{"paper_id":"1a93295b-191e-412a-984e-a3e66e2f9c8a","body_text":"A Multidimensional Prognostic Model for Gallbladder Cancer Based on a Multicenter Cohort Integrating Clinicopathological Features, Systemic Inflammation, and Tumor Biomarkers | 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 Multidimensional Prognostic Model for Gallbladder Cancer Based on a Multicenter Cohort Integrating Clinicopathological Features, Systemic Inflammation, and Tumor Biomarkers Mingyang Wang, Runfa Bao, Ziyi Yang, Zhengyu Chen, Shengxin Gu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9222230/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract Background The anatomical TNM staging system inadequately reflects the profound survival heterogeneity in gallbladder cancer (GBC), thereby limiting accurate risk stratification and contributing to suboptimal clinical decision-making. To overcome this limitation, we developed a multidimensional and dynamic prognostic framework integrating clinicopathological features, systemic inflammatory markers, and tumor biomarkers, leveraging large-scale multicenter real-world data. Methods A total of 1,354 patients with GBC from 44 medical centers were retrospectively analyzed and randomly assigned to training (n = 947) and validation (n = 407) cohorts. Independent prognostic factors were identified using LASSO and multivariable Cox regression to construct three risk scores: clinicopathological (C-score), blood-based inflammatory (B-score), and tumor marker (T-score). An integrated prognostic model was subsequently developed and evaluated through machine-learning–based benchmarking. Model performance was assessed using time-dependent ROC analysis (6–48 months), calibration curves, and decision curve analysis (DCA). Gene Set Enrichment Analysis (GSEA) was performed to explore the biological relevance of the scoring systems. Results Age, R0 resection status, TNM stage, CA19-9 (log1p), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-CRP ratio (LCR) were identified as independent prognostic factors. The integrated model consistently outperformed individual C-, B-, and T-scores across all follow-up intervals, demonstrating strong discriminative ability with a 12-month AUC of 0.857 in both cohorts. Calibration and decision curve analyses confirmed good model reliability and clinical utility. GSEA revealed distinct molecular associations underlying the three scores, including ECM–receptor interaction, immune-inflammatory signaling (JAK–STAT/NF-κB), and metabolic stress pathways (HIF-1/p53). A web-based dynamic prediction platform was further developed to enable individualized survival estimation. Conclusion This multidimensional framework provides a biologically interpretable and dynamically adaptable tool for prognostic stratification in gallbladder cancer. Implemented through a web-based platform, the model facilitates individualized risk assessment and supports data-driven clinical decision-making. Gallbladder cancer Prognostic model Systemic inflammatory markers Tumor biomarkers Machine learning Multicenter study Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background Gallbladder cancer (GBC), the most common malignancy of the biliary tract, remains one of the most lethal gastrointestinal cancers despite its relatively low incidence [ 1 – 3 ] . ts clinical course is characterized by insidious onset, rapid progression, and a striking lack of early diagnostic indicators, resulting in most patients being diagnosed at an advanced stage when curative treatment is no longer feasible [ 4 – 6 ] . Even among the minority of patients who undergo radical surgery, long-term outcomes remain poor, with a 5-year survival rate of only approximately 10%-20% [ 7 ] . Collectively, these features underscore a fundamental clinical reality: GBC is not only difficult to detect early, but also extremely challenging to control even when treated aggressively. Radical (R0) resection is currently regarded as the only potentially curative treatment for GBC. However, an alarming and clinically frustrating reality persists: even after apparently complete tumor removal, survival outcomes remain highly heterogeneous and largely unpredictable [ 8 ] . While a subset of patients may achieve meaningful long-term survival, a considerable proportion experience early recurrence and rapid disease progression within a short period after surgery [ 9 ] . Critically, this early failure often occurs despite “curative” resection, indicating that conventional surgical success does not necessarily translate into favorable prognosis. In clinical practice, this creates a profound dilemma, as physicians lack reliable tools to identify high-risk individuals at the time of treatment, thereby limiting the ability to tailor postoperative strategies such as adjuvant therapy and surveillance intensity. This challenge is further exacerbated by the relatively low incidence of GBC and the limited number of patients eligible for surgical intervention. Most existing studies are derived from single-center cohorts with small sample sizes, which restricts statistical power and hampers the identification of stable and generalizable prognostic factors. As a consequence, the development of robust and clinically applicable prognostic models in GBC has remained insufficient. Currently, the TNM staging system established by the American Joint Committee on Cancer (AJCC) remains the primary tool for prognostic assessment in GBC [ 10 , 11 ] . However, increasing clinical evidence suggests that the purely anatomical TNM paradigm fails to adequately capture the substantial heterogeneity in survival outcomes observed among patients within the same stage [ 11 – 13 ] . This limitation highlights the need for more comprehensive prognostic frameworks that extend beyond anatomical tumor burden. Emerging evidence indicates that tumor progression is not solely determined by anatomical characteristics but is also profoundly influenced by the host systemic environment [ 14 , 15 ] . The host “internal environment”, encompassing systemic inflammatory responses, immune surveillance, and nutritional–metabolic status, plays a critical role in tumor evolution, microenvironment remodeling, and immune escape [ 7 , 16 – 19 ] . With advances in precision medicine, peripheral blood biomarkers—including mGPS, NLR, LCR, NWR, and CAR—have demonstrated increasing prognostic value in gallbladder cancer [ 18 , 20 – 24 ] . These composite indicators reflect systemic inflammation and immune homeostasis, offering advantages of being minimally invasive, repeatable, and capable of dynamically reflecting the host biological state [ 23 , 25 , 26 ] . Moreover, integrating hematological indicators with classical tumor biomarkers such as CA19-9 may provide a more comprehensive representation of tumor burden and host response [ 6 , 26 ] . While multimodal data-driven prognostic frameworks have achieved remarkable success across various high-incidence malignancies, their application in gallbladder cancer (GBC) remains considerably underdeveloped. In many solid tumors, the integration of peripheral blood indices, serum biomarkers, and clinicopathological variables has enabled the development of sophisticated predictive models that outperform traditional staging systems [ 27 – 29 ] . Nevertheless, translating these advances to GBC is challenging because of the disease’s intrinsic rarity, the limited number of surgical candidates, and its pronounced molecular heterogeneity [ 30 , 31 ] , which together create a substantial “data-sparsity bottleneck.” Consequently, systematic investigations of dynamic and biologically interpretable prognostic systems based on large-scale multicenter real-world data remain scarce. Addressing this gap is essential for improving risk stratification and enabling more individualized therapeutic strategies for patients with GBC. Consequently, leveraging a nationwide multicenter cohort of 1,354 patients with gallbladder cancer from 44 medical centers, we developed a multidimensional prognostic framework integrating clinicopathological characteristics (C-score), systemic inflammatory indicators (B-score), and tumor biomarkers (T-score). Independent prognostic factors were identified using LASSO and multivariable Cox regression, and an integrated predictive model was subsequently constructed through machine-learning–based benchmarking. The biological relevance of the scoring system was further explored using Gene Set Enrichment Analysis (GSEA). Finally, a web-based dynamic prediction platform was developed to facilitate individualized survival estimation and support data-driven clinical decision-making. Patients and Methods 2.1 Study Population and Selection Criteria This study retrospectively analyzed 10,231 patients with gallbladder cancer screened from the Multicenter Biliary Database between January 2000 and December 2020. The final analysis cohort consisted of 1,354 patients. The detailed screening process is illustrated in Fig. 1A, and the analytical workflow was implemented as illustrated in Fig. 1B. The exclusion criteria were as follows: 1)Patients who did not undergo curative-intent radical or extended radical resection (n = 5,321). 2)Missing essential clinicopathological variables, such as TNM components, R0 status, or tumor grade (n = 1,325). 3)Missing follow-up information (n = 674). 4)Missing key preoperative laboratory variables required for score construction (n = 1,321). 5)Perioperative mortality within 30 days (n = 30). 6)Concurrent primary malignancies (n = 206). 2.2 Data Collection and Follow-Up Demographic data, comorbidities, surgical variables, and laboratory findings were extracted from the Hospital Information Systems (HIS) of 44 hospitals. Preoperative laboratory values, obtained within one week prior to radical cholecystectomy, included measurements of C-reactive protein (CRP), white blood cells, neutrophils, lymphocytes, platelets, hemoglobin, total bilirubin (TB), albumin, and tumor markers (CEA, CA19-9, CA125, and AFP). Based on these laboratory results, derived indices were calculated. The baseline clinicopathological characteristics and derived indices are summarized in Table 1 . Table 1 Abbreviations, calculation methods, and descriptions of inflammatory metrics Metric Abbreviation Calculation Method Description LCR (Lymphocyte to CRP Ratio) Lymphocyte count (number/L) / CRP (mg/L) The ratio of lymphocyte count to C-reactive protein level NLR (Neutrophil to Lymphocyte Ratio) Neutrophil count (number/L) / Lymphocyte count (number/L) The ratio of neutrophil count to lymphocyte count PLR (Platelet to Lymphocyte Ratio) Platelets count (number/L) / Lymphocyte count (number/L) The ratio of platelet count to lymphocyte count LWR (Lymphocyte to White Blood Cell Ratio) Lymphocyte count (number/L) / White Blood Cell count (number/L) The ratio of lymphocyte count to white blood cell count NWR (Neutrophil to White Blood Cell Ratio) Neutrophil count (number/L) / White Blood Cell count (number/L) The ratio of neutrophil count to white blood cell count CAR (CRP to Albumin Ratio) C-Reactive Protein (mg/L) / Albumin (g/L) The ratio of C-reactive protein to albumin PNI (Prognostic Nutritional Index) PNI = 1×Albumin level (g/L) + 5×Total Lymphocyte Count (per L) A combined index based on albumin level and total lymphocyte count mGPS (modified Glasgow Prognostic Score) Based on serum markers: Score 0: CRP ≤ 10 mg/L and Albumin ≥ 35 g/L Score 1:CRP > 10 mg/L with normal Albumin Score 2: CRP > 10 mg/L with Albumin < 35 g/L A prognostic score based on CRP and albumin levels Patients were followed up through telephone calls, outpatient visits, or inpatient visits until December 2020 or until the patient's death. Follow-up occurred every 3 months for 3 years post-surgery. 2.3 Cohort Construction All 1,354 patients were randomly assigned to two cohorts using a computer-generated randomization method: Training cohort (n = 947): Used for model development and construction. Validation cohort (n = 407): Used for internal validation of the model. 2.4 Statistical Analysis and Variable Selection Statistical analyses were performed to identify independent prognostic factors for overall survival (OS). To mitigate multicollinearity among hematological indices, LASSO regression was performed for feature selection via the 'glmnet' package. Variables significant in univariate analysis (P < 0.05) or identified by LASSO were entered into a multivariate Cox model to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). 2.5 Score Construction and Survival Analysis Based on the independent prognostic factors identified via multivariable Cox regression, three risk-stratification scores (C-score, B-score, and T-score) were constructed. Each score was calculated as a linear combination of its constituent variables, weighted by their respective regression coefficients (β). Patients were subsequently stratified into high- and low-risk groups based on the calculated scores. Survival curves were generated using Kaplan-Meier analysis, and differences between groups were assessed via the log-rank test. 2.6Time-Dependent ROC Analysis The longitudinal predictive accuracy of the C-score, B-score, and T-score was evaluated using time-dependent receiver operating characteristic (ROC) curves. This model was employed to account for the time-varying nature of survival data and the influence of censored observations. The area under the curve (AUC) was estimated as a function of time, AUC(t), allowing for a continuous assessment of discriminative power throughout the follow-up period. 2.7 Time-Window Survival Analysis The temporal stability of the scoring systems was evaluated through a stratified time-window framework. The total follow-up period was partitioned into three predefined discrete intervals: 0–12, 12–36, and 36–48 months. For each interval, risk-group stratification was performed based on the calculated scores to determine their longitudinal discriminative capacity. Inter-group survival distributions within these specific windows were compared via the log-rank test and visualized using Kaplan-Meier curves. 