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"154088077720430252387845832516873044915","date":"2026-04-16T03:30:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T08:56:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"298215762453620425962131496093066572360","date":"2026-04-12T08:45:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-10T08:36:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T10:02:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T05:44:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"SN Comprehensive Clinical Medicine","date":"2026-04-07T09:44:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sn-comprehensive-clinical-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sncm","sideBox":"Learn more about [SN Comprehensive Clinical Medicine](https://www.springer.com/journal/42399)","snPcode":"42399","submissionUrl":"https://submission.nature.com/new-submission/42399/3","title":"SN Comprehensive Clinical Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e8dd5c47-2ac2-41bf-8a6c-40b2e9347fc8","owner":[],"postedDate":"April 20th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-01T06:32:41+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-01T06:41:07+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-20 16:53:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9342974","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9342974","identity":"rs-9342974","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}