2.8 Machine Learning Framework and Selection The prognostic architecture was determined by evaluating eight candidate algorithms, including logistic regression (standard and penalized), tree-based models (decision tree, random forest, and gradient boosting), support vector machines, k-nearest neighbors, and neural networks. Hyperparameter optimization was conducted via five-fold cross-validation. Models were assessed based on AUC and Brier score; those demonstrating significant performance deficits or instability across time points were systematically excluded. 2.9 Integrated Logistic Regression Model and Comparative Analysis An integrated logistic regression model was developed using a logistic regression framework that integrated clinicopathological (C-score), blood-based inflammatory (B-score), and tumor marker (T-score) components. The model utilized the aforementioned dynamic coefficients to generate weighted prognostic scores. Its longitudinal generalization was validated by comparing time-dependent AUC (TD-ROC) trajectories between the training and validation cohorts, with assessments performed at three-month intervals from 6 to 48 months. Furthermore, the predictive superiority of the integrated model was benchmarked against individual scoring systems to evaluate the synergistic effect of multi-dimensional integration throughout the postoperative course. 2.10 Model Validation and Clinical Utility Assessment The predictive performance and clinical relevance of the integrated logistic regression model were rigorously evaluated at 12, 24, and 36 months. Discriminative accuracy was quantified using time-dependent ROC curves and the area under the curve (AUC) with corresponding 95% confidence intervals (CIs) in both training and validation cohorts. The agreement between predicted probabilities and actual survival outcomes was assessed via calibration curves to verify the model’s reliability across different time horizons. Furthermore, decision curve analysis (DCA) was implemented to determine the clinical net benefit of the model across a range of threshold probabilities. This multi-faceted validation approach was employed to ensure the model's robustness and its potential utility in supporting clinical decision-making. 2.11 Class Imbalance Handling To address potential class imbalance in time-specific survival outcomes, SMOTE was applied to the training cohort during model development. Additional analyses after SMOTE correction were performed to assess the robustness of model performance. 2.12 Gene Set Enrichment Analysis To investigate the biological relevance of the C-, B-, and T-score framework, Gene Set Enrichment Analysis (GSEA) was performed using an in-house transcriptomic cohort of 44 gallbladder cancer tumor tissues with retrievable clinical annotation. For each sample, the C-, B-, and T-scores were calculated using the β coefficients derived from the multivariable Cox regression model in the training cohort. Samples were subsequently stratified into high- and low-score groups based on the median value of each score. GSEA was then conducted to compare transcriptomic profiles between the two groups. KEGG pathway gene sets were used as the reference database to identify enriched biological processes associated with each prognostic dimension. Statistical significance was evaluated using permutation testing (n = 1000). Pathways with a normalized enrichment score (NES) and a false discovery rate (FDR) < 0.05 were considered significantly enriched. 2.13 Development of a Dynamic Prognostic Web-Tool For clinical translation, a web-based dynamic prognostic platform was developed by integrating the C-, B-, and T-score components. The platform utilized the logistic regression framework to generate individualized survival probabilities across a follow-up range of 6 to 36 months. The tool was designed to provide real-time visualization of individualized survival curves and risk scores based on patient-specific clinical inputs. This application was implemented using the 'shiny' framework within R software (version 4.5.1) to facilitate data-driven decision-making in personalized risk management. Results Characteristics of Study Patients A total of 1,354 patients with gallbladder cancer were included and randomly assigned to a training cohort (n = 947) and a validation cohort (n = 407). The median age of the overall population was 63 years, and 57% of patients were female. The median follow-up time was 20.58 months. TNM stage distribution was comparable between the two cohorts, with stage IIIB being the most prevalent stage. Most baseline clinicopathological characteristics were comparable between the training and validation cohorts, although sex distribution and albumin level differed modestly (Table 2 and Table S1 ). These findings indicate good cohort comparability, providing a reliable foundation for subsequent analyses. Independent Prognostic Factors To address potential multicollinearity among hematological and derived indices, LASSO regression was applied for feature selection and regularization (Figs. 2A-C). This approach effectively reduced redundancy and identified neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-CRP ratio (LCR) as independent prognostic indicators (Table 3 ). Table 3 Feature Selection and Corresponding Coefficients Derived from LASSO-Cox Regression Feature Coef(min) Coef(1se) mGPS 0.237 NLR 0.319 0.152 TB 0.001 SII 0.116 ALT 2.362E-04 GGT 1.724E-04 CREA -4.103E-04 GLU -1.238E-03 PT -9.92E-05 LCR -0.911 -0.509 In this study, univariate and multivariable Cox regression analyses were performed to identify key prognostic factors for patients with gallbladder cancer. Univariate analysis revealed that age, diabetes, tumor size, CEA, and CA19-9 (log1p) were significantly associated with overall survival (Table 4 ). Table 4 Results of Univariate and Multivariable Cox Regression Analyses Characteristic N = 947 Univariate Cox Analysis Multivariate Cox Analysis Hazard Ratio (95%CI) P-value Hazard Ratio (95%CI) P-value Age 1.015 (1.007–1.024) < 0.001 1.015 (1.005–1.024) 0.004 Hypertension No 1 Yes 1.096 (0.920–1.306) 0.305 Diabetes No 1 1 Yes 1.314 (1.030–1.675) 0.028 1.251 (0.932–1.680) 0.136 Polyp No 1 1 Yes 0.508 (0.314–0.823) 0.006 0.723 (0.401–1.304) 0.282 Gallstone No 1 Yes 1.119 (0.949–1.320) 0.182 Surgery type 2 1 1 3 1.662 (1.283–2.153) < 0.001 1.159 (0.860–1.563) 0.332 Bile duct resection No 1 1 Yes 1.479 (1.247–1.755) < 0.001 1.223 (0.880–1.622) 0.207 Liver resection None 1 1 Limited 1.384 (1.158–1.654) < 0.001 1.164 (0.882–1.564) 0.131 Major 1.539 (0.946–2.504) 0.083 1.369 (0.792–2.368) 0.261 R0 Yes 1 1 No 3.086 (2.567–3.710) < 0.001 2.479 (1.984–3.098) < 0.001 TNM Tis 1 1 I 1.409 (0.415–4.785) 0.583 1.288 (0.376–4.415) 0.687 II 2.693 (0.815–8.899) 0.104 2.243 (0.668–7.530) 0.191 IIIA 3.829 (1.220–12.015) 0.021 3.121 (0.977–9.969) 0.055 IIIB 6.132 (1.957–19.218) 0.002 4.604 (1.438–14.736) 0.01 IVA 6.552 (1.991–21.563) 0.002 4.284 (1.277–14.370) 0.018 IVB 11.642 (3.680–36.829) < 0.001 8.009 (2.473–25.942) < 0.001 Grade Well 1 1 Moderate 1.448 (1.065–1.969) 0.018 0.857 (0.598–1.227) 0.399 Poor 1.988 (1.466–2.696) < 0.001 1.210 (0.846–1.729) 0.297 Tumor size 1.006 (1.002–1.010) 0.002 1.003 (0.998–1.007) 0.261 CEA (log1p) 1.091 (1.022–1.165) 0.009 1.010 (0.961–1.061) 0.701 CA19-9 (log1p) 1.168 (1.121–1.218) < 0.001 1.112 (1.078–1.146) < 0.001 CA125 (log1p) 1.056 (0.997–1.117) 0.062 AFP (log1p) 1.013 (0.942–1.090) 0.727 L 0.325 (0.273–0.388) < 0.001 CRP (log1p) 1.361 (1.273–1.456) < 0.001 ALB 0.998 (0.995–1.001) 0.198 WBC 0.999 (0.995–1.002) 0.464 NEU 1.001 (0.985–1.018) 0.867 Hb 0.997 (0.993–1.000) 0.08 PLT 1.001 (1.000–1.002) 0.06 TB 1.001 (1.001–1.002) < 0.001 BUN 0.998 (0.994–1.001) 0.121 GLU 0.995 (0.987–1.002) 0.18 INR 0.999 (0.998–1.001) 0.22 PT 0.999 (0.997–1.000) 0.138 FIB 0.998 (0.996–1.000) 0.071 mGPS 0 1 1 1.630 (1.375–1.934) < 0.001 2 1.798 (1.396–2.317) < 0.001 PNI 0.991 (0.981–1.001) 0.064 SII (log1p) 1.380 (1.243–1.532) < 0.001 NLR (log1p) 2.138 (1.789–2.556) < 0.001 1.114 (1.068–1.162) < 0.001 LCR 0.606 (0.538–0.682) < 0.001 0.659 (0.580–0.749) < 0.001 Subsequent multivariable Cox regression confirmed that age, R0 resection status, TNM stage, CA19-9 (log1p), NLR, and LCR were independent predictors of survival. Specifically, age (HR = 1.015, P = 0.004), non-R0 resection (HR = 2.479, P < 0.001), higher TNM stage (overall P < 0.001), CA19-9 (log1p) (HR = 1.112, P < 0.001), and NLR (HR = 1.114, P < 0.001) were associated with increased mortality risk, whereas LCR (HR = 0.659, P < 0.001) was a protective factor. These findings indicate that, after adjusting for other variables, these factors independently predict survival outcomes in patients with gallbladder cancer. Development of Clinical, Biomarker, and Tumor Marker Scores To evaluate the prognosis of patients with gallbladder cancer, three risk scores were constructed based on the results of multivariable Cox regression: a clinicopathological score (C-score), a blood-based inflammatory score (B-score), and a tumor marker score (T-score). The C-score incorporates patients’ clinical characteristics, the B-score integrates hematological indicators, and the T-score reflects tumor marker levels. Each score was calculated as a weighted linear combination of its corresponding variables, with weights defined by the regression coefficients derived from the multivariable Cox model (Table 5 ). Kaplan–Meier analysis demonstrated that all three scores effectively stratified patients according to survival risk, indicating good prognostic discrimination (Figs. 2D-L). Each score was calculated using the corresponding regression coefficients (β), as follows: Table 5 Construction of the C-score, B-score, and T-score based on multivariable Cox regression Score Variable Coding / Unit HR (95% CI) β coefficient (ln(HR)) Contribution to score C-score Age per 1-year increase 1.015 (1.005–1.024) 0.02 0.015 × Age R0 resection status No = 1, Yes = 0 2.479 (1.984–3.098) 0.91 0.908 × R0 status TNM stage II II vs Tis 2.243 (0.668–7.530) 0.81 0.808 × TNM II TNM stage IIIA IIIA vs Tis 3.121 (0.977–9.969) 1.14 1.138 × TNM IIIA TNM stage IIIB IIIB vs Tis 4.604 (1.438–14.736) 1.53 1.527 × TNM IIIB TNM stage IVA IVA vs Tis 4.284 (1.277–14.370) 1.46 1.455 × TNM IVA TNM stage IVB IVB vs Tis 8.009 (2.473–25.942) 2.08 2.081 × TNM IVB B-score NLR (log1p) continuous 1.114 (1.068–1.162) 0.11 0.108 × NLR LCR continuous 0.659 (0.580–0.749) -0.42 -0.417 × LCR T-score CA19-9 (log1p) continuous 1.112 (1.078–1.146) 0.11 0.106 × CA19-9 C-score = βage × Age + βR0 × R0 status + ΣβTNMi × TNMi B-score = βNLR × NLR + βLCR × LCR T-score = βCA19-9 × CA19-9 (log1p) Dynamic Prognostic Assessment Using Risk Scores Time-dependent AUC analysis showed that the prognostic performance of the three risk scores (C-score, B-score, and T-score) changed over time. In both the training and validation cohorts, the AUC values of all three scores gradually declined with increasing follow-up duration (Figs. 3A-B). To further characterize their temporal prognostic value, patients were stratified into three follow-up windows (0–12 months, 12–36 months, and 36–48 months).In the early postoperative period (0–12 months), all three scores significantly stratified prognosis, with C-score and B-score showing stronger discrimination than T-score. In the 12–36 month interval, both C-score and T-score maintained significant discriminative ability, whereas B-score showed weaker and non-significant separation (P = 0.43). In the 36–48 month interval, only T-score retained significant prognostic value (P = 0.00025), while neither C-score nor B-score showed significant discrimination (P = 0.6 and P = 0.15, respectively) (Figs. 3C-E). Taken together, these findings suggest that C-score and B-score are more informative for early prognostic assessment, whereas T-score provides the most durable prognostic value over longer follow-up periods. These results support the use of a dynamic, time-window-based evaluation strategy for risk stratification and clinical outcome prediction in patients with gallbladder cancer. Model Selection and Performance Evaluation To identify the optimal predictive framework, eight candidate algorithms were evaluated using five-fold cross-validation at 12, 24, and 36 months (Figs. 4A–B). Model performance was assessed using the area under the curve (AUC) and Brier score. Among the evaluated models, logistic regression demonstrated the most stable performance across time points, consistently achieving high AUC values and low Brier scores in both training and validation cohorts (Figures S1 A–C). Given its robust predictive performance, interpretability, and suitability for clinical application, logistic regression was selected as the final integrated model. Further analysis of regression coefficient dynamics revealed temporal trends in key predictors, indicating heterogeneity in their contributions across different follow-up windows (Fig. 4C). The time-dependent ROC analysis further demonstrated consistent predictive performance of the integrated model in both training and validation cohorts, with minimal differences between the two datasets (Fig. 4D), supporting the robustness and generalizability of the model. Finally, the performance of the integrated model was compared with the individual C-, B-, and T-scores (Figs. 4E–F). The integrated model consistently outperformed the individual scores across all time points, particularly during the early postoperative period. These findings suggest that integrating multiple prognostic dimensions substantially improves the model’s discriminative ability for survival prediction. Performance Evaluation of the Integrated Logistic Regression Model The integrated prognostic model, constructed using logistic regression, was comprehensively evaluated through ROC curves, calibration plots, and decision curve analysis (DCA). At 12-, 24-, and 36-month time points in both training and validation cohorts, ROC curves demonstrated robust predictive performance. The AUC values in the training cohort were 0.857 (95% CI: 0.832–0.882) at 12 months, 0.808 (95% CI: 0.781–0.835) at 24 months, and 0.804 (95% CI: 0.776–0.831) at 36 months. In the validation cohort, the corresponding AUCs were 0.857 (95% CI: 0.820–0.895), 0.799 (95% CI: 0.757–0.841), and 0.770 (95% CI: 0.724–0.816)(Figs. 5A-B), indicating consistent predictive ability across time windows. Calibration curve analysis revealed high concordance between predicted and observed survival probabilities at all three time points, confirming the model’s reliability(Figs. 5C-D). DCA further demonstrated that the model provided substantial clinical net benefit across different threshold probabilities, particularly at 12 and 24 months, highlighting its value for clinical decision-making(Figs. 5E-F). Sensitivity analyses after SMOTE correction showed consistent model performance, supporting the robustness of the integrated model (Figures S2 A-D). Biological Mechanisms and Clinical Translation of the C-, B-, and T-Score System To further elucidate the biological mechanisms underlying the association of the C-, B-, and T-scores with gallbladder cancer (GBC), we performed Gene Set Enrichment Analysis (GSEA). As illustrated in Figs. 6A–C, GSEA identified significant enrichment of specific signaling pathways associated with each score, revealing the distinct molecular landscapes of GBC. Specifically, the C-score was closely linked to pathways governing tumor invasiveness and matrix remodeling, such as ECM-receptor interaction, Hedgehog, and Wnt signaling (Fig. 6A). The B-score exhibited a strong correlation with the immune microenvironment and inflammatory responses, showing enrichment in the JAK-STAT and NF-kappa B pathways, as well as the PD-1/PD-L1 checkpoint (Fig. 6B). Meanwhile, the T-score was primarily associated with metabolic stress and cell cycle regulation, including the HIF-1 and p53 signaling pathways (Fig. 6C). These enriched pathways provide functional insights into the prognostic value of the three scores and offer prioritized directions for future experimental validation. To facilitate clinical translation, we developed a web-based dynamic prognostic tool(Fig. 6D). This platform integrates multiple variables, including the C-, B-, and T-scores, to predict individualized survival probabilities across various time horizons (ranging from 6 to 36 months). By inputting patient-specific clinical data, clinicians can generate real-time individualized survival curves and visualize the corresponding prognostic scores. This user-friendly tool provides a precise risk assessment platform, empowering clinicians to make data-driven, individualized therapeutic decisions and providing a more intuitive approach for personalized risk management in GBC patients. The web-based tool is accessible at: https://wmy123456.shinyapps.io/gbc_iscore_app/ The source code for the web-based application is available upon reasonable request. Discussion n this large-scale multicenter study involving 1,354 patients with gallbladder cancer from 44 medical centers, we developed and validated a multidimensional prognostic framework that substantially improves risk stratification beyond conventional staging systems. By integrating clinicopathological characteristics, systemic inflammatory markers, and tumor biomarkers, the proposed model not only achieved superior predictive performance compared with individual scoring systems but also provided biologically interpretable insights into tumor–host interactions. More importantly, this framework addresses a critical clinical gap by enabling more precise identification of high-risk patients, thereby offering a potential tool to guide individualized postoperative management. Gallbladder cancer (GBC) remains a challenging malignancy due to its aggressive behavior and poor prognosis [ 4 , 32 – 34 ] . Although the current TNM staging system maintains a central role in prognostic assessment, its inherent anatomical limitations have become increasingly pronounced among highly heterogeneous early-stage patients, failing to fully capture the complex interplay between tumor progression and host anti-tumor immunity [ 35 – 38 ] . In alignment with the burgeoning field of Systems Oncology, this study constructed a composite predictive model integrating clinical characteristics (C-score), the systemic inflammatory landscape (B-score), and tumor biomarkers (T-score). This model complements conventional anatomical staging by incorporating multidimensional biological information for refined risk stratification. Each component of the model is supported by plausible pathophysiological rationale. The B-score captures the systemic immune–inflammatory status, with NLR and LCR reflecting the balance between pro-tumor inflammation and anti-tumor immunity [ 39 ] . This aligns with growing evidence that tumor progression is closely linked to systemic immune dysregulation [ 19 , 40 , 41 ] . The T-score, driven by CA19-9 levels, reflects not only tumor burden but also occult biological aggressiveness, such as early micrometastasis and vascular invasion [ 11 , 42 ] . Meanwhile, the C-score incorporates established clinical determinants, including R0 resection status and TNM stage, providing an anatomical and surgical baseline.Together, these components construct a multidimensional representation of tumor–host interactions, which may explain the superior predictive performance of the integrated model. A pivotal highlight of this study is the deep correlation established between clinical phenotypes and molecular mechanisms via GSEA analysis. The findings reveal that the C-score is significantly associated with ECM-receptor interaction and Hedgehog pathways, precisely echoing the pathological process of tumor cell expansion through regulated matrix remodeling [ 43 – 45 ] . The strong correlation between the B-score and the JAK-STAT, NF-κB, and immune checkpoint pathways confirms at the molecular level that peripheral blood inflammatory markers can effectively map the local immunosuppressive microenvironment (characterizing the transition from \"cold\" to \"hot\" tumors) [ 45 ] . Furthermore, the T-score, which was primarily driven by CA19-9, was associated with HIF-1 and p53 signaling pathways, reflecting the survival adaptation strategies of tumor cells under hypoxia and high metabolic stress [ 46 , 47 ] . These findings provide biological support for the prognostic relevance of the model and enhance its interpretability in the context of precision medicine. Regarding the robustness of the research design, this study relies on a multicentercohort of 1,354 patients from 44 medical centers nationwide. Given the rarity of gallbladder cancer, this large multicenter cohort enhances the credibility and generalizability of the findings. Although the model has yet to fully integrate dynamic response data from neoadjuvant/adjuvant immune checkpoint inhibitors (ICI), deep learning-based radiomics features, or genomic mutation profiles (such as ERBB2/TP53 status [ 48 ] ), this multidimensional framework may provide a basis for future expansion toward more comprehensive prognostic models. To bridge the gap between basic algorithms and clinical application, we further developed a web-based dynamic prognostic prediction platform designed to transform complex mathematical logic into intuitive, individualized survival curves. In an era where clinical decision-making increasingly relies on evidence and data, this platform achieves real-time, visualized prognostic assessment. It may assist clinicians in refining adjuvant treatment strategies at different follow-up intervals and establishes an open iterative framework for integrating more complex biomarkers in the future. In summary, the comprehensive assessment system constructed in this study deepens the understanding of the nature of GBC prognosis and serves as a powerful implementation of the \"patient-centered\" precision medicine philosophy, opening new pathways for the individualized management of gallbladder cancer. Importantly, this model is not merely predictive but has the potential to inform clinical decision-making, particularly in identifying patients who may benefit from intensified adjuvant therapy and tailored follow-up strategies. This study has several limitations. First, the retrospective design may have introduced potential selection bias. Second, although the cohort was derived from 44 centers, external validation in independent datasets is still warranted. Third, potential inter-center heterogeneity in surgical management and laboratory testing may have influenced the results. Finally, the biological insights from GSEA remain exploratory and require further experimental validation.The transcriptomic analysis was exploratory and requires further experimental validation. Conclusion In conclusion, we developed and validated a multidimensional prognostic system for gallbladder cancer integrating clinicopathological features, systemic inflammatory markers, and tumor biomarkers. The integrated model demonstrated superior predictive performance and effectively captured the dynamic heterogeneity of postoperative survival. The associated molecular signatures identified by GSEA further supported the biological relevance of the C-, B-, and T-score framework. Implemented as a web-based tool, this system provides a practical platform for individualized risk stratification and prognosis prediction in patients with gallbladder cancer. Abbreviations GBC gallbladder cancer CRGGC Chinese Research Group of Gallbladder Cancer NLR neutrophil-to-lymphocyte ratio LCR lymphocyte-to-C-reactive protein ratio ROC receiver operating characteristic AUC area under the curve DCA decision curve analysis GSEA Gene Set Enrichment Analysis OS overall survival SMOTE Synthetic Minority Oversampling Technique AJCC American Joint Committee on Cancer CRP C-reactive protein CEA carcinoembryonic antigen AFP alpha-fetoprotein. Declarations Ethics approval and consent to participate This multicenter retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine (Approval No. XHEC-C-2025-113-2). The requirement for informed consent was waived by the same Ethics Committee due to the retrospective nature of the study. Consent for publication Not applicable. Availability of data and materials The data used in this study were obtained from the multicenter gallbladder cancer cohort database of the Chinese Research Group of Gallbladder Cancer (CRGGC). The data are not publicly available due to privacy and data-sharing restrictions, but may be available from the corresponding author upon reasonable request and with permission from the CRGGC. The source code for the web-based application is available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the National Natural Science Foundation of China (Grant Nos. 82272691 and 82373370), the Shanghai Municipal Health Commission Leading Talent Program (Grant No. 2022XD010), the Shanghai “Yiyuan Xinxing” Young Medical Talents Training Funding Program (2024), and the Shanghai Municipal Health Commission Health Industry Research Special Program (Grant No. 20254Z0005). Authors ’ contributions Mingyang Wang: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing – original draft. Runfa Bao: Data curation, Formal analysis, Validation, Writing – review & editing. Ziyi Yang: Data curation, Investigation, Validation, Writing – review & editing. Zhengyu Chen, Lei Kong and Shengxin Gu: Data curation, Investigation. Wei Gong, Jun Gu, Xiangsong Wu and Yidi Zhu: Conceptualization, Supervision, Funding acquisition, Writing – review & editing. 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Supplementary Files SupplementaryMaterialTable.docx Supplementaryfigures.pptx Table2.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 08 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviewers agreed at journal 02 May, 2026 Reviews received at journal 01 May, 2026 Reviewers agreed at journal 01 May, 2026 Reviews received at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Editor invited by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 01 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. 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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-9222230\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":619154386,\"identity\":\"a07dd025-b30c-4284-a695-b6265c369778\",\"order_by\":0,\"name\":\"Mingyang Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of 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10:26:40\",\"extension\":\"docx\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":17970,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterialTable.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9222230/v1/b3979fdd9afa3678db548aae.docx\"},{\"id\":106875820,\"identity\":\"e9a85056-9c81-4691-8b5c-1863f82ba492\",\"added_by\":\"auto\",\"created_at\":\"2026-04-14 10:26:28\",\"extension\":\"pptx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":851679,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementaryfigures.pptx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9222230/v1/80da0130c1da79a4c261240c.pptx\"},{\"id\":106875835,\"identity\":\"102ed0da-6999-44d6-9c02-e3cbb7fbf9cc\",\"added_by\":\"auto\",\"created_at\":\"2026-04-14 10:26:40\",\"extension\":\"docx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":19558,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Table2.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9222230/v1/ef25cda3a9b3df74383961b6.docx\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Multidimensional Prognostic Model for Gallbladder Cancer Based on a Multicenter Cohort Integrating Clinicopathological Features, Systemic Inflammation, and Tumor Biomarkers\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eGallbladder cancer (GBC), the most common malignancy of the biliary tract, remains one of the most lethal gastrointestinal cancers despite its relatively low incidence\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. ts clinical course is characterized by insidious onset, rapid progression, and a striking lack of early diagnostic indicators, resulting in most patients being diagnosed at an advanced stage when curative treatment is no longer feasible\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. Even among the minority of patients who undergo radical surgery, long-term outcomes remain poor, with a 5-year survival rate of only approximately 10%-20%\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. Collectively, these features underscore a fundamental clinical reality: GBC is not only difficult to detect early, but also extremely challenging to control even when treated aggressively.\\u003c/p\\u003e \\u003cp\\u003eRadical (R0) resection is currently regarded as the only potentially curative treatment for GBC. However, an alarming and clinically frustrating reality persists: even after apparently complete tumor removal, survival outcomes remain highly heterogeneous and largely unpredictable\\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. While a subset of patients may achieve meaningful long-term survival, a considerable proportion experience early recurrence and rapid disease progression within a short period after surgery\\u003csup\\u003e[\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e]\\u003c/sup\\u003e. Critically, this early failure often occurs despite \\u0026ldquo;curative\\u0026rdquo; resection, indicating that conventional surgical success does not necessarily translate into favorable prognosis. In clinical practice, this creates a profound dilemma, as physicians lack reliable tools to identify high-risk individuals at the time of treatment, thereby limiting the ability to tailor postoperative strategies such as adjuvant therapy and surveillance intensity.\\u003c/p\\u003e \\u003cp\\u003eThis challenge is further exacerbated by the relatively low incidence of GBC and the limited number of patients eligible for surgical intervention. Most existing studies are derived from single-center cohorts with small sample sizes, which restricts statistical power and hampers the identification of stable and generalizable prognostic factors. As a consequence, the development of robust and clinically applicable prognostic models in GBC has remained insufficient.\\u003c/p\\u003e \\u003cp\\u003eCurrently, the TNM staging system established by the American Joint Committee on Cancer (AJCC) remains the primary tool for prognostic assessment in GBC\\u003csup\\u003e[\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e. However, increasing clinical evidence suggests that the purely anatomical TNM paradigm fails to adequately capture the substantial heterogeneity in survival outcomes observed among patients within the same stage\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR12\\\" citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e. This limitation highlights the need for more comprehensive prognostic frameworks that extend beyond anatomical tumor burden.\\u003c/p\\u003e \\u003cp\\u003eEmerging evidence indicates that tumor progression is not solely determined by anatomical characteristics but is also profoundly influenced by the host systemic environment\\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e. The host \\u0026ldquo;internal environment\\u0026rdquo;, encompassing systemic inflammatory responses, immune surveillance, and nutritional\\u0026ndash;metabolic status, plays a critical role in tumor evolution, microenvironment remodeling, and immune escape\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR17 CR18\\\" citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e. With advances in precision medicine, peripheral blood biomarkers\\u0026mdash;including mGPS, NLR, LCR, NWR, and CAR\\u0026mdash;have demonstrated increasing prognostic value in gallbladder cancer\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR21 CR22 CR23\\\" citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]\\u003c/sup\\u003e. These composite indicators reflect systemic inflammation and immune homeostasis, offering advantages of being minimally invasive, repeatable, and capable of dynamically reflecting the host biological state\\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e. Moreover, integrating hematological indicators with classical tumor biomarkers such as CA19-9 may provide a more comprehensive representation of tumor burden and host response\\u003csup\\u003e[\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eWhile multimodal data-driven prognostic frameworks have achieved remarkable success across various high-incidence malignancies, their application in gallbladder cancer (GBC) remains considerably underdeveloped. In many solid tumors, the integration of peripheral blood indices, serum biomarkers, and clinicopathological variables has enabled the development of sophisticated predictive models that outperform traditional staging systems\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR28\\\" citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e]\\u003c/sup\\u003e. Nevertheless, translating these advances to GBC is challenging because of the disease\\u0026rsquo;s intrinsic rarity, the limited number of surgical candidates, and its pronounced molecular heterogeneity\\u003csup\\u003e[\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]\\u003c/sup\\u003e, which together create a substantial \\u0026ldquo;data-sparsity bottleneck.\\u0026rdquo; Consequently, systematic investigations of dynamic and biologically interpretable prognostic systems based on large-scale multicenter real-world data remain scarce. Addressing this gap is essential for improving risk stratification and enabling more individualized therapeutic strategies for patients with GBC.\\u003c/p\\u003e \\u003cp\\u003eConsequently, leveraging a nationwide multicenter cohort of 1,354 patients with gallbladder cancer from 44 medical centers, we developed a multidimensional prognostic framework integrating clinicopathological characteristics (C-score), systemic inflammatory indicators (B-score), and tumor biomarkers (T-score). Independent prognostic factors were identified using LASSO and multivariable Cox regression, and an integrated predictive model was subsequently constructed through machine-learning\\u0026ndash;based benchmarking. The biological relevance of the scoring system was further explored using Gene Set Enrichment Analysis (GSEA). Finally, a web-based dynamic prediction platform was developed to facilitate individualized survival estimation and support data-driven clinical decision-making.\\u003c/p\\u003e\"},{\"header\":\"Patients and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Study Population and Selection Criteria\\u003c/h2\\u003e \\u003cp\\u003eThis study retrospectively analyzed 10,231 patients with gallbladder cancer screened from the Multicenter Biliary Database between January 2000 and December 2020. The final analysis cohort consisted of 1,354 patients. The detailed screening process is illustrated in Fig.\\u0026nbsp;1A, and the analytical workflow was implemented as illustrated in Fig.\\u0026nbsp;1B.\\u003c/p\\u003e \\u003cp\\u003eThe exclusion criteria were as follows:\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003e1)Patients who did not undergo curative-intent radical or extended radical resection (n = 5,321).\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e2)Missing essential clinicopathological variables, such as TNM components, R0 status, or tumor grade (n = 1,325).\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e3)Missing follow-up information (n = 674).\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e4)Missing key preoperative laboratory variables required for score construction (n = 1,321).\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e5)Perioperative mortality within 30 days (n = 30).\\u003c/h3\\u003e\\n\\n\\u003ch3\\u003e6)Concurrent primary malignancies (n = 206).\\u003c/h3\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 Data Collection and Follow-Up\\u003c/h2\\u003e \\u003cp\\u003eDemographic data, comorbidities, surgical variables, and laboratory findings were extracted from the Hospital Information Systems (HIS) of 44 hospitals. Preoperative laboratory values, obtained within one week prior to radical cholecystectomy, included measurements of C-reactive protein (CRP), white blood cells, neutrophils, lymphocytes, platelets, hemoglobin, total bilirubin (TB), albumin, and tumor markers (CEA, CA19-9, CA125, and AFP). Based on these laboratory results, derived indices were calculated. The baseline clinicopathological characteristics and derived indices are summarized in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eAbbreviations, calculation methods, and descriptions of inflammatory metrics\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMetric Abbreviation\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCalculation Method\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDescription\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLCR (Lymphocyte to CRP Ratio)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLymphocyte count (number/L) / CRP (mg/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe ratio of lymphocyte count to C-reactive protein level\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR (Neutrophil to Lymphocyte Ratio)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNeutrophil count (number/L) / Lymphocyte count (number/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe ratio of neutrophil count to lymphocyte count\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePLR (Platelet to Lymphocyte Ratio)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePlatelets count (number/L) / Lymphocyte count (number/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe ratio of platelet count to lymphocyte count\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLWR (Lymphocyte to White Blood Cell Ratio)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLymphocyte count (number/L) / White Blood Cell count (number/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe ratio of lymphocyte count to white blood cell count\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNWR (Neutrophil to White Blood Cell Ratio)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNeutrophil count (number/L) / White Blood Cell count (number/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe ratio of neutrophil count to white blood cell count\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCAR (CRP to Albumin Ratio)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eC-Reactive Protein (mg/L) / Albumin (g/L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThe ratio of C-reactive protein to albumin\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePNI (Prognostic Nutritional Index)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePNI\\u0026thinsp;=\\u0026thinsp;1\\u0026times;Albumin level (g/L)\\u0026thinsp;+\\u0026thinsp;5\\u0026times;Total Lymphocyte Count (per L)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eA combined index based on albumin level and total lymphocyte count\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emGPS (modified Glasgow Prognostic Score)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBased on serum markers:\\u003c/p\\u003e \\u003cp\\u003eScore 0: CRP\\u0026thinsp;\\u0026le;\\u0026thinsp;10 mg/L and Albumin\\u0026thinsp;\\u0026ge;\\u0026thinsp;35 g/L\\u003c/p\\u003e \\u003cp\\u003eScore 1:CRP\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 mg/L with normal Albumin Score 2: CRP\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 mg/L with Albumin\\u0026thinsp;\\u0026lt;\\u0026thinsp;35 g/L\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eA prognostic score based on CRP and albumin levels\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003ePatients were followed up through telephone calls, outpatient visits, or inpatient visits until December 2020 or until the patient's death. Follow-up occurred every 3 months for 3 years post-surgery.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Cohort Construction\\u003c/h2\\u003e \\u003cp\\u003eAll 1,354 patients were randomly assigned to two cohorts using a computer-generated randomization method:\\u003c/p\\u003e \\u003cp\\u003e \\u003col\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eTraining cohort (n\\u0026thinsp;=\\u0026thinsp;947): Used for model development and construction.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003cspan\\u003e \\u003cli\\u003e \\u003cp\\u003eValidation cohort (n\\u0026thinsp;=\\u0026thinsp;407): Used for internal validation of the model.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/span\\u003e \\u003c/ol\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4 Statistical Analysis and Variable Selection\\u003c/h2\\u003e \\u003cp\\u003eStatistical analyses were performed to identify independent prognostic factors for overall survival (OS). To mitigate multicollinearity among hematological indices, LASSO regression was performed for feature selection via the 'glmnet' package. Variables significant in univariate analysis (P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) or identified by LASSO were entered into a multivariate Cox model to calculate hazard ratios (HRs) and 95% confidence intervals (CIs).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5 Score Construction and Survival Analysis\\u003c/h2\\u003e \\u003cp\\u003eBased on the independent prognostic factors identified via multivariable Cox regression, three risk-stratification scores (C-score, B-score, and T-score) were constructed. Each score was calculated as a linear combination of its constituent variables, weighted by their respective regression coefficients (β). Patients were subsequently stratified into high- and low-risk groups based on the calculated scores. Survival curves were generated using Kaplan-Meier analysis, and differences between groups were assessed via the log-rank test.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6Time-Dependent ROC Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe longitudinal predictive accuracy of the C-score, B-score, and T-score was evaluated using time-dependent receiver operating characteristic (ROC) curves. This model was employed to account for the time-varying nature of survival data and the influence of censored observations. The area under the curve (AUC) was estimated as a function of time, AUC(t), allowing for a continuous assessment of discriminative power throughout the follow-up period.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.7 Time-Window Survival Analysis\\u003c/h2\\u003e \\u003cp\\u003eThe temporal stability of the scoring systems was evaluated through a stratified time-window framework. The total follow-up period was partitioned into three predefined discrete intervals: 0\\u0026ndash;12, 12\\u0026ndash;36, and 36\\u0026ndash;48 months. For each interval, risk-group stratification was performed based on the calculated scores to determine their longitudinal discriminative capacity. Inter-group survival distributions within these specific windows were compared via the log-rank test and visualized using Kaplan-Meier curves.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.8 Machine Learning Framework and Selection\\u003c/h2\\u003e \\u003cp\\u003eThe prognostic architecture was determined by evaluating eight candidate algorithms, including logistic regression (standard and penalized), tree-based models (decision tree, random forest, and gradient boosting), support vector machines, k-nearest neighbors, and neural networks. Hyperparameter optimization was conducted via five-fold cross-validation. Models were assessed based on AUC and Brier score; those demonstrating significant performance deficits or instability across time points were systematically excluded.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.9 Integrated Logistic Regression Model and Comparative Analysis\\u003c/h2\\u003e \\u003cp\\u003eAn integrated logistic regression model was developed using a logistic regression framework that integrated clinicopathological (C-score), blood-based inflammatory (B-score), and tumor marker (T-score) components. The model utilized the aforementioned dynamic coefficients to generate weighted prognostic scores. Its longitudinal generalization was validated by comparing time-dependent AUC (TD-ROC) trajectories between the training and validation cohorts, with assessments performed at three-month intervals from 6 to 48 months. Furthermore, the predictive superiority of the integrated model was benchmarked against individual scoring systems to evaluate the synergistic effect of multi-dimensional integration throughout the postoperative course.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.10 Model Validation and Clinical Utility Assessment\\u003c/h2\\u003e \\u003cp\\u003eThe predictive performance and clinical relevance of the integrated logistic regression model were rigorously evaluated at 12, 24, and 36 months. Discriminative accuracy was quantified using time-dependent ROC curves and the area under the curve (AUC) with corresponding 95% confidence intervals (CIs) in both training and validation cohorts. The agreement between predicted probabilities and actual survival outcomes was assessed via calibration curves to verify the model\\u0026rsquo;s reliability across different time horizons. Furthermore, decision curve analysis (DCA) was implemented to determine the clinical net benefit of the model across a range of threshold probabilities. This multi-faceted validation approach was employed to ensure the model's robustness and its potential utility in supporting clinical decision-making.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.11 Class Imbalance Handling\\u003c/h2\\u003e \\u003cp\\u003eTo address potential class imbalance in time-specific survival outcomes, SMOTE was applied to the training cohort during model development. Additional analyses after SMOTE correction were performed to assess the robustness of model performance.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.12 Gene Set Enrichment Analysis\\u003c/h2\\u003e \\u003cp\\u003eTo investigate the biological relevance of the C-, B-, and T-score framework, Gene Set Enrichment Analysis (GSEA) was performed using an in-house transcriptomic cohort of 44 gallbladder cancer tumor tissues with retrievable clinical annotation. For each sample, the C-, B-, and T-scores were calculated using the β coefficients derived from the multivariable Cox regression model in the training cohort.\\u003c/p\\u003e \\u003cp\\u003eSamples were subsequently stratified into high- and low-score groups based on the median value of each score. GSEA was then conducted to compare transcriptomic profiles between the two groups. KEGG pathway gene sets were used as the reference database to identify enriched biological processes associated with each prognostic dimension.\\u003c/p\\u003e \\u003cp\\u003eStatistical significance was evaluated using permutation testing (n\\u0026thinsp;=\\u0026thinsp;1000). Pathways with a normalized enrichment score (NES) and a false discovery rate (FDR)\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 were considered significantly enriched.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.13 Development of a Dynamic Prognostic Web-Tool\\u003c/h2\\u003e \\u003cp\\u003eFor clinical translation, a web-based dynamic prognostic platform was developed by integrating the C-, B-, and T-score components. The platform utilized the logistic regression framework to generate individualized survival probabilities across a follow-up range of 6 to 36 months. The tool was designed to provide real-time visualization of individualized survival curves and risk scores based on patient-specific clinical inputs. This application was implemented using the 'shiny' framework within R software (version 4.5.1) to facilitate data-driven decision-making in personalized risk management.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eCharacteristics of Study Patients\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 1,354 patients with gallbladder cancer were included and randomly assigned to a training cohort (n\\u0026thinsp;=\\u0026thinsp;947) and a validation cohort (n\\u0026thinsp;=\\u0026thinsp;407). The median age of the overall population was 63 years, and 57% of patients were female. The median follow-up time was 20.58 months. TNM stage distribution was comparable between the two cohorts, with stage IIIB being the most prevalent stage. Most baseline clinicopathological characteristics were comparable between the training and validation cohorts, although sex distribution and albumin level differed modestly (Table \\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). These findings indicate good cohort comparability, providing a reliable foundation for subsequent analyses.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eIndependent Prognostic Factors\\u003c/strong\\u003e\\u003c/p\\u003e\\u003cp\\u003eTo address potential multicollinearity among hematological and derived indices, LASSO regression was applied for feature selection and regularization (Figs.\\u0026nbsp;2A-C). This approach effectively reduced redundancy and identified neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-CRP ratio (LCR) as independent prognostic indicators (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eFeature Selection and Corresponding Coefficients Derived from LASSO-Cox Regression\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFeature\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCoef(min)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCoef(1se)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emGPS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.237\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.319\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.152\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSII\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.116\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eALT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2.362E-04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGGT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.724E-04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCREA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-4.103E-04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGLU\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-1.238E-03\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePT\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-9.92E-05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLCR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.911\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.509\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eIn this study, univariate and multivariable Cox regression analyses were performed to identify key prognostic factors for patients with gallbladder cancer. Univariate analysis revealed that age, diabetes, tumor size, CEA, and CA19-9 (log1p) were significantly associated with overall survival (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eResults of Univariate and Multivariable Cox Regression Analyses\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCharacteristic\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eN\\u0026thinsp;=\\u0026thinsp;947\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c4\\\" namest=\\\"c3\\\"\\u003e \\u003cp\\u003eUnivariate Cox Analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eMultivariate Cox Analysis\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eHazard Ratio (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eHazard Ratio (95%CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eP-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAge\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.015 (1.007\\u0026ndash;1.024)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.015 (1.005\\u0026ndash;1.024)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.004\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHypertension\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.096 (0.920\\u0026ndash;1.306)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.305\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eDiabetes\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.314 (1.030\\u0026ndash;1.675)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.028\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.251 (0.932\\u0026ndash;1.680)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.136\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePolyp\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.508 (0.314\\u0026ndash;0.823)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.723 (0.401\\u0026ndash;1.304)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.282\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGallstone\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.119 (0.949\\u0026ndash;1.320)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.182\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSurgery type\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.662 (1.283\\u0026ndash;2.153)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.159 (0.860\\u0026ndash;1.563)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.332\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBile duct resection\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.479 (1.247\\u0026ndash;1.755)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.223 (0.880\\u0026ndash;1.622)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.207\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLiver resection\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNone\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLimited\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.384 (1.158\\u0026ndash;1.654)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.164 (0.882\\u0026ndash;1.564)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.131\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMajor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.539 (0.946\\u0026ndash;2.504)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.083\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.369 (0.792\\u0026ndash;2.368)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.261\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eR0\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eYes\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.086 (2.567\\u0026ndash;3.710)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.479 (1.984\\u0026ndash;3.098)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTNM\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.409 (0.415\\u0026ndash;4.785)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.583\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.288 (0.376\\u0026ndash;4.415)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.687\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eII\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.693 (0.815\\u0026ndash;8.899)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.104\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.243 (0.668\\u0026ndash;7.530)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.191\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIIIA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.829 (1.220\\u0026ndash;12.015)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.021\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e3.121 (0.977\\u0026ndash;9.969)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.055\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIIIB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.132 (1.957\\u0026ndash;19.218)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.604 (1.438\\u0026ndash;14.736)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIVA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.552 (1.991\\u0026ndash;21.563)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e4.284 (1.277\\u0026ndash;14.370)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.018\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIVB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.642 (3.680\\u0026ndash;36.829)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e8.009 (2.473\\u0026ndash;25.942)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGrade\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eWell\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eModerate\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.448 (1.065\\u0026ndash;1.969)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.018\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.857 (0.598\\u0026ndash;1.227)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.399\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePoor\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.988 (1.466\\u0026ndash;2.696)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.210 (0.846\\u0026ndash;1.729)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.297\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTumor size\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.006 (1.002\\u0026ndash;1.010)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.003 (0.998\\u0026ndash;1.007)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.261\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCEA (log1p)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.091 (1.022\\u0026ndash;1.165)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.009\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.010 (0.961\\u0026ndash;1.061)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.701\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCA19-9 (log1p)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.168 (1.121\\u0026ndash;1.218)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.112 (1.078\\u0026ndash;1.146)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCA125 (log1p)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.056 (0.997\\u0026ndash;1.117)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.062\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eAFP (log1p)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.013 (0.942\\u0026ndash;1.090)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.727\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eL\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.325 (0.273\\u0026ndash;0.388)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eCRP (log1p)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.361 (1.273\\u0026ndash;1.456)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eALB\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.998 (0.995\\u0026ndash;1.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.198\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eWBC\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.999 (0.995\\u0026ndash;1.002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.464\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNEU\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.001 (0.985\\u0026ndash;1.018)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.867\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eHb\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.997 (0.993\\u0026ndash;1.000)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePLT\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.001 (1.000\\u0026ndash;1.002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTB\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.001 (1.001\\u0026ndash;1.002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eBUN\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.998 (0.994\\u0026ndash;1.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.121\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eGLU\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.995 (0.987\\u0026ndash;1.002)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eINR\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.999 (0.998\\u0026ndash;1.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePT\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.999 (0.997\\u0026ndash;1.000)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.138\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFIB\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.998 (0.996\\u0026ndash;1.000)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.071\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003emGPS\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.630 (1.375\\u0026ndash;1.934)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.798 (1.396\\u0026ndash;2.317)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003ePNI\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.991 (0.981\\u0026ndash;1.001)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.064\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eSII (log1p)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.380 (1.243\\u0026ndash;1.532)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eNLR (log1p)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.138 (1.789\\u0026ndash;2.556)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.114 (1.068\\u0026ndash;1.162)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eLCR\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.606 (0.538\\u0026ndash;0.682)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.659 (0.580\\u0026ndash;0.749)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSubsequent multivariable Cox regression confirmed that age, R0 resection status, TNM stage, CA19-9 (log1p), NLR, and LCR were independent predictors of survival. Specifically, age (HR\\u0026thinsp;=\\u0026thinsp;1.015, P\\u0026thinsp;=\\u0026thinsp;0.004), non-R0 resection (HR\\u0026thinsp;=\\u0026thinsp;2.479, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), higher TNM stage (overall P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), CA19-9 (log1p) (HR\\u0026thinsp;=\\u0026thinsp;1.112, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), and NLR (HR\\u0026thinsp;=\\u0026thinsp;1.114, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) were associated with increased mortality risk, whereas LCR (HR\\u0026thinsp;=\\u0026thinsp;0.659, P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) was a protective factor.\\u003c/p\\u003e \\u003cp\\u003eThese findings indicate that, after adjusting for other variables, these factors independently predict survival outcomes in patients with gallbladder cancer.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDevelopment of Clinical, Biomarker, and Tumor Marker Scores\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo evaluate the prognosis of patients with gallbladder cancer, three risk scores were constructed based on the results of multivariable Cox regression: a clinicopathological score (C-score), a blood-based inflammatory score (B-score), and a tumor marker score (T-score). The C-score incorporates patients\\u0026rsquo; clinical characteristics, the B-score integrates hematological indicators, and the T-score reflects tumor marker levels. Each score was calculated as a weighted linear combination of its corresponding variables, with weights defined by the regression coefficients derived from the multivariable Cox model (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Kaplan\\u0026ndash;Meier analysis demonstrated that all three scores effectively stratified patients according to survival risk, indicating good prognostic discrimination (Figs.\\u0026nbsp;2D-L). Each score was calculated using the corresponding regression coefficients (β), as follows:\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eConstruction of the C-score, B-score, and T-score based on multivariable Cox regression\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eScore\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eVariable\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCoding / Unit\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eHR (95% CI)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eβ coefficient (ln(HR))\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eContribution to score\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eC-score\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eper 1-year increase\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.015 (1.005\\u0026ndash;1.024)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.02\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.015 \\u0026times; Age\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eR0 resection status\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNo\\u0026thinsp;=\\u0026thinsp;1, Yes\\u0026thinsp;=\\u0026thinsp;0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.479 (1.984\\u0026ndash;3.098)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.908 \\u0026times; R0 status\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTNM stage II\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eII vs Tis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.243 (0.668\\u0026ndash;7.530)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.808 \\u0026times; TNM II\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTNM stage IIIA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIIIA vs Tis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.121 (0.977\\u0026ndash;9.969)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.138 \\u0026times; TNM IIIA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTNM stage IIIB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIIIB vs Tis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.604 (1.438\\u0026ndash;14.736)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.527 \\u0026times; TNM IIIB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTNM stage IVA\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVA vs Tis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e4.284 (1.277\\u0026ndash;14.370)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.46\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e1.455 \\u0026times; TNM IVA\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTNM stage IVB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIVB vs Tis\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e8.009 (2.473\\u0026ndash;25.942)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e2.081 \\u0026times; TNM IVB\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eB-score\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNLR (log1p)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003econtinuous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.114 (1.068\\u0026ndash;1.162)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.108 \\u0026times; NLR\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLCR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003econtinuous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.659 (0.580\\u0026ndash;0.749)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e-0.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.417 \\u0026times; LCR\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eT-score\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCA19-9 (log1p)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003econtinuous\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1.112 (1.078\\u0026ndash;1.146)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.106 \\u0026times; CA19-9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eC-score\\u0026thinsp;=\\u0026thinsp;βage \\u0026times; Age\\u0026thinsp;+\\u0026thinsp;βR0 \\u0026times; R0 status\\u0026thinsp;+\\u0026thinsp;ΣβTNMi \\u0026times; TNMi\\u003c/p\\u003e \\u003cp\\u003eB-score\\u0026thinsp;=\\u0026thinsp;βNLR \\u0026times; NLR\\u0026thinsp;+\\u0026thinsp;βLCR \\u0026times; LCR\\u003c/p\\u003e \\u003cp\\u003eT-score\\u0026thinsp;=\\u0026thinsp;βCA19-9 \\u0026times; CA19-9 (log1p)\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eDynamic Prognostic Assessment Using Risk Scores\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTime-dependent AUC analysis showed that the prognostic performance of the three risk scores (C-score, B-score, and T-score) changed over time. In both the training and validation cohorts, the AUC values of all three scores gradually declined with increasing follow-up duration (Figs.\\u0026nbsp;3A-B).\\u003c/p\\u003e \\u003cp\\u003eTo further characterize their temporal prognostic value, patients were stratified into three follow-up windows (0\\u0026ndash;12 months, 12\\u0026ndash;36 months, and 36\\u0026ndash;48 months).In the early postoperative period (0\\u0026ndash;12 months), all three scores significantly stratified prognosis, with C-score and B-score showing stronger discrimination than T-score. In the 12\\u0026ndash;36 month interval, both C-score and T-score maintained significant discriminative ability, whereas B-score showed weaker and non-significant separation (P\\u0026thinsp;=\\u0026thinsp;0.43). In the 36\\u0026ndash;48 month interval, only T-score retained significant prognostic value (P\\u0026thinsp;=\\u0026thinsp;0.00025), while neither C-score nor B-score showed significant discrimination (P\\u0026thinsp;=\\u0026thinsp;0.6 and P\\u0026thinsp;=\\u0026thinsp;0.15, respectively) (Figs.\\u0026nbsp;3C-E).\\u003c/p\\u003e \\u003cp\\u003eTaken together, these findings suggest that C-score and B-score are more informative for early prognostic assessment, whereas T-score provides the most durable prognostic value over longer follow-up periods. These results support the use of a dynamic, time-window-based evaluation strategy for risk stratification and clinical outcome prediction in patients with gallbladder cancer.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eModel Selection and Performance Evaluation\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo identify the optimal predictive framework, eight candidate algorithms were evaluated using five-fold cross-validation at 12, 24, and 36 months (Figs.\\u0026nbsp;4A\\u0026ndash;B). Model performance was assessed using the area under the curve (AUC) and Brier score. Among the evaluated models, logistic regression demonstrated the most stable performance across time points, consistently achieving high AUC values and low Brier scores in both training and validation cohorts (Figures \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003eA\\u0026ndash;C). Given its robust predictive performance, interpretability, and suitability for clinical application, logistic regression was selected as the final integrated model.\\u003c/p\\u003e \\u003cp\\u003eFurther analysis of regression coefficient dynamics revealed temporal trends in key predictors, indicating heterogeneity in their contributions across different follow-up windows (Fig.\\u0026nbsp;4C). The time-dependent ROC analysis further demonstrated consistent predictive performance of the integrated model in both training and validation cohorts, with minimal differences between the two datasets (Fig.\\u0026nbsp;4D), supporting the robustness and generalizability of the model.\\u003c/p\\u003e \\u003cp\\u003eFinally, the performance of the integrated model was compared with the individual C-, B-, and T-scores (Figs.\\u0026nbsp;4E\\u0026ndash;F). The integrated model consistently outperformed the individual scores across all time points, particularly during the early postoperative period. These findings suggest that integrating multiple prognostic dimensions substantially improves the model\\u0026rsquo;s discriminative ability for survival prediction.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003ePerformance Evaluation of the Integrated Logistic Regression Model\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe integrated prognostic model, constructed using logistic regression, was comprehensively evaluated through ROC curves, calibration plots, and decision curve analysis (DCA). At 12-, 24-, and 36-month time points in both training and validation cohorts, ROC curves demonstrated robust predictive performance. The AUC values in the training cohort were 0.857 (95% CI: 0.832\\u0026ndash;0.882) at 12 months, 0.808 (95% CI: 0.781\\u0026ndash;0.835) at 24 months, and 0.804 (95% CI: 0.776\\u0026ndash;0.831) at 36 months. In the validation cohort, the corresponding AUCs were 0.857 (95% CI: 0.820\\u0026ndash;0.895), 0.799 (95% CI: 0.757\\u0026ndash;0.841), and 0.770 (95% CI: 0.724\\u0026ndash;0.816)(Figs.\\u0026nbsp;5A-B), indicating consistent predictive ability across time windows.\\u003c/p\\u003e \\u003cp\\u003eCalibration curve analysis revealed high concordance between predicted and observed survival probabilities at all three time points, confirming the model\\u0026rsquo;s reliability(Figs.\\u0026nbsp;5C-D). DCA further demonstrated that the model provided substantial clinical net benefit across different threshold probabilities, particularly at 12 and 24 months, highlighting its value for clinical decision-making(Figs.\\u0026nbsp;5E-F).\\u003c/p\\u003e \\u003cp\\u003eSensitivity analyses after SMOTE correction showed consistent model performance, supporting the robustness of the integrated model (Figures \\u003cspan refid=\\\"MOESM2\\\" class=\\\"InternalRef\\\"\\u003eS2\\u003c/span\\u003eA-D).\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eBiological Mechanisms and Clinical Translation of the C-, B-, and T-Score System\\u003c/b\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo further elucidate the biological mechanisms underlying the association of the C-, B-, and T-scores with gallbladder cancer (GBC), we performed Gene Set Enrichment Analysis (GSEA). As illustrated in Figs.\\u0026nbsp;6A\\u0026ndash;C, GSEA identified significant enrichment of specific signaling pathways associated with each score, revealing the distinct molecular landscapes of GBC. Specifically, the C-score was closely linked to pathways governing tumor invasiveness and matrix remodeling, such as ECM-receptor interaction, Hedgehog, and Wnt signaling (Fig.\\u0026nbsp;6A). The B-score exhibited a strong correlation with the immune microenvironment and inflammatory responses, showing enrichment in the JAK-STAT and NF-kappa B pathways, as well as the PD-1/PD-L1 checkpoint (Fig.\\u0026nbsp;6B). Meanwhile, the T-score was primarily associated with metabolic stress and cell cycle regulation, including the HIF-1 and p53 signaling pathways (Fig.\\u0026nbsp;6C). These enriched pathways provide functional insights into the prognostic value of the three scores and offer prioritized directions for future experimental validation.\\u003c/p\\u003e \\u003cp\\u003eTo facilitate clinical translation, we developed a web-based dynamic prognostic tool(Fig.\\u0026nbsp;6D). This platform integrates multiple variables, including the C-, B-, and T-scores, to predict individualized survival probabilities across various time horizons (ranging from 6 to 36 months). By inputting patient-specific clinical data, clinicians can generate real-time individualized survival curves and visualize the corresponding prognostic scores. This user-friendly tool provides a precise risk assessment platform, empowering clinicians to make data-driven, individualized therapeutic decisions and providing a more intuitive approach for personalized risk management in GBC patients.\\u003c/p\\u003e \\u003cp\\u003eThe web-based tool is accessible at: \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://wmy123456.shinyapps.io/gbc_iscore_app/\\u003c/span\\u003e\\u003cspan address=\\\"https://wmy123456.shinyapps.io/gbc_iscore_app/\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe source code for the web-based application is available upon reasonable request.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003en this large-scale multicenter study involving 1,354 patients with gallbladder cancer from 44 medical centers, we developed and validated a multidimensional prognostic framework that substantially improves risk stratification beyond conventional staging systems. By integrating clinicopathological characteristics, systemic inflammatory markers, and tumor biomarkers, the proposed model not only achieved superior predictive performance compared with individual scoring systems but also provided biologically interpretable insights into tumor\\u0026ndash;host interactions. More importantly, this framework addresses a critical clinical gap by enabling more precise identification of high-risk patients, thereby offering a potential tool to guide individualized postoperative management.\\u003c/p\\u003e \\u003cp\\u003eGallbladder cancer (GBC) remains a challenging malignancy due to its aggressive behavior and poor prognosis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan additionalcitationids=\\\"CR33\\\" citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e. Although the current TNM staging system maintains a central role in prognostic assessment, its inherent anatomical limitations have become increasingly pronounced among highly heterogeneous early-stage patients, failing to fully capture the complex interplay between tumor progression and host anti-tumor immunity\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR36 CR37\\\" citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e. In alignment with the burgeoning field of Systems Oncology, this study constructed a composite predictive model integrating clinical characteristics (C-score), the systemic inflammatory landscape (B-score), and tumor biomarkers (T-score). This model complements conventional anatomical staging by incorporating multidimensional biological information for refined risk stratification.\\u003c/p\\u003e \\u003cp\\u003eEach component of the model is supported by plausible pathophysiological rationale. The B-score captures the systemic immune\\u0026ndash;inflammatory status, with NLR and LCR reflecting the balance between pro-tumor inflammation and anti-tumor immunity\\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e. This aligns with growing evidence that tumor progression is closely linked to systemic immune dysregulation \\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e. The T-score, driven by CA19-9 levels, reflects not only tumor burden but also occult biological aggressiveness, such as early micrometastasis and vascular invasion\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e. Meanwhile, the C-score incorporates established clinical determinants, including R0 resection status and TNM stage, providing an anatomical and surgical baseline.Together, these components construct a multidimensional representation of tumor\\u0026ndash;host interactions, which may explain the superior predictive performance of the integrated model.\\u003c/p\\u003e \\u003cp\\u003eA pivotal highlight of this study is the deep correlation established between clinical phenotypes and molecular mechanisms via GSEA analysis. The findings reveal that the C-score is significantly associated with ECM-receptor interaction and Hedgehog pathways, precisely echoing the pathological process of tumor cell expansion through regulated matrix remodeling\\u003csup\\u003e[\\u003cspan additionalcitationids=\\\"CR44\\\" citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]\\u003c/sup\\u003e. The strong correlation between the B-score and the JAK-STAT, NF-κB, and immune checkpoint pathways confirms at the molecular level that peripheral blood inflammatory markers can effectively map the local immunosuppressive microenvironment (characterizing the transition from \\\"cold\\\" to \\\"hot\\\" tumors)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]\\u003c/sup\\u003e. Furthermore, the T-score, which was primarily driven by CA19-9, was associated with HIF-1 and p53 signaling pathways, reflecting the survival adaptation strategies of tumor cells under hypoxia and high metabolic stress\\u003csup\\u003e[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]\\u003c/sup\\u003e. These findings provide biological support for the prognostic relevance of the model and enhance its interpretability in the context of precision medicine.\\u003c/p\\u003e \\u003cp\\u003eRegarding the robustness of the research design, this study relies on a multicentercohort of 1,354 patients from 44 medical centers nationwide. Given the rarity of gallbladder cancer, this large multicenter cohort enhances the credibility and generalizability of the findings. Although the model has yet to fully integrate dynamic response data from neoadjuvant/adjuvant immune checkpoint inhibitors (ICI), deep learning-based radiomics features, or genomic mutation profiles (such as ERBB2/TP53 status\\u003csup\\u003e[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]\\u003c/sup\\u003e), this multidimensional framework may provide a basis for future expansion toward more comprehensive prognostic models.\\u003c/p\\u003e \\u003cp\\u003eTo bridge the gap between basic algorithms and clinical application, we further developed a web-based dynamic prognostic prediction platform designed to transform complex mathematical logic into intuitive, individualized survival curves. In an era where clinical decision-making increasingly relies on evidence and data, this platform achieves real-time, visualized prognostic assessment. It may assist clinicians in refining adjuvant treatment strategies at different follow-up intervals and establishes an open iterative framework for integrating more complex biomarkers in the future. In summary, the comprehensive assessment system constructed in this study deepens the understanding of the nature of GBC prognosis and serves as a powerful implementation of the \\\"patient-centered\\\" precision medicine philosophy, opening new pathways for the individualized management of gallbladder cancer.\\u003c/p\\u003e \\u003cp\\u003eImportantly, this model is not merely predictive but has the potential to inform clinical decision-making, particularly in identifying patients who may benefit from intensified adjuvant therapy and tailored follow-up strategies.\\u003c/p\\u003e \\u003cp\\u003eThis study has several limitations. First, the retrospective design may have introduced potential selection bias. Second, although the cohort was derived from 44 centers, external validation in independent datasets is still warranted. Third, potential inter-center heterogeneity in surgical management and laboratory testing may have influenced the results. Finally, the biological insights from GSEA remain exploratory and require further experimental validation.The transcriptomic analysis was exploratory and requires further experimental validation.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, we developed and validated a multidimensional prognostic system for gallbladder cancer integrating clinicopathological features, systemic inflammatory markers, and tumor biomarkers. The integrated model demonstrated superior predictive performance and effectively captured the dynamic heterogeneity of postoperative survival. The associated molecular signatures identified by GSEA further supported the biological relevance of the C-, B-, and T-score framework. Implemented as a web-based tool, this system provides a practical platform for individualized risk stratification and prognosis prediction in patients with gallbladder cancer.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGBC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003egallbladder cancer\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCRGGC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eChinese Research Group of Gallbladder Cancer\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eNLR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eneutrophil-to-lymphocyte ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eLCR\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003elymphocyte-to-C-reactive protein ratio\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eROC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ereceiver operating characteristic\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAUC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003earea under the curve\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eDCA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003edecision curve analysis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eGSEA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eGene Set Enrichment Analysis\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eOS\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eoverall survival\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eSMOTE\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eSynthetic Minority Oversampling Technique\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAJCC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eAmerican Joint Committee on Cancer\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCRP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eC-reactive protein\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eCEA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ecarcinoembryonic antigen\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eAFP\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ealpha-fetoprotein.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis multicenter retrospective study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine (Approval No. XHEC-C-2025-113-2). The requirement for informed consent was waived by the same Ethics Committee due to the retrospective nature of the study.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eNot applicable.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe data used in this study were obtained from the multicenter gallbladder cancer cohort database of the Chinese Research Group of Gallbladder Cancer (CRGGC). The data are not publicly available due to privacy and data-sharing restrictions, but may be available from the corresponding author upon reasonable request and with permission from the CRGGC. The source code for the web-based application is available from the corresponding author upon reasonable request.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no competing interests.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis work was supported by the National Natural Science Foundation of China (Grant Nos. 82272691 and 82373370), the Shanghai Municipal Health Commission Leading Talent Program (Grant No. 2022XD010), the Shanghai \\u0026ldquo;Yiyuan Xinxing\\u0026rdquo; Young Medical Talents Training Funding Program (2024), and the Shanghai Municipal Health Commission Health Industry Research Special Program (Grant No. 20254Z0005).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026rsquo;\\u003c/strong\\u003e\\u003cstrong\\u003e contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eMingyang Wang: Conceptualization, Data curation, Formal analysis, Methodology, Visualization, Writing \\u0026ndash; original draft. Runfa Bao: Data curation, Formal analysis, Validation, Writing \\u0026ndash; review \\u0026amp; editing. Ziyi Yang: Data curation, Investigation, Validation, Writing \\u0026ndash; review \\u0026amp; editing. Zhengyu Chen, Lei Kong and Shengxin Gu: Data curation, Investigation. Wei Gong, Jun Gu, Xiangsong Wu and Yidi Zhu: Conceptualization, Supervision, Funding acquisition, Writing \\u0026ndash; review \\u0026amp; editing. All authors read and approved the final manuscript.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors thank all participating centers of the Chinese Research Group of Gallbladder Cancer (CRGGC) for their support in data collection and management.\\u003c/p\\u003e\\n\\n\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eROA JC, GARC\\u0026iacute;A P. Gallbladder cancer [J]. 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J Immunother Cancer. 2018;6(1):74.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOZER M, GOKSU S Y, SANFORD N N, et al. A Propensity Score Analysis of Chemotherapy Use in Patients With Resectable Gallbladder Cancer [J]. JAMA Netw Open. 2022;5(2):e2146912.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLIU F, LI Y, YING D, et al. Whole-exome mutational landscape of neuroendocrine carcinomas of the gallbladder [J]. Signal Transduct Target Ther. 2021;6(1):55.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCONCI S, CATALANO G, ROMAN D, et al. Current Role and Future Perspectives of Immunotherapy and Circulating Factors in Treatment of Biliary Tract Cancers [J]. Int J Med Sci. 2023;20(7):858\\u0026ndash;69.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBANH S, FEHERVARI M, FLOD S, et al. Single stage management of suspected gallbladder cancer guided by intraoperative frozen section analysis: a retrospective cohort study [J]. Int J Surg. 2024;110(10):6314\\u0026ndash;20.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAMIN M B, GREENE F L, EDGE S B, et al. The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more personalized approach to cancer staging [J]. CA Cancer J Clin. 2017;67(2):93\\u0026ndash;9.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eXING M, LIN S, MATHUR A, et al. Genetic modification of the AJCC classification of papillary thyroid cancer: an international, multicentre, retrospective cohort study [J]. Lancet Oncol. 2025;26(10):1382\\u0026ndash;92.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLIU M, ZHANG P, WANG S, et al. Comparation between novel online models and the AJCC 8th TNM staging system in predicting cancer-specific and overall survival of small cell lung cancer [J]. Front Endocrinol (Lausanne). 2023;14:1132915.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBHUTIANI N, HU C Y, PALIS B et al. Lack of Hierarchical Survival Prognosis in AJCC Staging for Colon and Rectal Cancer-Implications for Future Summary Stage Classification [J]. Clin Colorectal Cancer, 2025, 24(2): 159\\u0026thinsp;\\u0026ndash;\\u0026thinsp;65.e2.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLI X, XU J, ZHU L, et al. A novel nomogram with preferable capability in predicting the overall survival of patients after radical esophageal cancer resection based on accessible clinical indicators: A comparison with AJCC staging [J]. Cancer Med. 2021;10(13):4228\\u0026ndash;39.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePEI B, ZHANG J. Neutrophil-to-lymphocyte ratio as a predictive biomarker for hyperprogressive disease mediated by immune checkpoint inhibitors: a systematic review and meta-analysis [J]. Front Immunol. 2024;15:1393925.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eOKUGAWA Y, TOIYAMA Y, YAMAMOTO A, et al. Lymphocyte-to-C-reactive protein ratio and score are clinically feasible nutrition-inflammation markers of outcome in patients with gastric cancer [J]. Clin Nutr. 2020;39(4):1209\\u0026ndash;17.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZHAO Y, YANG M, FENG J, et al. Advances in immunotherapy for biliary tract cancers [J]. Chin Med J (Engl). 2024;137(5):524\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWANG X Y, ZHU W W, LU L, et al. Development and validation of a mutation-annotated prognostic score for intrahepatic cholangiocarcinoma after resection: a retrospective cohort study [J]. Int J Surg. 2023;109(11):3506\\u0026ndash;18.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLAMARCA A, BARRIUSO J, MCNAMARA M G, et al. Molecular targeted therapies: Ready for prime time in biliary tract cancer [J]. J Hepatol. 2020;73(1):170\\u0026ndash;85.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCHUNG T, OH S, WON J, et al. Genomic and transcriptomic signatures of sequential carcinogenesis from papillary neoplasm to biliary tract cancer [J]. J Hepatol. 2025;83(1):119\\u0026ndash;30.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRIMLAND C A, TILSON S G, MORELL C M, et al. Regional Differences in Human Biliary Tissues and Corresponding In Vitro-Derived Organoids [J]. Hepatology. 2021;73(1):247\\u0026ndash;67.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLIU R, SONG Y, HUA R, et al. Cell-Free DNA in Plasma Reveals Genomic Similarity Between Biliary Tract Inflammatory Lesion and Biliary Tract Cancer [J]. Phenomics. 2024;4(4):339\\u0026ndash;51.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLI S, JUENGPANICH S, TOPATANA W, et al. Adavosertib-encapsulated metal-organic frameworks for p53-mutated gallbladder cancer treatment via synthetic lethality [J]. Sci Bull (Beijing). 2024;69(9):1286\\u0026ndash;301.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZHANG Y, ZUO C, LIU L, et al. Single-cell RNA-sequencing atlas reveals an MDK-dependent immunosuppressive environment in ErbB pathway-mutated gallbladder cancer [J]. J Hepatol. 2021;75(5):1128\\u0026ndash;41.\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"},{\"header\":\"Table 2\",\"content\":\"\\u003cp\\u003eTable 2 is available in the Supplementary Files section.\\u003c/p\\u003e\\n\"}],\"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\":\"info@researchsquare.com\",\"identity\":\"bmc-medical-informatics-and-decision-making\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"midm\",\"sideBox\":\"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/midm/default.aspx\",\"title\":\"BMC Medical Informatics and Decision Making\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Gallbladder cancer, Prognostic model, Systemic inflammatory markers, Tumor biomarkers, Machine learning, Multicenter study\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9222230/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9222230/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eThe anatomical TNM staging system inadequately reflects the profound survival heterogeneity in gallbladder cancer (GBC), thereby limiting accurate risk stratification and contributing to suboptimal clinical decision-making. To overcome this limitation, we developed a multidimensional and dynamic prognostic framework integrating clinicopathological features, systemic inflammatory markers, and tumor biomarkers, leveraging large-scale multicenter real-world data.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eA total of 1,354 patients with GBC from 44 medical centers were retrospectively analyzed and randomly assigned to training (n\\u0026thinsp;=\\u0026thinsp;947) and validation (n\\u0026thinsp;=\\u0026thinsp;407) cohorts. Independent prognostic factors were identified using LASSO and multivariable Cox regression to construct three risk scores: clinicopathological (C-score), blood-based inflammatory (B-score), and tumor marker (T-score). An integrated prognostic model was subsequently developed and evaluated through machine-learning\\u0026ndash;based benchmarking. Model performance was assessed using time-dependent ROC analysis (6\\u0026ndash;48 months), calibration curves, and decision curve analysis (DCA). Gene Set Enrichment Analysis (GSEA) was performed to explore the biological relevance of the scoring systems.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eAge, R0 resection status, TNM stage, CA19-9 (log1p), neutrophil-to-lymphocyte ratio (NLR), and lymphocyte-to-CRP ratio (LCR) were identified as independent prognostic factors. The integrated model consistently outperformed individual C-, B-, and T-scores across all follow-up intervals, demonstrating strong discriminative ability with a 12-month AUC of 0.857 in both cohorts. Calibration and decision curve analyses confirmed good model reliability and clinical utility. GSEA revealed distinct molecular associations underlying the three scores, including ECM\\u0026ndash;receptor interaction, immune-inflammatory signaling (JAK\\u0026ndash;STAT/NF-κB), and metabolic stress pathways (HIF-1/p53). A web-based dynamic prediction platform was further developed to enable individualized survival estimation.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eThis multidimensional framework provides a biologically interpretable and dynamically adaptable tool for prognostic stratification in gallbladder cancer. Implemented through a web-based platform, the model facilitates individualized risk assessment and supports data-driven clinical decision-making.\\u003c/p\\u003e\",\"manuscriptTitle\":\"A Multidimensional Prognostic Model for Gallbladder Cancer Based on a Multicenter Cohort Integrating Clinicopathological Features, Systemic Inflammation, and Tumor Biomarkers\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-14 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